Computer and Computing Technologies in Agriculture IV, Part II - CCTA 2010

IFIP Advances in Information and Communication Technology 345 Editor-in-Chief A. Joe Turner, Seneca, SC, USA Editori...

Author: Daoliang Li | Yande Liu | Yingyi Chen

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IFIP Advances in Information and Communication Technology

345

Editor-in-Chief A. Joe Turner, Seneca, SC, USA

Editorial Board Foundations of Computer Science Mike Hinchey, Lero, Limerick, Ireland Software: Theory and Practice Bertrand Meyer, ETH Zurich, Switzerland Education Arthur Tatnall, Victoria University, Melbourne, Australia Information Technology Applications Ronald Waxman, EDA Standards Consulting, Beachwood, OH, USA Communication Systems Guy Leduc, Université de Liège, Belgium System Modeling and Optimization Jacques Henry, Université de Bordeaux, France Information Systems Jan Pries-Heje, Roskilde University, Denmark Relationship between Computers and Society Jackie Phahlamohlaka, CSIR, Pretoria, South Africa Computer Systems Technology Paolo Prinetto, Politecnico di Torino, Italy Security and Privacy Protection in Information Processing Systems Kai Rannenberg, Goethe University Frankfurt, Germany Artificial Intelligence Tharam Dillon, Curtin University, Bentley, Australia Human-Computer Interaction Annelise Mark Pejtersen, Center of Cognitive Systems Engineering, Denmark Entertainment Computing Ryohei Nakatsu, National University of Singapore

IFIP – The International Federation for Information Processing IFIP was founded in 1960 under the auspices of UNESCO, following the First World Computer Congress held in Paris the previous year. An umbrella organization for societies working in information processing, IFIP’s aim is two-fold: to support information processing within its member countries and to encourage technology transfer to developing nations. As its mission statement clearly states, IFIP’s mission is to be the leading, truly international, apolitical organization which encourages and assists in the development, exploitation and application of information technology for the benefit of all people. IFIP is a non-profitmaking organization, run almost solely by 2500 volunteers. It operates through a number of technical committees, which organize events and publications. IFIP’s events range from an international congress to local seminars, but the most important are: • The IFIP World Computer Congress, held every second year; • Open conferences; • Working conferences. The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high. As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed. The working conferences are structured differently. They are usually run by a working group and attendance is small and by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is less rigorous and papers are subjected to extensive group discussion. Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers. Any national society whose primary activity is in information may apply to become a full member of IFIP, although full membership is restricted to one society per country. Full members are entitled to vote at the annual General Assembly, National societies preferring a less committed involvement may apply for associate or corresponding membership. Associate members enjoy the same benefits as full members, but without voting rights. Corresponding members are not represented in IFIP bodies. Affiliated membership is open to non-national societies, and individual and honorary membership schemes are also offered.

Daoliang Li Yande Liu Yingyi Chen (Eds.)

Computer and Computing Technologies in Agriculture IV 4th IFIP TC 12 Conference, CCTA 2010 Nanchang, China, October 22-25, 2010 Selected Papers, Part II

13

Volume Editors Daoliang Li Yingyi Chen China Agricultural University EU-China Center for Information & Communication Technologies (CICTA) 17 Tsinghua East Road, Beijing, 100083, P.R. China E-mail: {dliangl, chenyingyi}@cau.edu.cn Yande Liu East China Jiaotong University College of Mechanical and Electronic Engineering Shuanggang Road, Nanchang, 330013 Jiangxi, China E-mail: [email protected]

ISSN 1868-4238 e-ISSN 1868-422X e-ISBN 978-3-642-18336-2 ISBN 978-3-642-18335-5 DOI 10.1007/978-3-642-18336-2 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010942867 CR Subject Classification (1998): I.2.11, H.4, C.3, C.2, D.2, K.4.4

© IFIP International Federation for Information Processing 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

I want to express my sincere thanks to all authors who submitted research papers to the 4th IFIP International Conference on Computer and Computing Technologies in Agriculture and the 4th Symposium on Development of Rural Information (CCTA 2010) that were held in Nanchang, China, 22–25 October 2010. This conference was hosted by CICTA (EU-China Centre for Information & Communication Technologies, China Agricultural University); China Agricultural University; China Society of Agricultural Engineering, China; International Federation for Information Processing (TC12); Beijing Society for Information Technology in Agriculture, China. It was organized by East China Jiaotong University. CICTA focuses on research and development of advanced and practical technologies applied in agriculture and aims at promoting international communication and cooperation. Sustainable agriculture is currently the focus of the whole world, and the application of information technology in agriculture has become more and more important. ‘Informatized agriculture’ has been the goal of many countries recently in order to scientiﬁcally manage agriculture to achieve low costs and high income. The topics of CCTA 2010 covered a wide range of interesting theories and applications of information technology in agriculture, including simulation models and decision-support systems for agricultural production, agricultural product quality testing, traceability and e-commerce technology, the application of information and communication technology in agriculture, and universal information service technology and service systems development in rural areas. We selected 352 best papers among those submitted to CCTA 2010 for these proceedings. It is always exciting to have experts, professionals and scholars getting together with creative contributions and sharing inspiring ideas which will hopefully lead to great developments in these technologies. Finally, I would like also to express my sincere thanks to all the authors, speakers, session chairs and attendees for their active participation and support of this conference.

October 2010

Daoliang Li

Conference Organization

Organizer East China Jiaotong University

Organizing Committee Chair Yande Liu

Academic Committee Chair Daoliang Li

Conference Secretariat Lingling Gao

Sponsors China Agricultural University China Society of Agricultural Engineering, China International Federation for Information Processing, Austria Beijing Society for Information Technology in Agriculture, China National Natural Science Foundation of China

Table of Contents – Part II

Food Safety and Technological Implications of Food Traceability Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hailiang Zhang, Xudong Sun, and Yande Liu

1

Function Design of Township Enterprise Online Approval System . . . . . . Peng Lu, Gang Lu, and Chao Ding

11

Application of GPS on Power System Operation . . . . . . . . . . . . . . . . . . . . . Chunmei Pei, Huiling Guo, Xiuqing Yang, Bin He, Wei Liu, and Xuemei Li

18

Greenhouse Temperature Monitoring System Based on Labview . . . . . . . . Zhihong Zheng, Kai Zhang, and Chengliang Liu

23

Image-Driven Panel Design via Feature-Preserving Mesh Deformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baojun Li, Xiuping Liu, Yanqi Liu, Ping Hu, Mingzeng Liu, and Changsheng Wang

30

Inﬂuences of Temperature of Vapour-Condenser and Pressure in the Vacuum Chamber on the Cooling Rate during Vacuum Cooling . . . . . . . . Tingxiang Jin, Gailian Li, and Chunxia Hu

41

Inspection of Lettuce Water Stress Based on Multi-sensor Information Fusion Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongyan Gao, Hanping Mao, and Xiaodong Zhang

53

Measurement of Chili Pepper Plants Size Based on Mathematical Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yun Gao, Xiaoyu Li, Kun Qi, and Hong Chen

61

Methodology Comparison for Eﬀective LAI Retrieving Based on Digital Hemispherical Photograph in Rice Canopy . . . . . . . . . . . . . . . . . . . . . . . . . . Lianqing Zhou, Guiying Pan, and Zhou Shi

71

Molecular Methods of Studying Microbial Diversity in Soil Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liu Zhao, Zhihong Ma, Yunxia Luan, Anxiang Lu, Jihua Wang, and Ligang Pan Monitoring the Plant Density of Cotton with Remotely Sensed Data . . . Junhua Bai, Jing Li, and Shaokun Li

83

90

VIII

Table of Contents – Part II

Motion Blurring Direction Identiﬁcation Based on Second-Order Diﬀerence Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Junxiong Zhang, Fen He, and Wei Li

102

Multi-agent Quality of Bee Products Traceability Model Based on Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yue E, YePing Zhu, and YongSheng Cao

110

NIR Spectroscopy Identiﬁcation of Persimmon Varieties Based on PCA-SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shujuan Zhang, Dengfei Jie, and Haihong Zhang

118

One Method for Batch DHI Data Import into SQL-Server: A Batch Data Import Technique for DateSet Based on .NET . . . . . . . . . . . . . . . . . . Liang Shi and Wenxing Bao

124

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Fu, Jianhua Yang, and Tingting Zuo

131

Overall Layout Design of Iron and Steel Plants Based on SLP Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ermin Zhou, Kelou Chen, and Yanrong Zhang

139

Performance Forecasting of Piston Element in Motorcycle Engine Based on BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rong Dai

148

Performance Monitoring System for Precision Planter Based on MSP430-CT171 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianming Xia, Xiangyou Wang, Duanyang Geng, and Qingfeng Zhang

158

Pervasive Agricultural Environment Monitoring System Based on Embedded Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hu Zhao, Sangen Wang, and Dake Wu

166

Precipitation Resource Potential in Mountainous Areas in Hebei Province Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zheng Liu, Yanxia Zheng, and Zhiyong Zhao

177

Precision Drip Irrigation on Hot Pepper in Arid Northwest China Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huiying Yang, Haijun Liu, Yan Li, Guanhua Huang, and Fengxin Wang Study on Thermal Conductivities Prediction for Apple Fruit Juice by Using Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Zhang, Zhenhua Che, Jiahua Lu, Huizhong Zhao, Jianhua Chen, Zhiyou Zhong, and Le Yang

185

198

Table of Contents – Part II

IX

Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, λ, ρ Model) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Di-yi Chen, Yu-xiao Liu, Xiao-yi Ma, and Yan Long

205

Prediction of Irrigation Security of Reclaimed Water Storage in Winter Based on ANN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinfeng Deng

211

Progress of China Agricultural Information Technology Research and Applications Based on Registered Agricultural Software Packages . . . . . . Kaimeng Sun

218

Quantiﬁcation Research on Diﬀerent Load Weight-Bearing Running Biochemical Indexes of Rats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaping Shang

227

Rapid Determination of Ascorbic Acid in Fresh Vegetables and Fruits with Electrochemically Treated Screen-Printed Carbon Electrodes . . . . . . Ling Xiang, Hua Ping, Liu Zhao, Zhihong Ma, and Ligang Pan

234

Regional Drought Monitoring and Analyzing Using MODIS Data—A Case Study in Yunnan Province . . . . . . . . . . . . . . . . . . . . . . . . . . . Guoyin Cai, Mingyi Du, and Yang Liu

243

Regression Analysis and Indoor Air Temperature Model of Greenhouse in Northern Dry and Cold Regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ting Zhao and Heru Xue

252

Remote Control System Based on Compressed Image . . . . . . . . . . . . . . . . . Weichuan Liao Analysis of the Poverty-Stricken Rural Areas’ Demand for Rapid Dissemination of Agricultural Information—Taking Wanquan County in Hebei Province as an Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Shi and Yongchang Wu

259

264

Research and Analysis about System of Digital Agriculture Based on a Network Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duan Yane

274

Research and Development of Preceding-Evaluation System of Rural Drinking Water Safety Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lian He and Jilin Cheng

283

Research of Evaluation on Cultivated Land Fertility in Xinjiang Desert Oasis Based on GIS Technology—Taking No. 22 State Farm as the Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ling Wang, Xin Lv, and Hailong Liu

290

X

Table of Contents – Part II

Research of Pest Diagnosis System Development Tools Based on Binary Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yun Qiu and Guomin Zhou

300

Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Caojun Huang, Lin Li, Souhua Ren, and Zhisheng Zhou

309

Research of the Measurement on Palmitic Acid in Edible Oils by Near-Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui Li, Jingzhu Wu, and Cuiling Liu

317

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ran Cao, Yushu Yang, and Wei Guo

322

Research on Docking of Supply and Demand of Rural Informationization and “Internet Digital Divide” in Urban and Rural Areas in China . . . . . . Zhongwei Sun, Yang Wang, and Peng Lu

329

Research on Evaluation of Rural Highway Construction in Hebei Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guisheng Rao, Limeng Qi, Runqing Zhang, and Li Deng

339

Research on Farmland Information Collecting and Processing Technology Based on DGPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weidong Zhuang and Chun Wang

345

Research on Fertilizer Eﬃciency of Continuous Cropping Greenhouse Cucumber Based on DEA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaohui Yang, Yuxiang Huang, Shuqin Li, and Sheng Huang

351

Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale . . . . . . . . . . . . . . . . . Hao Zhang, Li Zhang, Yanna Ren, Juan Zhang, Xin Xu, Xinming Ma, and Zhongmin Lu

357

Research on Inﬂuenced Factors about Routing Selection Scheme in Agricultural Machinery Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fan Zhang, Guifa Teng, Jie Yao, and Sufen Dong

365

Research on Informationization Talented Person Training Pattern of the Countryside Area in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yang Wang and Zhongwei Sun

374

Research on Quality Index System of Digital Aerial Photography Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wencong Jiang, Yanling Li, Yong Liang, and Yanwei Zeng

381

Table of Contents – Part II

XI

Research on Quality Inspection Method of Digital Aerial Photography Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaojun Wang, Yanling Li, Yong Liang, and Yanwei Zeng

392

On RFID Application in the Tracking and Tracing System of Agricultural Product Logistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weihua Gan, Yuwei Zhu, and Tingting Zhang

400

Research on Rough Set and Decision Tree Method Application in Evaluation of Soil Fertility Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guifen Chen and Li Ma

408

Research on the Method of Geospatial Information Intelligent Search Based on Search Intention Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jingbo Liu, Jian Wang, and Bingbo Gao

415

Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context . . . . . . . . . . . . . . . . . . . . Wang Kai, Li Shao-Wen, Zhang You-Hua, and Liu Chao

425

Research on Traceability System of Food Safety Based on PDF417 Two-Dimensional Bar Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shipu Xu, Muhua Liu, Jingyin Zhao, Tao Yuan, and Yunsheng Wang

434

Research and Application of Cultivation-Simulation- Optimization Decision Making System for Rapeseed (Brassica napus L.) . . . . . . . . . . . . Hongxin Cao, Chunlei Zhang, Baojun Zhang, Suolao Zhao, Daokuo Ge, Baoqing Wang, Chuanbao Zhu, David B. Hannaway, Dawei Zhu, Juanuan Zhu, Jinying Sun, Yan Liu, Yongxia Liu, and Xiufang Wei Residue Dynamics of Phoxim in Pericarp, Sarcocarp and Kernel of Apple . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yunxia Luan, Hua Ping, and Ligang Pan Risk Analysis of Aedes triseriatus in China . . . . . . . . . . . . . . . . . . . . . . . . . . Jingyuan Liu, Xiaoguang Ma, Zhihong Li, Xiaoying Wu, and Nan Sun Risk Assessment of Reclaimed Water Utilization in Basin Based on GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yanxia Zheng, Shaoyuan Feng, Na Jiang, and Qingyi Meng

441

457 465

473

Root Architecture Modeling and Visualization in Wheat . . . . . . . . . . . . . . Liang Tang, Feng Tan, Haiyan Jiang, Xiaojun Lei, Weixing Cao, and Yan Zhu

479

Sensors in Smart Phone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunmei Pei, Huiling Guo, Xiuqing Yang, Yangqiu Wang, Xiaojing Zhang, and Hairong Ye

491

XII

Table of Contents – Part II

Simulation Analyze the Dice and Shape of the Dicer Based on ADAMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yingsa Huang, Jianping Hu, Deyong Yang, Xiuping Shao, and Fa Liu

496

Simulation and Design of Mixing Mechanism in Fertilizer Automated Proportioning Equipment Based on Pro/E and CFD . . . . . . . . . . . . . . . . . Liming Chen and Liming Xu

505

Simulation Study of a Novel Algorithm for Digital Relaying Based on FPGA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Renwang He, Dandan Xie, Yuling Zhao, and Yibo Yang

517

Simulation Study of Single Line-to-Ground Faults on Rural Teed Distribution Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wanying Qiu

521

Single Leaf Area Measurement Using Digital Camera Image . . . . . . . . . . . Baisong Chen, Zhuo Fu, Yuchun Pan, Jihua Wang, and Zhixuan Zeng

525

Sliding Monitoring System for Ground Wheel Based on ATMEGA16 for No-Tillage Planter—CT246 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lianming Xia, Xiangyou Wang, Duayang Geng, and Qingfeng Zhang

531

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China . . . . . . . . . . . . . . . . . . . . . . Zhongdong Yin, Changqing Zuo, and Liang Ma

538

Spatial and Temporal Variability of Annual Precipitation during 1958–2007 in Loess Plateau, China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rui Guo, Fengmin Li, Wenying He, Sen Yang, and Guojun Sun

551

Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS . . . Lin Li, Yong Yang, Hongbin Wang, Jing Dong, Yujun Zhao, and Jianbin He Study for Organic Soybean Production Information Traceability System Based on Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xi Wang, Chun Wang, Xinzhong Wang, and Weidong Zhuang Study of Agricultural Informatization Standards Framework . . . . . . . . . . . Yunpeng Cui, Shihong Liu, and Pengju He

561

567 573

On Countermeasures of Promoting Agricultural Products’ E–Commerce in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weihua Gan, Tingting Zhang, and Yuwei Zhu

579

Study on Approaches of Land Suitability Evaluation for Crop Production Using GIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Linyi Li, Jingyin Zhao, and Tao Yuan

587

Table of Contents – Part II

Tracking of Human Arm Based on MEMS Sensors . . . . . . . . . . . . . . . . . . . Yuxiang Zhang, Liuyi Ma, Tongda Zhang, and Fuhou Xu Study on Integration of Measurement and Control System for Combine Harvester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jin Chen, Yuelan Zheng, Yaoming Li, and Xinhua Wei Study on Jabber Be Applied to Video Diagnosis for Plant Diseases and Insect Pests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wei Zhang, JunFeng Zhang, Feng Yu, JiChun Zhao, and RuPeng Luan Study on Pretreatment Algorithm of Near Infrared Spectroscopy . . . . . . . Xiaoli Wang and Guomin Zhou Study on Rapid Identiﬁcation Methods of Transgenic Rapeseed Oil Based on Near Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shiping Zhu, Jing Liang, and Lin Yan Study on Regional Agro-ecological Risk and Pressure Supported by City Expansion Model and SERA Model – A Case Study of Selangor, Malaysia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Shi, Yaoli Zhang, and Cheng Peng Study on Relationship between Tobacco Canopy Spectra and LAI . . . . . . Hongbo Qiao, Weng Mei, Yafei Yang, Wang Yong, Jishuai Zhang, and Yu Hua Study on Spatial Scale Transformation Method of MODIS NDVI and NOAA NDVI in Inner Mongolia Grassland . . . . . . . . . . . . . . . . . . . . . . . . . . Hongbin Zhang, Guixia Yang, Qing Huang, Gang Li, Baorui Chen, and Xiaoping Xin Study on Storage Characteristic of Navel Orange Based on ANN . . . . . . . Junfang Xia and Runwen Hu Study on the Diﬀerences of Village-Level Spatial Variability of Agricultural Soil Available K in the Typical Black Soil Regions of Northeast China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Weiwei Cui and Jiping Liu Study on the Management System of Farmland Intelligent Irrigation . . . Fanghua Li, Bai Wang, Yan Huang, Yun Teng, and Tijiu Cai Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xueyan Sui, Xiaodong Zhang, Shaokun Li, Zhenlin Zhu, Bo Ming, and Xiaoqing Sun

XIII

597

607

615

623

633

641 650

658

667

674 682

691

XIV

Table of Contents – Part II

Study on the Rainfall Interpolation Algorithm of Distributed Hydrological Model Based on RS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoxia Yang, Yong Liang, and Song Jia Study on Vegetable Field Evaluation Index System for Non-Point Source Pollution of Dagu River Basin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jinheng Zhang, Junqiang Wang, Yongliang Lv, Jianting Liu, Dapeng Li, Zhenxuan Yao, Xi Jiang, and Ying Liu

700

706

Study on Water Resources Optimal Allocation of Irrigation District and Irrigation Decision Support System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liang Zhang, Daoxi Li, and Xiaoyu An

716

Study on Web-Based Cotton Fertilization Recommendation and Information Management Decision Support System . . . . . . . . . . . . . . . . . . . Yv-mei Dang and Xin Lv

726

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

735

Food Safety and Technological Implications of Food Traceability Systems Hailiang Zhang, Xudong Sun, and Yande Liu East China Jiaotong University, School of Mechatronics Engineering, 330013, China

Abstract. Food safety has become an important food quality attribute.Both food industry and authorities need to be able to trace back and to authenticate food products and raw materials used for food production to comply with legislation and to meet the food safety and food quality requirements. Traceability is increasingly becoming a necessary task in the food industry which is mainly driven by recent food crises and the consequent demands for transparency in the food chain. This is leading to the development of traceability concepts and technologies adapted to different food industry needs. The content of this paper include several aspects such as overseas food traceability system present conditions and development, food traceability system present conditions, problems and prospect in China, put forward the main measures of pushing on food traceability system of china. Keywords: food traceability, quality, safety, technology.

1 Introduction The demand for food traceability has significantly expanded in the last few years all over the world with increasing incidence of food-related safety hazards and scares such as footh-and-mouth disease, mad cow disease, microbial contamination of fresh produce, dioxin in poultry which greatly decline consumer confidence on food safety. There can be found several definitions for traceability, such as “the ability to follow the movement of a food through specified stages of production, processing and distribution”(Codex Alimentarius,2004), “the ability to trace the history, application or location of that which is under consideration” or “when considering a product, traceability can be related to the origin of materials and parts, the processing history, the distribution and location of the product after delivery”(International Standardization Organization (ISO)). The EU Regulation 178/2002 describes it as “the ability to trace and follow a food, feed, food-producing animal or substance intended to be, or expected to be incorporated into a food or feed, through all stages of production, processing and distribution”.(E.Abad,2009) The term “food traceability” can be traced back to 1986, the year that the first case of mad-cow disease (Bovine Spongiform Encephalopathy, BSE) was reported in the UK. Four years later, the government of UK started a committee to survey the cause and origins of BSE, using the traceability system of cattle production, which is the embryonic form of the current food traceability system. Food traceability has placed responsibilities on producers, processors, D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 1–10, 2011. © IFIP International Federation for Information Processing 2011

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caterers and other handlers in the supply chain to ensure food safety because foods are mostly composed of more than one ingredient and have often been composed of a series of processes. Raw material producers, ingredients/packaging suppliers, distributors, storage operators, retailers, points of sale, shops, and transporters are all relative partners of the food traceability system. Within the food industry, traceability implies the ability to trace and follow feed, food, and food producing through all stages of production, processing, and distribution. As a result, making a complete traceability system will need enormous resources and effort. Traceability is essential, particularly with raw materials, to establish that control procedures have been applied and are effective. There are good examples of where traceability has had a specific approach, e.g. beef labelling and genetically modified materials.(M.F Stringer,2007) Traceability systems can be considered as a bridge between producers and consumers, since the details of where the products come from and how they are marketed is available for those who are concerned.

2 Technological Implications of Food Traceability Systems Food traceability system is highly knowledge-intensive and increasingly informationdriven. Technological innovations are necessary to reduce transaction costs and facilitate the production of top quality,safe and traceable products to meet consumer demands. Technological innovations are needed for product identification,process and environmental characterization, information capture, analysis, storage and transmission. These technologies include hardware (such as measuring equipment, identification tags and labels) and software (computer programmes and information systems). 2.1 Food Product Identification Technology A major character of the food traceability system is the ability to trace-back the history and the physical location of the food products. To achieve these, accurate labeling is essential. The simplest technology to achieve this is to attach a tag to the surface of the food package and to transfer that data on the tag to the bar code of the food product. (Fig.1)The use of computers and other information technologies have spurred the development of electronic identification (EID) systems, which include electronic tags with chips and scanners for reading, storing and transmitting the data to PCs for analysis and long-term storage. An important attribute of tags is that the materials must be resistant to rough handling and bad weather. Advancements in material science have led the development of tags that are resistant to tear which can withstand harsh environmental conditions.Innovations in geospatial science and technology such as radio frequency technology and mobile tracking devices have the potential for collecting and transmitting data from tags to distant locations for storage and analysis. 2.2 Quality and Safety Measurement Technology The success of traceability is to meet the expectations of the consumer and other stakeholders, the ability to ascertain the location of the food product for effective recall in the event of food quality or safety breach. This requires accurate information

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on the maturity and quality attributes and safety status of the product, which must be measured and analyzed using appropriate instrument and procedures. Product features such as size, firmness, soluble solids, acidity, flavour, etc, are some of the physical, mechanical and chemical properties that may require measurement. Nondestructive tests based on force sensing, infrared and magnetic resonance imaging can also be used to measure firmness and other internal quality attributes (Linus U. Opara,2003). 2.3 Genetic Analysis Technology The need to preserve the identity of food product and the demand for genetic traceability have led to the development of procedures and measurement devices for the analysis of the genetic constitutions and contamination of foods and other biological products(Giese, J.H. 2001). 2.4 Environmental Monitoring Technology Environmental conditions such as data of temperature and relative humidity collecting, atmospheric composition of the air including pollutants and so on which impact on the quality stability and safety of food products. Instrumented environmental recording devices for monitoring these parameters are available (Linus U. Opara,2003) .environmental monitoring process is shown in Fig.2.

Fig. 2. Environmental monitoring process

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2.5 Evelopments in Geospatial Science and Technology The integration of geographic information systems (GIS), remote sensing (RS) and global positioning systems (GPS) offers considerable opportunity for the derivation of data related to the food products. These technologies enable data to be remotely collected on a farm, which can be processed, transmitted and presented as product attributes. With respect to food traceability, a vital feature of these technologies is the possibility to map the geospatial variability of selected attributes such as yield, product quality (Bossler, J.D. 2001). 2.6 Web and Database Technology for Food Traceability System Web and database technology relies on the application of appropriate computer system, and which links the food traceability to a central database at the company, national or international level.(Fig.3) Food traceability systems require lots of data uploading to be saved as digital files. The growth in the use of personal computers, with immense processing power, and the continued development of the Internet provides an appropriate environment.The increasing speed and capability of the required communication hardware together with falling prices also contribute to the viability of such a system. Modern personal computers provide a simple means of connecting to the outside world, using software and hardware which are provided with the machine.The system makes use of Internet technology to implement a worldwide solution.It is the physical connectivity of the Internet, as well as the communication protocol (TCP/IP) that is used. The food traceability system can be quickly deployed anywhere in the world. This can be communicated directly to the database server.This process is automated and transparent to the user. After being verified by officially qualified institutions, agricultural food products are labeled with a traceable combination of numbers, just like IDs for foods. Purchasers who purchase goods with these labels can trust the products are officially guaranteed to be of safe and high quality. Terminals set at retail markets are the communication port of producers and consumers. Once the traceable label is scanned on the machine, the detailed traceable information will be showed on the terminal screen. center database of food traceability system

Internet produce process data

base of food produce

sale process data

identity data

data administator

Fig. 3. Center database structure of food traceability system

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2.7 RFID Technology Tags, which contain an integrated circuit chip and antenna, are integrated into objects such that these objects can be identified and their label information can be read. Radio frequency identification involves detecting and identifying a tagged object via radio waves transmitting data from tags to a reader (Jung Lyu Jr.,2009). One of the greatest challenges of implementing food traceability system under certain circumstance lies in the fact that products distribution has a global covering, thus it becomes difficult to precisely trace goods movement throughout the distribution chain. There is a solution that could eliminate these difficulties – an automate gathering of data named radiofrequency identification RFID). Moreover, the RFID tag can store much more information than the linear bar code, and the information can be updated. Sometimes, solutions can be thought in order to combine bar code technology, RFID and vocal recognition, to create a flexible infrastructure which would optimally use the advantages of each technology. (Goodrum, McLaren, & Durfee, 2006; Kwon & Choi, 2008). RFID technology allows the storing of information about all the products that have circulated in a certain container. This type of traceability is very useful for retailers, who can easily locate where to find a certain product, for a rapid delivery. With the help of the RFID tags, a supplier of fresh products (fruit and vegetables, for example) can trace where the goods have been delivered in order to accelerate the payment, or a retailer can be sure that the products are on shelves in the order they were stocked.

3 Overseas Food Traceability System Present Conditions and Development As knowledge and economic grow, the people in developed countries are more concerned about quality and safety of food. Food traceability system is considered as a risk management tool for food safety and is widespread in developed countries such as Japan, the U.S., Canada and many countries in the European Union. Food traceability systems can provide clear, correct sources and marketing routes of food products. With an integrated food traceability system, the government can recall the products immediately and limit the possible loss. In December 2003, the United States developed the statutes of tracking food safety,”Farm Security and Rural Investment Act” requires country of origin labeling for many kinds of food, including perishable agricultural commodities, which required all enterprises involved in food transportation, distribution and import recording their trade information for tracking and tracing back. In addition, the United States also plans to include 70 percent of the cattle in the NAIS (National Animal Identity System) project at the end of 2009. The European Union adopted mandatory traceability actions in food industry since 1st January 2005, Regulation No. 178/2002 establishes that food business operators must label adequately food, in order to facilitate the traceability(Official Journal of the European Union,2002). The Marche Region (Italy) project called SiTRA, aiming to provide food chain stakeholders with a Web platform managing traceability for the main regional food products. Consumers can identify the SiTRA traced products by means a Regional Brand (QM Quality guaranteed by Marche) embedded in the

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product label, reporting the traceability code to access the Web for traceability information of the product. SITRA platform is currently tracing 8 large food chains (fresh milk, bread, pasta, fresh fish, olive oil, wine, pigs and cattle) all over the Region. In June 2002, the Canadian federal government established an ambitious goal that, before 2008, the country would achieve tracing back 80 percent of agricultural products to its source, supporting the "Brand Canada strategy", of which a mandatory identification system for cattle and beef on July 1, 2002 came into operation. Japan was the first country to introduce a food traceability system in Asia. Due to the occurrence of a series of food safety events, the Japanese government raised food traceability system promotion to the list of important administration policies of Prime Minister Koizumi and expected to accomplish 50% implementation of food traceability in 2007 and 100% by the year 2010.Since 2001, the Japanese government has been promoting the development and use of food traceability systems, and the integration of traceability systems with agricultural risk management systems in order to improve food safety amongst food operators such as producers (farmers), retailers, and manufacturers (Nanseki and Yokoyama, 2008). The supply chain networks for the food industry are reacting both to global trends and to the changes brought about by the continued expansion and ever-deeper integration of the European Union. Ferrer and Findlay (2003) hold the opinion that the drive to unity is strong, but the diversity of competitive practices, labour laws, and regulations across Europe different countries and regions is rich. The supply chain winners will be those who can continue to create new opportunities through the implementation of optimised networks and the development of collaborative partnerships across their extended enterprises. (Ingrid Hunt, 2005).

4 Food Traceability System Present Conditions in China 4.1 Present Conditions It is highly necessary for China to establish traceability systems. For concerning about both domestic food safety and international trade, since 2000 China has been adopting lots of measures and programs to introduce, extend, encourage and even mandate traceability system in food supply chain. In legislation, there are a few specific laws or regulations concerning food safety but little referring traceability before 2001. There have been numerous food contamination events and animal diseases like avianinfluenza, foot-and-mouth disease, bad duck eggs and event of milk in recent years, causing extensive panic among the populace and tremendous losses to the farmers of China. Regarding on substantial advantages such as reducing marketing costs, ensuring product integrity, increasing consumer confidence, China is now developing and implementing food traceability programs throughout (Ministry of Commence of China, 2006). Provide visibility of China’s food safety and quality systems to consumers, importers and governments of world, as well as to domestic businesses and consumers. Enhance the user's confidence on the safety of materials, raw materials and products from China. In order to ensure food safety, Beijing had established a specialized food safety traceability system for the 2008 Olympics, in order to monitor food quality from the origin of production to each stage of processing, packaging,

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transportation, distribution till ultimate consumption. The Beijing Administration for Industry and Commerce (BAIC) and Beijing Food Safety Supervision Office (BFSSO) already founded Beijing Food Safety Traceability System (henceforth BFSTS) based on Capital Food Safety Monitoring System (CFSMS) under the network environment of BAIC. BFSTS consists of one first–level platform and four individual sub–systems. The first–level platform is that Beijing Data Centre for food safety traceability, which is responsible for information collection, analysis, evaluation, tracing, early–alarming. The four sub–systems includes fruit and vegetable, animal products, prepackaged food and Olympic food traceability sub–system. One survey in city markets and rural markets performed in 2006 by Ministry of Commence of China showed that 53.7% of city markets, 32% of supermarkets, 80.4% of wholesale markets of agricultural products, 70.7% of retail markets introduced the above initial measures to foster traceability (Ministry of Commence of China, 2006). More than half of frozen food had already been able to be traced back to origin in 2008. In April 2004, the State Food and Drug Administration and other departments chose meat industry as a pilot industry, started meat and meat products traceability institution construction and system implementation (General Administration of Quality Supervision. 2002). The main tasks include: developing suitable technical standards and Management norms, publishing guidelines for implementing traceability system including "Meat products tracking and tracing Guide" and "Fresh product tracking and tracing Guide”. In June 2004, Administration of national barcode management promoting investigated on vegetable products traceability and started an application project on two vegetable production bases located in Shouguang and Luocheng respectively in Shandong province. The project was successful in food quality control in the origin and enforcing standards of market access, product identification and recall. Integrated with electronic auctions and E-commerce, this project established a traceability system for pollution-free vegetables. Shanghai Livestock Bureau legislated to build digital archives for pigs, cattle, sheep, and the residents can now get access to the egg production information through internet. In August 2008, Beijing had already enforced a food traceability system along the full supply chain for the food supplied for Olympic games to secure food quality and safety. According to the law of People’s Republic of China on food products’ safe quality issued in 2006, all agricultural enterprises must set up production recording that should be authentic and be kept for at least 2 years; otherwise, the transgressor will be penalized not more than 5000 RMB. In addition, individual producers are also encouraged to keep recording within their own production. Such actions are considered as the rudiments of food traceability system in China. On June 26 of 2006, Ministry of Agriculture of China (MOA) promulgated the regulation on animal labeling and their feeding documents establishment in farms (No. 67 Act).In this act, the most key points are as follows: animals such as swine, cattle and sheep/goats in the farms must be gained unique identity code around the China, and it shall be labeled with a special tag embodying a unique code before moved from its region of origin. The feeding enterprises must establish their feeding information documents to record inputs mainly used, such as feeds, feed additives and veterinary drugs as treatment. Traceability on food–produced animals will be started in case of any of the following issues: (a) Some labeling are not in accord with livestock and products from

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themselves; (b) Livestock and their products have been infected or contaminated by some diseases and virus; (c) There is no quarantine certificate authorized by the accredited institution; (d) Some veterinary drugs and other venomous substance have been used, which are forbidden in terms of related regulations; (e) when serious animal health events occurred; and (f) other situations in which traceability should be applied. 4.2 Problems and Measure of Pushing on Food Traceability System of China Promoting food traceability systems among consumers should focus on obtaining recognition from consumers. Being unfamiliar with this new concept in china, the public may consider “traceability” only as a commercial term, which makes no difference whether the products are traceable or not. Since most consumers don’t understand the principle and value of food traceability systems, the Chinese government should endeavor to educate the public on the concept of food safety and possible food contamination routes. Once the public recognize that the food traceability system is a possible solution which can prevent certain problems and ensure their health, they will be interested in traceable food products and be willing to purchase the traceable agricultural products at a higher price. Recent theoretical literatures provide some useful information relevant to analyzing different consumers’ increasing concerns about food quality and safety knowledge and the effect on food choices. Some studies about consumers show knowledge about food safety tends to increase with age, level of education, and experience in food preparation. These research findings are useful to assist in research of Chinese consumers’ perception toward quality and safety of safe products (Wang Feng, Zhang Jian, 2009). The basis of the traceability system is the detailed data recorded by the producers. In the early days of promotion in China, most of the farmers didn’t consider the traceability system as a constructive policy but rather a complicated and inconvenient one. From their viewpoint, the consumers would not care about the traceable records; the policy was considered just a waste of effort and time. Besides, some older farmers are not educated as well as the young; so even the ordinary paper records are huge obstacles for them, not to mention electronic documents. For companies, compliance to legislation is recognized as the major driving force towards introducing a quality supervision and traceability system. Value added to products through increased consumer confidence may be another important reason. The government should start to educate the producers about the advantages of implementing food traceability systems, popularizing the concept of food safety, making them believe that this could be a wonderful resource with full cooperation and could lead to a more profitable career. The endorsement from consumer to food traceability system is the biggest strength of promotion. Food safety and quality, rather than price, is considered the most important factor affecting food product purchasing decisions of Chinese consumers (Zhang, 2002). Once the consumers agree and favor the traceable products, the rise in profits will bring confidence to the producers. Through mutual trust established via food traceability systems, the benefits of both the producers and the consumers can be ensured. Therefore, food traceability systems

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can be considered as an investment which gives farmers profitable and stable business, and safe food for the public, leading to a promising future for sustainable agriculture.

5 Conclusion The emergence of food traceability system is the result of developments in improving food quality and safety management. Farmers, processors and handlers, and food policy experts need to be aware of future developments in this area to assist them in implementing food traceability systems for their enterprises. The methods for data capture, data exchange, data storage and the integration of the food traceable supply chain are essential for the success of food traceability system. The traceability system of food products, including fish, poultry and meat products, means that the information of a product, from producing and processing to marketing, is recorded and can be traced, “from farm to fork”. If all the food products are implanted with traceability, the consumer can not only query detailed information about the food but also secure themselves from impairment by checking suspicious process in the food supply chain through the food traceability system.

References 1. Codex Alimentarius, Codex Alimentarius Commission. FAO/WHO (2004) 2. Abad, E., Palacio, F., Nuin, M.: RFID smart tag for traceability and cold chain monitoring of foods: Demonstration in an intercontinental fresh fish logistic chain. Journal of Food Engineering, 394–399 (2009) 3. Stringer, M.F., Hall, M.N.: A generic model of the integrated food supply chain to aid the investigation of food safety breakdowns. Food Control 18, 755–765 (2007) 4. US Federal Register: Farm Security and Rural Investment Act of 2002, vol. 68(210), October 30 (2003) 5. Official Journal of the European Communities: Regulation (EC) No 178/2002 Of The European Parliament And Of The Council of 28 January 2002, article 18 (2002) 6. RFID Position Statement of Consumer Privacy and Civil Liberties Organizations, Privacy Rights Learing house (November 30, 2003) 7. Bossler, J.D. (ed.): Manual of geospatial science and technology, p. 664. Taylor & Francis Group plc., UK (2001) 8. Opara, L.U.: Traceability in agriculture and food supply chain: A review of basic concepts, technological implications, and future prospects. Food, Agriculture & Environment 1(1), 101–106 (2003) 9. Giese, J.H.: Lab exhibits promote traceability and safety. Food Technology 55(8), 100, 102–104 (2001) 10. Ferrer, J., Findlay, C.: European supply chain management characteristics and challenges. Ascet achieving supply chain excellence through technology (2003), http://www.ascet.com (accessed 11/09/2003) 11. Hunt, I., Wall, B.: Applying the concepts of extended products and extended enterprises to support the activities of dynamic supply networks in the agri-food industry. Journal of Food Engineering 70(2005), 393–402 (2005)

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12. Lyu Jr., J., Chang, S.-Y., Chen, T.-L.: Integrating RFID with quality assurance system – Framework and applications. Expert Systems with Applications 36, 10877–10882 (2009) 13. Goodrum, P.M., McLaren, M.A., Durfee, A.: The application of active radio frequency identification technology for tool tracking on construction job sites. Automation in Construction 15, 292–302 (2006) 14. Feng, W., Jian, Z.: Consumers’ perception toward quality and safety of fishery products, Beijing, China. Food Control 20, 918–922 (2009) 15. Zhang, X.: Consumption trends and habits for fishery products in China. In: ASEM Aqua Challenge Workshop (2002)

Function Design of Township Enterprise Online Approval System Peng Lu1, Gang Lu2, and Chao Ding2 2

1 Tourism Department, Hebei Normal University, Shijiazhuang, Hebei, China School of Management and Engineering, Shijiazhuang University of Economics, Shijiazhuang, Hebei, China [email protected], [email protected], [email protected]

Abstract. Township enterprise is a kind of new economic organization that appeared under the special historical background in rural areas of China. Since 30 years of reform and opening-up, township enterprise has made a great contribution to the economic development of China with its unique development style and great vitality. Taking the place of the traditional manual way, township enterprise online approval system applies computer and network technology to realize the normalization and standardization of approval and administration of township enterprise. This paper first gives an introduction on the comprehensive function design of township enterprise online approval system, then makes an evaluation on the system, last points out the sphere of application of this system. Keywords: Township Enterprise; Online Approval System; Evaluation; Sphere of Application.

Since 1990s, human being entered into the Information-Dominated society, which is also called information society. According to the requirements of avoiding risk, moderate advancement and market operation, the management department of township enterprises should provide general business information services and agricultural enterprise information application services for characteristic industry, such as shoemaking, textile and garment, stone carving and stone processing, petrochemical industry, pottery and porcelain, hardware and electromechanical, tea, orange and so on. The agricultural enterprise application service system can provide township enterprises with integrated services in improving production efficiency, reducing cost and timely information gathering. At the same time, we should encourage the powerful and reputable information product enterprises to provide the peasants with both information services and business information in supplying and selling agricultural products. We should also strengthen supervision and crack down on price deception and the behavior of selling shoddy terminals so as to protect the legitimate interests of peasants. Therefore, we should build a government-dominating informationalized township enterprise constructing and organizing management institution and make an overall and long-term general plan for information development and a series of township enterprise e-business normative system to ensure the verity and health of the township enterprise information and the smooth channels of information collecting and D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 11–17, 2011. © IFIP International Federation for Information Processing 2011

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communicating and to strengthen the macro- guidance to the development of township enterprise e-business.

1 The Introduction of Township Enterprise Online Approval System The township enterprise business online approval system can achieve the standardization of township enterprise approval by using the computer and network technology instead of the traditional manual way to handle the application and approval of township enterprise. The administrative department of township enterprise acts as trade and economy commission in the villages and towns, as township enterprise administrative bureau or industrial promotion bureau in the county and also as a small-and-middle-sized enterprise bureau affiliated to industrial information office in the province. The responsibilities of the superior management departments are mainly

Fig. 1. The Overall Function Design of Township Enterprise Online Approval System

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for the interpretation and implementation of policies (referring to various kinds of national preferential policies and encouragement policies), calculating on numbers and examining and approving license for waste metal and coal marketing. The approval of township enterprises is handled by industrial and commercial bureau. If the enterprise is relatively large or with high technology content, the examination and approval shall be handled by development and reform bureau in the province, city and county first, and then get business license from industrial and commercial bureau. 1.1 Requirements of Township Enterprise Online Approval Function 1.1.1 Online Approval Module The online approval business includes the record keeping of the founding of township enterprise and its business change. The online approval functional module consists of the declarer’s bidding and inquiring function, assisting with input of villages and towns trade and economy commission and county township enterprises administrative bureau or industrial promotion bureau, industrial and commercial bureau’s three-level approval function and industrial and commercial bureau’s discipline inspection and supervision function. The online approval module, which is responsible for all the township enterprise approval business, is the core of township enterprise approval system. The necessary materials for township enterprises’ founding include enterprise name, domicile, business place, legal representative or legal person, business registration number, economic nature, organization type, scope of business, mode of operation, category of business, registered capital, number of employees and duration of operation and so on. The required certifying documents shall be uploaded in the forms of scanning or be filled in Word document format. It needs industrial and commercial bureau’s three-level approval in the online approval module. The industrial and commercial bureau’s three-level approval consists of preliminary reviewer’s examination and verification, competent business director’s examination and verification and competent business head’s examination and approval. If any level of the three-level examination and approval system doesn’t make any examination opinion, the examination and approval process will not continue. That is to say, the higher authority can only skim over the township enterprise’ application information and can't make any specific examination and approval opinion if the lower authority doesn’t express any opinion in accordance with the examination and approval process. The higher authority can make the decision of approval or not approval only when the low authority make the examination and approval opinion. This kind of process design can standardize the examination and approval process so as to avoid the skip-level examination and approval, which plays a significant role in the higher authority’s supervision to the lower authority. 1.1.2 Township Enterprise Information Management Module The township enterprise management module is responsible for collecting all the township enterprise information and managing the existing township enterprises. In this function module we can conduct such processes as certificate printing, information inquiry, township enterprise’s cancellation of registration, generating and exporting of information, the management of township enterprise business registration number and so on.

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1.2 Function Output of Township Enterprise Business Online Approval System Township Enterprise Business Online Approval System is composed of two sets of modules. One is Township Enterprise Approval System, including township enterprise set up approval system and township enterprise business change system; the other is Township Enterprise Database Management System, including inquiry system, report management system and certificate print system. Township Enterprise Online Approval System establishes relationships with terminal customers by relying on Internet. To ensure the safety of Township Enterprise Online Approval System and avoid attack and destruction of internet hackers, the system is divided into two web interfaces, one being logged in by natural persons and legal representatives who need transact township enterprise business through user name and password, another being used for approval of industrial and commercial bureau and assisting administrative department of township and county with inputting information through “dongle” password and user name and password. In addition, the system opens a discipline inspection and supervision window to make it easier to inspect and supervise so as to make the approval more transparent. 1.2.1 Township Enterprise Approval System The applicant logs in the application web interface of the Township Enterprise Online Approval System and input user name, password and random verification code successively, according to the indication of the computer, to enter the online approval application program of township enterprise. Then the above mentioned user name and password belong to the applicant who shall remember them carefully for future login and inquiry of relevant information. According to the indication of the computer, the applicant reads the provisions regarding to the establishment application of township enterprise in the Regulations on Township Enterprise and Implementation Rules. After deeply understanding the required documents, conditions and document format for application, the applicant shall click “Next” to fill in the township enterprise establishment form item by item carefully. During the process of filling in the above form, if the applicant fills in wrong contents or the contents filled in need to be modified, then he can click command of modification to fill in the form again. After that, he scans and uploads supporting documents according to the required format. Finally he clicks “Save” and submit the form. The filling contents will be transmitted to Information Center server of Administration for Industry and Commerce through network. The applicant, after receiving the documents returned from any approval level by industrial and commercial bureau, only needs to selectively modify the items not conforming to the provisions according to the suggestions. Then he can submit the documents and enter the approval link again. The purpose of adding the above function is to reduce the repeated input workload of the applicant and save application time. 1.2.2 Township Enterprise Business Change System The business change of township enterprise refers to the change of investor, legal representative, business operation site as well as change of its name. If any above items change, the township enterprise shall go to the original approval authority in time to go through the formalities for the change of the relevant business. In order to facilitate the operators, the township enterprise business change function module is designed in the Township Enterprise Online Approval System.

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Changing applicant uses user name and password to log in the application web interface of the Township Enterprise Online Approval System and enter the Township Enterprise Online Business Change System. Then he shall, in accordance with the indication of the computer, fill in the business change application form of township enterprise after reading the application documents needed to be submitted for the change. During the process of filling in application form, if the alteration applicant fills in wrong contents or the contents filled in need to be modified, then he can modify the contents or fill in the application form again. After that, he scans and uploads supporting documents according to the required format. After the saving and submission, the filling contents will be transmitted to Information Center server of industrial and commercial bureau through network. The applicant of township enterprise shall remember the user name for login and inquiry of relevant information in the future. The applicant for business change, after receiving the documents returned from any approval link by industrial and commercial bureau, only needs to selectively fill in the application form or modify the items not conforming to the provisions according to the suggestions. Then he can submit the documents and enter the business change link again.

2 The Evaluation of Township Enterprise Online Approval System The application of Township Enterprise Online Approval System is a useful exploration and an attempt at using modern network technology to approve township enterprise. It is an important measure for standardizing township enterprise approval management, and it indicates a higher stage of approval management of township enterprise. Meanwhile it is also a useful attempt at promoting Township Enterprise Online Approval System all over the country. 2.1 Convenient for Application The applicant can go through the application formalities of township enterprise at home through preparing the complete application documents in accordance with the requirements of Township Enterprise Registration and Record Stipulation. 2.2 Convenient for Supervision and Making Approval Procedure Opener and More Transparent Every step and link of online checking and approval is recorded in files, thus it is convenient for inquiry and for the leaders to supervise and examine; discipline inspection and supervision window is specially set in the online approval system, it is convenient for discipline inspection and supervision and by doing this, it strengthens the openness of approval. 2.3 More Standardized Approval Formality In the Township Enterprise Online Approval System, the traditional approval procedures and steps are edited into programs recognizable by the computer, the applicant

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only needs to submit step by step the necessary documents according to the conditions stipulated in the Township Enterprise Registration and Record Stipulation and the indication of the computer, then the industrial and commercial bureau can check and approve them step by step according to the programs of the computer, by doing this, it greatly reduces the human factors and non-standard operation in the traditional approval procedure. 2.4 High Safety and Reliability of the Whole Approval System The applicant, using his user name and password, logs in the application web page to submit application documents, while the administration authorities at different levels log in the checking and approval web page with their keys and user names and passwords to check and approve the documents. The method of using different web pages for application and approval respectively effectively avoids the attack and destruction by network hackers and ensures the safety and reliability of the whole approval system. 2.5 Greatly Improve Administrative Approval Efficiency The whole approval procedure, from the applicant’s logging in the network to the checking and approval of administrative authority of township enterprise, is very simple and swift and it lasts no more than half a day. The whole process of application, checking and approval is open and transparent, favorable for building a clean and honest government.

3 The Sphere of Application of Township Enterprise Online Approval System It is difficult for medium-and-small township enterprises to build a famous e-business platform since it needs supporting of technology and a large quantity of money. Township enterprise administrative authorities should build township enterprise private network and information sharing platform; construct a characteristic township enterprise information network system with sound system, fully functioning and highly practical and secure, which can make a link between the province, city, county, villages and towns and enterprises. And the network can also become a promoting platform to domestic and international market and in this way promote township enterprises e-business actively to intensification, economizing and efficiency.

4 Conclusion The township enterprise online approval system belongs to the areas of e-government, and the main objective is to provide a convenient and effective way of doing business for the applicants. How to save the application time and cost of user and put forward a realistic model of the online approval system would be the basis for the township enterprises to transfer to the network. Using decomposition of structured analysis method step by step, the paper proposes a functional model of township enterprises

Function Design of Township Enterprise Online Approval System

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online approval system. According to the characteristics of township enterprise business and combining with the traditional method of township enterprises business, the paper puts forward a kind of functional model of different functional modules and roles, which relies on the contacts between the internet network and end users. The functional model allows the user to shorten the application time to the minimum and satisfies the needs of users. It achieves standardization of township enterprise approval and management, thus reduces processing costs. It will also accept social supervision, so that the efficiency can be improved, and it serves the community better. In order to make the model functions well, it still needs to combine with the actual function of township enterprises approval, so that the function of the system can be further close to the actual environment.

References 1. Aharon, K.: Phases in the Rise of the Information Society. Info (2000) 2. General Office of the CPC Central Committee, General Office of the State Council: National Informationization Development Strategy 2006—2020. ZBF (2006), http://www.cnii.com.cn/20050801/ca350966.htm 3. Zhang, J.: Practical Exploration of Online Approval System. Journal of Ningbo Radio & TV University (2007) 4. Zhang, J.: Design and Realization of Online Approval System. Journal of Lujiang University (2005)

Application of GPS on Power System Operation Chunmei Pei1, Huiling Guo2, Xiuqing Yang1, Bin He1, Wei Liu1, and Xuemei Li1 1

Beijing Vocational College of Electronic Science and Technology, Beijing, China 2 China University of Mining Technology (Beijing), Beijing, China [email protected]

，

Abstract. Applying GPS positioning and navigation technology to power systems will realize precise navigation of power equipments, improve power system automation of routine works, and enhance working efficiency. In case of emergency, rapid fixing arrangements can be implemented through monitoring and command platform. With advance communication technology, the realtime video can be transferred to the experts all over the world for remote joint consultation. Keywords: Communication Terminal Equipment, Position and Navigation.

，GPS Technology, Power Transmission

1 Introduction After years of development, GPS system has been extensively spread out from mainly military usage to civilian ones. Now more and more GPS terminal, such as PND (Portable Navigation Device), CND (Car Navigation Device), GPS Cell Phone, became popular in people’s daily lives. GPS-related applications of various industries have also gradually growing and many industries have their own GPS applications. At present, most technologies of modern Power Enterprises have met international standards, but the methods of inspection and location are at a lower level, still relying on manpower. The equipments of Power Enterprise are diversiform and widely distributed, so it is a fact that many inspection and repairing staff are not familiar with the equipments’ accurate positions. The traditional method is that senior staff lead the way for young people, which is a waste of manpower. Particularly in the accident emergency, the traditional method cannot ensure technical personnel to arrive at the scene in time. GPS positioning and navigation system will replace the traditional method with high-tech satellite navigation, which is an ultimate solution to the current problems.

，

2 The Advantage of GPS Positioning and Navigation System in Electrical Power System 2.1 Improvement in Efficiency of Daily Operation and Maintenance China’s territory is vast. The number of electrical power equipments is big, and they’re widely distributed. Lots of devices are deployed in remote areas, which brings D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 18–22, 2011. © IFIP International Federation for Information Processing 2011

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much inconvenience for inspection and checking. It’s difficult to find the equipments. The traditional method is to train the new generation of staff by senior ones. This approach is neither scientific nor efficient, especially for large-scale activities (such as the Olympic Games), which need regional cooperation. The GPS positions of all equipments are entered into the map data, so everyone can easily find the destination with a GPS device. Labor cost is saved and the efficiency is significantly improved. 2.2 Improvement in the Ability of Handling Accidents Accidents can not be completely avoided today. In addition to active prevention, a quick solution is particularly important after it happens. Quick and accurate arrival at the scene with a precise navigation device could avoid great loss of the country. Meanwhile, with advanced wireless communication systems, real-time videos of the accident are transmitted to the experts all round the country for remote diagnosis, which can greatly enhance the ability to handle accidents.

3 The Benefits of GPS Positioning and Navigation System in Electrical Power System 3.1 Considerable Economic Benefit Power failure could cause national economic disaster. A malfunction may cause economic losses from a few hundred million to more than a billion dollars. (Northeast blackout in United States, 2003, according to the USA and Canada Joint Investigation Team’s published report in Dec 5,2003, was the most serious in U.S. history to make a total blackout affecting about 50 million citizens. During the two day power outage, plants shut down and companies stopped businesses caused 4 to 10 billion U.S. dollars losses.) When an emergency occurs, every second can be extremely valuable. GPS positioning and navigation system can realize fast positioning, rapid troubleshooting, and hence avoiding economic losses as much as possible. Meanwhile, the reservation of the guides can be avoided in daily operation and maintenance, even in large cross-regional operations. The labor cost is greatly saved. 3.2 Large Social Benefits Electrical power is closely related to everyone’ life in civil society. In any city, power failure is an inconceivable disaster. Several large-scale power outage caused by the accident in history gave local people painful memories. Thus, with GPS positioning and navigation system, the ability of troubleshooting is enhanced for Power Enterprises, which has a very significant impact on people’s livelihood and social stability.

4 The Main Functions of GPS Positioning and Navigation in Electrical Power System 4.1 Positioning of Transmission Towers, Substations and Offices The locations of transmission towers, substations, offices and so on, are preset in GPS devices, as in Fig. 1. With an electronic map in GPS devices, after positioning by

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GPS satellites, the route can be automatically calculated by directly clicking on the destination. The route from the start point to the destination can be reasonably planned and tracked, as in Fig. 2. 4.2 Dynamically Addition or Subtraction of Location Information According to Requirement The system employs open data structure, which makes it convenient to add or remove location information. When new equipments are added, or some equipment is out of

Fig. 1. To preset location information

Fig. 2. To start navigation

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date, the user can easily make the change by him/herself. The system also realizes data sharing of address book and navigation path with different devices. Crossregional cooperation can be achieved by simply integrating location information of different areas. New devices are no longer required. 4.3 Real-Time Monitoring, Improving Management Efficiency The GPS navigation device receives GPS satellite signals, automatically positions, and sends the location information in the forms of SMS or data (GPRS / 3G, etc.), to the master control center, via built-in wireless modules, as shown in Fig. 3. The master control center receives the information, extracts the location information, and dynamically displays the longitude, latitude, speed, status, etc., of the vehicles on the electronic map, as shown in Fig. 4. By integrating the data collected, the corporation can find the most appropriate operating fashion, avoid waste, and save cost. By digging deeper into the data, the analyzer can provide the most authentic and reliable reference to the management team, to make more opportunities.

Fig. 3. GPS Monitoring System

4.4 Combined with Advanced Network, to Enhance Emergency Response Capabilities When dealing with urgent accidents, the monitoring platform can accurately obtain the distribution of vehicles and personnel, and carry out overall arrangements. At the same time, GPS positioning and navigation system not only can guide staff to the scene quickly, but can communicate with supervisors via wireless communication capabilities in time, to obtain the correct commands. When facing with complex problems, live scene video can be sent to the master-monitoring center via advanced wireless network (GPRS/3G, etc.). Experts from different regions can participate in the multi-party consultation to diagnose and resolve the problem in time, saving the loss.

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Car No.1 80km 52.213918,7.12532

Fig. 4. GPS system monitoring interface sketch

5 Conclusions Electricity is closely related to people's lives. Power Enterprises are taking on more social responsibilities than other general ones. It is Power Enterprises’ duty to keep the power grid working stably and regularly. GPS positioning and navigation system, which relies on advanced technologies and is integrated with modern Internet applications, will greatly reform the way of inspection and routine work, thus improving working efficiency. It can also play a valuable role in emergency. As GPS positioning and navigation system is widely used in Power Enterprises, it is no doubt that the whole power grid’s modernization and technology level will be improved. It will make a positive contribution to the society.

Reference 1. Zhu, K.: GPS Application in Power System. J. Computer & Digital Engineering, Beijing (2007)

Greenhouse Temperature Monitoring System Based on Labview 2

Zhihong Zheng1, Kai Zhang1, and Chengliang Liu 1

School of information and control, Nanjing university of information science & technology, Nanjing Jiangsu, China, 210044 2 School of mechanical and power engineering, Shanghai Jiao Tong University, Minhang Shanghai, China, 200240 [email protected], [email protected], [email protected]

Abstract. The environmental temperature plays an important role in the growing crops. How to make use of computer technology to realize automatic control ambient temperature of greenhouse is a hot issue in the intelligent agriculture. The control of environmental temperature in modern greenhouses is collected and analyzed by monitoring system of greenhouse environment based on Labview software and wireless communications technology. NRF24L01 wireless receive-send model makes temperature collection come true. Moreover, managing computer makes data wireless communications become possible. Over- temperature alarm information is transmissioned to user timely. Finally system interface is designed on the Labview software platform. Keywords: temperature, intelligent agriculture, labview, temperature sensor.

1 Introduction Facility gardening is a kind of production with the bad-effect condition in which the crops (flowers, fruit trees and vegetables) don't tend to grow normally in the cold or hot season .Thus people must utilize heat preservation, cold-proof, temperature reduction, defense and equipments in a man-made way to create an environment that favors the crop's growth without the effect of climate change[1]. It is a comprehensive greenhouse technology, which meets the demand of ecological condition in plant including light, temperature, water, gas, soil and nutrition. Planting in the different seasons is able to yield high vegetables and fruits production in good quality. Comparing with foreign countries, overall technology ability of national facility gardening is poor due to the later start and shorter development time, and therefore environmental regulation should be enhanced. Diverse facility structure, automatic production management, mechanized operation, production intensification are the typical features in Holland, America, Japan, France and Israel. In those countries, modern industry, high technology and advanced management equip agriculture. We utilize computer and information technology to realize intelligent management of the temperature. The system adopts Labview software, wireless communications technology and GSM technology to realize temperature auto- monitoring and alarm. It is a good way to facility gardening automatic management. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 23–29, 2011. © IFIP International Federation for Information Processing 2011

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2 Wireless Communications on NRF24L01 It is good for monitoring environmental from several spots due to large gardening scale, especially in a sunny exposure and at vents. Outside environment largely influence the temperature. So we should set more spots to collect and pay special attention to the important spots. It is the best to adopt wireless communications and use lower powered chip to timely send collected temperature to managing computer due to difficult allotting the positions. In this thesis, NRF24L01 wireless receive- send pattern as wireless communication equipment and set 6 sensors (all are 18b20) to attache to it. The place is that respectively two positions at vents, middle and side in the greenhouse. The collected environmental temperature is sent to store in the computer by NRF24L01 pattern in time. 2.1 NRF24L01 Chip Introduction NRF24L01 chip adopts 2.4 Ghz global ISM band comply with 126 multi- spots and skip band communications requirement. Build- in CRC hardware error detecting and one to many spots communications requirement. Module is able to set up address. If only receiving this computer address, the data can be sent out(offer interrupt directions). Directly connected with single chip to use, programming is very convenient. Receive-send module is categorized to Enhanced ShockBurstTM, ShockBurstTM and Direct ways, which is made up to device configuration[5]. Four parts as bellows: Data width: declaring data occupied decimals in the radio frequency database. It renders NRF24L01 to tell data of receive-send database from CRC code; Address width: declaring address occupied decimals in the radio frequency database. It renders NRF24L01 to tell address from data; Address: receive data address, address of Passage 0 to Passage 5; CRC: yield CRC check code and decoding If use CRC technology within NRF24L01, CRC checking code should be used in the configuration( CONFIG’s EN_CRC). Send and receive the same protocol. Under the Enhanced ShockBurst TM pattern, use first-in and first-out stack area, data is sent to micro controller with low speed. But it can save more energy under high speed. Therefore, using low speed micro controller get high sending ratio as well. All the high speed signals processing in the chip under ShockBurstTM receive send pattern, NRF24L01 automatically process character and CRC checking code. When sending the data, character and CRC checking code is added. EC is high under sending pattern. It will take 10us to send over. 2.2 Data Receive- Send Process of NRF24L01 NRF24L01 adopts Enhanced ShockBurstTM pattern to send,it is shown as bellows: send the address and data to NRF24L01 as time sequences; configure CONFIG register and make it access to sending pattern; micro controller put CE higher(at least 10us), activate NRF24L01 and send Enhanced ShockBurstTM

Greenhouse Temperature Monitoring System Based on Labview

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EnhancedShockBustTM sending has four steps: (1) supply electricity to ratio front; (2) Data packing ( adding character and CRC checking code ); (3) sending data package with high speed ; (4) send it over, NRF24L01 enters idle state. Sending BYTES data procedure via NRF24L01 pattern are as follows: uchar SPI_Write_Buf(BYTE reg, BYTE *pBuf, BYTE bytes) { uchar status,byte_ctr; CSN = 0; status = SPI_RW(reg); for(byte_ctr=0; byte_ctr
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2.3 Computer Collecting Data NRF24L01 pattern conect to sensors as data collecting . It is required to collect all the spots’ environmental value per minute due to slow temperature change. In the other aspect, wireless data receive- send pattern sends temperature data to internal computer to store and analyze taking advantage of computer serial communications. NRF24L01 data is sent through single chip according to receiving address. Write the data to Labview’s TDMS formats storage files by sequences. And show the temperature value collected from every sensor. The circuit drawing connected with single chip as bellows: IRQ MISO MOSI SCK CSN CE VCC GND

8 7 6 5 4 3 2 1

VCC

S1 SW-PB

VCC

AT89S52 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

J2 nRF24L01Wireless Transceiver Module C7 10uf

R12 10K

C6 30pf 12M CRYSTAL

C7

P10/T P11/T P12 P13 P14 P15 P16 P17 RESET RXD TXD INT0 INT1 T0 T1 WR RD X2 X1 GND

VCC P00 P01 P02 P03 P04 P05 P06 P07 EA/VP ALE/P PSEN P20 P21 P22 P23 P24 P25 P26 P27

40 39 38 37 36 35 34 33 32 31 30 29 21 22 23 24 25 26 27 28

U1 15 C3 10uf 6 4 C2 10uf

5 14 7 13 8

GND

VCC

CAPCAP + CAP 2+ CAP 1+ CAP 2-

CAP 1-

RT-OUT1 T-IN1 RT-OUT2 T-IN2 RR-IN1 R-OUT1 RR-IN2 R-OUT2 MAX232

16 2 1

C4 10uf C1 10uf

3 11 10 12 9

J1 1 6 2 7 3 8 4 9 5

30pf DB9

Fig. 1. J2 is NRF2401 wireless communications pattern, AT89S52 single chip which uses P1 foot’s I/O to imitate SPI connector links NRF24L01 wireless receive-send pattern. Received temperature value attach to max232 chip and then to computer as serial P3^0 and P3^1 pin.

3 Sending Over-Temperature Alarm Information to User When temperature goes above the definited value, the monitoring system should notify the user so that take measure to adjust the temperature in the condition of no people guarded and low auto processing capability. Nowadays mobile is pretty popular, thus send timely information to user’s phone by using GSM module is a good way. Here putting MCU into USB and then insert a SIM into MCU. When the temperature exceeds the defined value, the software platform will transfer such information to MCU. Finally MCU sends the information out via GSM network. This system send messages to mobilephone via MCU, thus we should consider phone code’s compatibility. PDA pattern is a common sending or receiving SMS information method. Most of the phones stand by this pattern. Each GSM SMS can transfer no more than 140b data. It is enough for transfer alarm information.SMS adopts a store-and-forward system and provides a guaranted two-way servise. Don’t make call’s connection and release. That’s not collided with voice messege. We can receive the SMS even through the phone in the conversation state still receive SMS. It’s convenient to transfer alarm information by SMS. Computer and MCU’s communications is based on virtual serial communications. On Labview software platform, attribute set of serial communications is: baud rate 9600, 8 data bits, one stop bits, no error check. Monitoring system writes down AT instructions via serial:

Greenhouse Temperature Monitoring System Based on Labview

AT + CSCA =″+ 8613010710500″< CR > centre number

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// set SMS

AT + CNMI = 2 ,1 ,0 ,0 ,1 < CR > //set receive-send way via SIM card AT + CMGF = 1 < CR >

//set Text pattern

AT + CMGS =″15951775730″< CR > // send one short message, 15951775730 is target address Warning! <^Z >

// SMS content

AT + CMGR = 6 < CR > // read receiving SMS, suppose if new SMS’s postion in the SIM is 6 + 861595177573 is mobile service center number in Nanjing area. The side double quotation mark should be sent. < CR > is enter mark. Each AT instrucion is end up with enter mark; Warning! That’s the SMS content; <^Z > is CTRL + Z,SMS should be ended up with < ^Z >. Last line is readable SMS instructions, in which 6 is SIM index number. Once that instrucion is sent, GSM pattern sends bellow informations back from UART interface: + CMGR :″REC UNREAD″,″+ 8615951775730″,″19/ 05/ 10 ,15 :54 :00 + 00″Warning! It is easy to read out sender’s mobilephone number,sending time and SMS conetent. The system writes AT intructions into virtual serial, which comes out by Labview software. There are 6 AT instructions to write down by sequence. So each time we respectively write down different instrutions with condition structure in the circular struction. Procedures are as follows:

Fig. 2. The program is to write AT instructions on Labview

4 Making Use of Labview Software Developing System Platform Labview is a kind of developing environment of graphic program language. It is widely accepted by the industry and education and lab, which is considered as a standardized data collecting and equipment control software. LabVIEW integrates the hardware in the GPIB, VXI, RS-232 and RS-485 agreement and the entire functions from data collecting card communications. It also contains standard library function that is easy to apply for TCP/IP, ActiveX, etc. It makes user rapidly build self data

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Fig. 3. The above waveform shows temperature change curve within one day. There are 48 points in the X, one temperature is collected per half an hour. It directly shows temperature change conditions in one day. But six numbers below waveform shows current temperature by 6 sensors. Obviously, the value of Sensor 1 and Sensor 3 at the center position is lower. But the value of Sensor 5 and Sensor 6 at the corner is a little bit higher.

and analyzed system, and then system over- temperature alarm information is sent to user via serial writing functions. TDMS data storage formats is mathematics model specially designed for data storage. It’s characterized as steady read data API and self- configured data management tools that are used to manage data. The collected temperature each time is stored as binary in the TDM format folder. The value from the sensor can be find out by sequence. On the system interface, value can be traced through replaying the data and time input. Alarm light will turn to red to remind user that the current temperature has exceeded the defined figure. Thermometer shows the maximized temperature value. Below is monitoring interface.

5 Conclusion Through the analysis of the monitoring results, it turns out that,when the change of environmental temperature is large in one day, the hysteresis of temperature adjustment mainly manifested in space. In the middle position near the thermostat, sensor detects the environment temperature unchanged, equal to the set temperature. But in the corner of the greenhouse external environment, the detected temperature is affected by the temperature of external environment, there exist certain volatility. Therefore, it is necessary to install the adjusting devices at the position where the temperature is vulnerable effectted by the external environment temperature, it can reduce the influence of temperature change for crops. The control of environmental temperature in modern greenhouses is collected and analyzed by monitoring systems of facility gardening environment based on Labview software and wireless communications technology. NRF24L01 wireless receive-send model makes temperature collection come true. Moreover, managing computer makes data wireless communications become possible. Over- temperature alarm information is transmitted to user timely.

Acknowledgment This paper is sponsored by National High Technology Research and Development Program 863 (NO:2006AA-10A301).

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References 1. Yu, H., Zhang, Y., Sun, R.: LabVIEW-based research of remote monitoring system for greenhouse. Agricultural and Machinery Study, 75–77 (2004) 2. Qiu Shi Science and Technology: The Navigation of Typical Module Design by MCU, pp. 194–202. Posts & Telecom Press (2004) 3. Cheng, X., Zhang, Y.: LabVIEW 8.2 Programming from Entry to Maste, pp. 312–315. Tsinghua University Press (2007) 4. Qi, F.: Software Implementation of Intelligent Control System of Greenhouse environment based on Labview, pp. 1–2. Zhejiang University (2004) 5. Shenzhen Yun Jia Technology Co., Ltd.: NRF24L01 Manual, pp. 6–10 (2008) 6. Wu, B., Liu, X., Wu, M.: Reserch of GSM-based Universal Remote Alarm Controller. Computer Engineering and Application, 92–94 (2007)

Image-Driven Panel Design via Feature-Preserving Mesh Deformation Baojun Li1, Xiuping Liu2, Yanqi Liu2, Ping Hu1,*, Mingzeng Liu1, and Changsheng Wang1 1

School of Automotive Engineering, Faculty of Vehicle Engineering and Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, 116024 Dalian, China 2 School of Mathematical Sciences, Dalian University of Technology, 116024 Dalian, China {bjli,xpliu,pinghu}@dlut.edu.cn

Abstract. In this paper, we propose an image-driven 3D modeling technique for rapid panel design. Our semi-automatic approach is based on template technique and mesh volume deformation controlled by a special cage. We designed our modeling system to be interactive in 2D, automating the process of shape generation while relying on the user to provide image samples. Once a parametric model template is given, using the contour extracted from images, the new control cage corresponding to mesh models generated. Then the geometry of new panel is automatically recovered from the deformable template model. Our system also allows the user to easily reconstruct other 3D objects in a similar manner, such as realistic-looking plant modeling from images. We show realistic reconstructions of a variety of panels, automobile shapes and demonstrate examples of plant editing. Keywords: Image-driven, Mesh deformation, Panel Design, Deformable template.

1 Introduction Nowadays polygon meshes are widely used in both geometric modeling and finite element analysis fields. Mesh deformation is useful for providing various shapes of meshes for CAE tools, especially in very early phases of conceptual automotive design. Most of 3D objects are largely dominated by a few typical features which include contours, even and engineered meanings. Thus, to create 3D models with new appearances by processing and reusing the existing models is becoming an extremely important way to ease the efficiency problem of geometric design in computer-aided design and computer graphics. Space deformations (volume deformation) are to this day the method of choice for shape deformation due to its independence of surface representation[1]. A space deformation is defined via a (usually simple) control cage or grid; user-defined deformation of this object is interpolated to the 3D space and evaluated at the input surface *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 30–40, 2011. © IFIP International Federation for Information Processing 2011

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points. Space deformations are popular since they can handle various object representations, including parametric surfaces, polygonal meshes with multiple connected components, etc. In addition, space deformations are simple to implement, and they are highly efficient and robust, because the cost of the deformation is mainly dependent on the complexity of the control object and not on the deformed shape. Early space deformations used lattices as control objects, and then had also been explored as FFD[2], EFFD[3], and DFFD[4] etc. Later work proposed the use of so-called cages as control objects for shape deformations. Typically, the cage is a very coarse and offsetted version of the input shape. Various coordinate functions have been designed to carry over the deformation of the cage to the entire space, such as mean-value coordinates[5], harmonic coordinates[6], Green coordinates[7]. However, cage-based deformation schemes in references [5][6][7] which preserve differential properties, so far, cannot support direct manipulations very well. Cages generation and its manipulations are more complicate. Thus, in this work, for more adaptive for different kind of FEM meshes and easy to edit in 1D, we adapt modified DFFD [4] method as our main deformation technique. The core techniques of the reuse of existing 3D mesh models using mesh deformation, the key property of mesh editing is interaction techniques and more input examples, such as images, 2D sketches. Masuda et al. proposed a combination method to manually specify varying surface stiffness for panel design[10], [11]. Gal et al. introduce a so called iWIRES, a novel approach based on the argument that man-made models can be distilled using a few special 1D wires and their mutual relations0. In this paper, a simple 1D editing method based on symmetric projection is developed, which deals with the control box of DFFD easily and can support the image examples very well. In this paper, we introduce an image-driven mesh morphing framework for rapid design of the automobile panels, and also generalize this method to other applications, such as realistic-looking planting modeling from images. Section 2 presents the whole pipeline of the method. Then the detailed algorithm of the mesh deformation and contour extraction from images are introduced in Section 3. Section 4 gives the numerical examples and discussion.

2 Overview of Image-Driven Framework For production of high quality panels in a short period of time, the more freely and rapid design technique is widely studied in the past few years. Inspired by IWires method [1] and image morphing[12] method, our main motivation is to generate new models rapidly from images; meanwhile, users can edit the shapes in 1D. In this section, we will introduce our framework of the image-driven panel design based on mesh deformation. Fig. 1 shows the whole process of our method proposed in this paper. There are three main parts of this framework, which include contour extraction from images, generation of initial control box CB0 and new control box CB1, deformation and related verification via CAE software and aesthetics respectively. The pipeline of the framework illustrated in Fig. 1 is given briefly as follows. Pre-processing (Control box generation) In the pre-processing phase, the deformable mesh template M should be chosen and constructed properly according to needs of the practical panels. The first step of the

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CB0 generation algorithm is to compute the bounding box of the initial dense mesh model M by principal component analysis (PCA); the so constructed bounding box captures the major geometric shape of the mesh M. For the simplicity of editioninteraction, we consider the main contour of the model via projection mapping, see Fig. 2. Then the corresponding contour and the control box CB0 associated with DFFD method should be constructed automatically.

Fig. 1. Flowchart of our method

(a)

(b)

(c)

(d)

Fig. 2. (a-d) Control box generation

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Fig. 2(a-d) illustrates the construction process of the control box corresponding to the given model in Fig. 2 (a). More details are described in Section 3 and Section 4. New control box constructed from images For the purpose of the image-driven mesh deformation, the key point is to create new control box from input image examples. Moreover, there are two core steps should be done carefully, which are the contour extraction from images with high precision and subsequent reconstruction of the new control box respectively. This stage will be described particularly in one full section due to its complicity with many techniques, see Section 4. Panel design via mesh deformation With the deformable template and the control boxes CB0 and CB1 are given, the next task is to generate the new panel or model using mesh deformation. The final mesh models are generated to meet requirements of panel CAE analysis, such as crash, NVH, durability and formability. Thus, a general deformation method which is independent of surface representation will be developed. In this paper, FFD method is used. In the next section, the mesh deformation techniques are described in detail.

3 Design via Mesh Deformation The rapid design of panels or carbody shapes is constrained by the conflicting requirements of multiple objectives. For example, designers need a more conventional CAD tool to modify the original models to obtain the shape changes required and time constants necessary for these changes. However, the final design is determined by CAE engineers and designers to verify the artistic and reliability of the shapes. State-of-the-art commercial CAE software, such as LS-Dyna, Nastran, Abaqus, PamCrash, Fluent, etc. are all based on meshes. Thus, this paper focuses on the platform which enables the user to rapidly change an existing FE / CFD mesh into a new target shape without having to redraw it in the CAD system. In this section, the mesh deformation method used in this paper will be introduced in detail. 3.1 Pre-processing Firstly, in order to rapidly generate a panel model, it is important that the suitable deformable template should be chosen and constructed. Thus, a series of panels or carbody database is constructed to meet the requirement of the different products. Once the template model was constructed, the following step is to generate a corresponding control box, which can design new shapes via space deformation method. In this paper, for the sake of edition simplicity, the 2D edition approach will be adopted. Now we introduce our main methods used in this work in brief. The automatic CB generation steps are described as following: 1) Compute the principle axis of the template model by Principal Component Analysis (PCA)[17]; 2) Rotate and project the model into the principle plane, then obtain the contour polygon, see Fig. 2;

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3) Automatic generation of the control polygon feature points from the contour; see Fig. 2b; 4) Semi-automatic generation of control box CB0 by tensor production to the principle axis, see Fig. 2 c-d. It is noted that the suitable CB0 should be construct carefully, which affects the deformable template and the final design. Meanwhile, control boxes CB0 and the corresponding CB1 also provide the feature-preserving of the template through space deformation method. In this work, we use the projection method in order to simplify the user-interaction process, that is, designers can modify the shapes in 2D principle plane but in the complicated 3D model directly. Taking the advantage of 2D editing, our method can generate a new model just from an input image or image examples, which is so called image-driven panel design method. Fig. 3 illustrates a new control box generated by our method.

Fig. 3. New control box generation by projection

3.2 New Control Box Generated from Images Due to editing the control box CB0 in 2D while in 3D directly, our method can generate a new control box from a new contour with some constraints. So, an image-driven modeling method is proposed based on the mesh space deformation method. This part is of the most importance in this framework, and will be given in Section 4. 3.3 Mesh Deformation In order to make use of surface-based techniques for deforming automobile sheetmetal panels, Masuda et al.[10][11] develop the soft and hard constraints on the mesh and propose a framework which can preserve the form features of the sheet-metal panel while deforming the model. Huang et al. [13] proposed a morphing method with feature-preserved for panel design, which use the DFFD method on NURBS surface directly. However, as described in Section 3, the rapid panel design method should provide different meshes to satisfy the need of subsequent CAE analysis. Thus, in this work, the mesh space deformation theory is used rather than the surface-based method.

4 New CB1 Constructed from Images In this section, we introduce the detailed techniques to reconstruct new control box CB1 which shares the same topological structure as the original one CB0, see Fig. 4.

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Fig. 4. Pipeline of CB1 reconstruction from an image

4.1 Contour Extraction from Images First of all we introduce how to extract the exact contour information from an ideal panel image which is the key point to generate new control box. As shown in Fig. 4, the input images should be pre-processed to obtain a contour with high precision firstly. There are two necessary operations to improve the extracted result in the pre-processing phase, which are image resizing and image smoothing respectively. In this work, for higher effencicy, the bi-linear interpolation method is used to resize an image for ideal; In order to obtain an initial contour from the input image, Guassian smoothing filter is used. Fig. 5 or an imput image example, and the Fig. 5 obtained after the pre-processing stage. As the pipeline shown in Fig. 4, the following step is to extract the contour from the image, as shown in Fig. 5b-c. The improved gradient vector flow snake (GVF) [14] method is used in this paper. Now we introduce this method in brief. A GVF model is the vector field

v ( x, y ) = ⎣⎡u ( x, y ) , v ( x, y ) ⎦⎤

,

which minimizes the energy functional

ε = ∫∫ μ ( u x2 + u y2 + vx2 + vy2 ) + ∇f

2

2

v − ∇f dxdy .

This variational formulation follows a standard principle, that of making the result smooth when there is no noisy data. In particular, when | ∇f | is small, the energy is dominated by sum of the squares of the partial derivatives of the vector field, yielding a slowly varying field. On the other hand, when | ∇f | is large, the second term dominates the integrand, and is minimized by setting v =| ∇f | . This produces the desired effect of keeping v nearly equal to the gradient of the edge map when it is large, but forcing the field to be slowly-varying in homogeneous regions. The

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(a)

(b)

(c)

Fig. 5. Contour extraction from an image

parameter μ is a regularization parameter governing the tradeoff between the first term and the second term in the integrand. This parameter should be set according to the amount of noise present in the image (more noise, increase μ ). 4.2 Post-Processing of Contour In the above subsection, the initial contour is obtained from an image, as illustrated in Fig. 4a-c. However, the initial contour cannot match with the contour of original control box CB0 very well. There are two necessary operations to solve the problem, which are contour assessment and feature point assignment respectively. Contour assessment Once the template model is given, through the pre-processing operation in Section 3, a constant original contour is obtained. In order to generate a final control box CB1 with the same topology and features as CB0, the original contour and contours extracted from images should be assessed exactly. Fig. 6 shows the geometric assessment result for a given carbody model, and Fig. 6b illustrates assessment result of the contour shown in Fig. 4c. It is should be noted that this step can be an automatic process for a given specific panel case.

(a)

(b) Fig. 6. Geometric assessment of contours

Feature point assignment There is another important point of contour post-processing, i.e., feature point assignment. It is obvious that there are no any apparent relationships between the original contours and contours extracted from images. Thus, to bridge this gap, we should assign some specific feature points on the contours which determine the panel shapes and main corresponding engineering meanings. In this paper, we assignment the

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Fig. 7. Coding the feature points

feature points semi-manually. As shown in Fig. 7. Coding the feature points, the coding of the feature points for contour in Fig. 4c is given. 4.3 Reconstruction of New Control Box CB1 By extraction from images and suitable post-processing, a coded contour with geometric assessment is obtained. In this step, a new control box CB1 with the same topology with CB0 will be constructed using the above contour information. The geometric transformation between the contour from image and the contour associated with CB0 is used. Due to the difference of geometric information between two contours, we adjust the control polygon of the contour extracted from image, according to the feature calibration and geometric information of the original contour. Fig. 8 illustrates an adjustment for the contour shown in Fig. 6 (in blue), and the red one denotes contour associated with the template. After this operation, the control polygon and contour of CB1 are obtained.

Fig. 8. Adjustment of the control polygon

Fig. 9. The final contour of the CB1

In order to construct a new control box CB1 with high precision and the same topology with CB0, a resampling operation with high precision is necessary. However, the contour extracted from an image directly usually has a low-resolution. In this paper, cubic spline interpolation method[16] is used for the initial extracted contour.

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Then the final contour of the CB1 is obtained by resampling from the interpolated spline curve with the same number of points with CB0. Fig. 9 shows the final contour of the CB1 corresponding to Fig.5a-c.

5 Implementation and Numerical Examples We have applied our method to a large variety of panel and carbody models, and obtained very ideal results. All models are computed on a double 2.80-GHz PentiumR (2G RAM) machine, using VC++ 6.0 and KMAS/COMX development platform. In this section, three images of cars from Internet are given, and the associated carbodies are computed rapidly by our framework. Fig. 10 is the template model used in this paper, and the final corresponding designs are given as following, such as Fig. 11-13.

Fig. 10. The template carbody model in this paper

Based on the template model shown in Fig. 10, a new carbody design from image in Fig. 5 is obtained as following figures using the new control box illustrated in Fig. 3 and Fig. 9. Fig. 11 bottom shows the final FEM mesh of the new design, and the others shows the shading one from different views.

Fig. 11. The final design corresponding to Fig.5-9

We also provide two carbody designs from images directly using our method, see Fig. 12 and Fig. 13.

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Fig. 12. New design generated using our method

Fig. 13. New SUV design generated using our method

6 Conclusions In this paper, we proposed a framework for rapid panel design based on mesh space deformation. Furthermore, we also explored the approach to create any other kinds of man-made, engineered objects and realistic shapes. This approach generates so many different kinds of shapes due to image example input which is abundant from internet. Numerical examples show that our framework is effective and able to reconstruct visually pleasing objects. The final output finite element meshes can be utilized by CAE software. It should be noted that this study has examined only for auto-body design in the conceptual design stage. For the future work, we will improve current contour-extraction method from images, in order to obtain the feature information with higher precision. A more automatic registration algorithm to reconstruct new control boxes CB1 from images will be also considered. Furthermore, a more parametric framework will be constructed to generate panels or carbody self-adaptively to meet designer’s needs. More applications under this framework will be also considered.

Acknowledgement This work was funded by the Key Project of the NSFC (No. 10932003, u0935004), NSFC (No. 60873181), “863” Project of China (No. 2009AA04Z101), “973” National Basic Research Project of China (No. 2010CB832700) and the Fundamental Research Funds for the Central Universities. The model in Fig.10 is provided by DEP.

References [1] Gal, R., Sorkine, O., Mitra, N.J., Cohen-Or, D.: iWIRES: An Analyze-and-Edit Approach to Shape Manipulation. ACM Trans. Graph. 28(3), 1–10 (2009) [2] Sederberg, T.W., Parry, S.R.: Free-form Deformation of Solid Geometric Models. In: Proc. of ACM SIGGRAPH 1986, pp. 151–160. ACM, New York (1986)

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[3] Coquillart, S.: Extended Free-form Deformation: A Sculpturing Tool for 3D Geometric Modeling. In: Proc. of ACM SIGGRAPH 1990, pp. 187–196. ACM, New York (1990) [4] Hu, S., Zhang, H., Tai, C.: Direct manipulation of FFD: Efficient explicit solutions and decomposable multiple point constraints. Visual Computer 17(6), 370–379 (2001) [5] Ju, T., Schaefer, S., Warren, J.: Mean Value Coordinates for Closed Triangular Meshes. ACM Trans. Graph. 24(3), 561–566 (2005) [6] Joshi, P., Meyer, M., DeRose, T., Green, B., Sanocki, T.: Harmonic Coordinates for Character Articulation. ACM Trans. Graph. 26(3), #71 (2007) [7] Jiang, N., Tan, P., Cheong, L.F.: Symmetric Architecture Modeling with a Single Image. ACM Trans. Graph. 28(5), 1–8 (2009) [8] Tan, P., Zeng, G., Wang, J., Kang, S.B., Quan, L.: Image-based Tree Modleing. ACM Trans. Graph. 26(3), 87–93 (2007) [9] Lipman, Y., Levin, D., Cohen-Or, D.: Green Coordinates. ACM Trans. Graph. 27(3), 1–10 (2008) [10] Masuda, H., Ogawa, K.: Application of Interactive Deformation to Assembled Mesh Models for CAE Analysis. In: ASME Int. Design Engineering Technical Conferences (2007) [11] Masuda, H., Yoshioka, Y., Furukawa, Y.: Preserving Form Features in Interactive Mesh Deformation. Computer Aided Design 39(5), 361–368 (2007) [12] Chen, L.L., Wang, G.F., Hsiao, K.A.: Affective Product Shapes through Image Morphing. In: Proceedings of the International Conference on Designing Pleasurable Products and Interfaces, pp. 11–16. ACM, New York (2003) [13] Huang, Q., Li, B.J., Liu, M.Z., Bao, J.R.: Feature-preserved Morphing Method for Panel Design. Mathematical and Computer Modelling 51, 1417–1420 (2010) [14] Xu, C.Y., Jerry, L.P.: Snake, Shapes and Gradient Vector Flow. IEEE Trans. on Image Processing 7, 359–369 (1998) [15] Xu C.Y., Jerry L.P.: Gradient Vector Flow Deformable Models. In: Handbook of Medical Imaging, pp. 159–169 (2000) [16] Wang, R.H.: Numerical Approximation. Higher Education Press, Beijing (1999) [17] David, L.: Linear Algebra and Its Applications. Addison-Wesley, New York (2000)

Influences of Temperature of Vapour-Condenser and Pressure in the Vacuum Chamber on the Cooling Rate during Vacuum Cooling Tingxiang Jin*, Gailian Li, and Chunxia Hu School of Mechanical & Electricity engineering, Zhengzhou University of Light Industry, 5 Dong Feng Road, Zhengzhou 450002, Henan Province, P. R. China Tel.: +86-371-63556785 [email protected]

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Abstract. The temperature of vapour-condenser below 0 and the final pressure in the vacuum chamber below 0.61kPa during vacuum cooling were experimentally analysed in this paper. The temperature of vapour-condenser, -2 , -35 , -39 and -71 , and the final pressure in the vacuum chamber, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, were chosen. The experimental results showed that the cooling rate varies with the temperature of vapour-condenser and the final pressure in the vacuum chamber. Water vapour becomes the frost on the surface of vapour-condenser when the initial temperature of vapour-condenser is below 0 , which is helpful to trap water vapour for vapour-condenser. In addition, the formation mechanism of frost at the surface of vapour-condenser was analysed in this paper. The cooling time for vacuum cooling can be reduced when the final pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa. However, the surface temperature of cooked meat occurred freezing when the final pressure in the vacuum chamber was 0.3kPa. Therefore, in order to reduce the cooling time and avoid freezing, the temperature of vapour-condenser should be set around 30 ~-40 and the final pressure in the vacuum chamber can be defined at from 0.4kPa to 0.61kPa.

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Keywords: Temperature; Pressure; Vapour-condenser; Vacuum cooling.

1 Introduction Vacuum cooling is a rapid evaporative cooling method. Vacuum cooling has been successfully used to cool vegetables and flowers since the 1950s [1]. In the recent years, for the safety of foods, a rapid cooling treatment after cooking process should be used to minimize the growth of surviving organisms. Compared with the conventional cooling methods including air-blast, slow-air and water-immersion cooling, vacuum cooing has many advantages. Therefore, many researches have highlighted the applications of vacuum cooling for the cooked meats [2-4]. In addition, heat and mass transfer characteristics during vacuum cooling have been investigated [5]. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 41–52, 2011. © IFIP International Federation for Information Processing 2011

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Predictive models can provide much valuable information for the cooling process of large cooked meat joints under broad experimental conditions within a short time. Wang and Sun have developed a mathematical model for describing the vacuum cooling process of the large cooked meat joints [6-9]. Nomenclature

－cold load of vapour-condenser, W ； R －gas constant for water vapour, J ⋅ mol h －sublimation heat of ice, J ⋅ kg ； T －the Kelvin temperature, K ； Q0 v

−1

⋅ K −1

−1

；

vs

m − mass flux, kg ⋅ s −1 ;

－specific volume, m ⋅ kg ; －pressure, Pa ；－the diffusivity, m ⋅s ； Greeks ρ －density kg ⋅ m ； −1

3

v P D

2

−1

−3

λ − thermal conductivity, J ⋅ m −1 ⋅ K −1 ⋅ s −1 ；

Subscripts fr frost layer

－； v －vapour；

ice − ice layer;

A vacuum cooler is a machine to maintain the defined vacuum pressure in a sealed chamber, where the boiling of the water in the cooked meats occurs to produce the cooling effect. Theoretically, only the speed of vacuum pump is high enough to produce the defined vacuum pressure in the vacuum chamber. However, at a low pressure, the volume ratio of steam and water is very large. For example, when the pressure is 1073 Pa, the corresponding saturation temperature is 8 , the specific 3

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volume is 120.851 m kg . If the entire vapour is evacuated only through the vacuum pump, the speed of vacuum pump should be very large, many vacuum pumps are required in the vacuum cooler, which is obviously unsuitable. In order to remove the large amount of water vapour and keep the cooling cycle within a reasonable length of time, the vapour-condenser is used to economically and practically handle the large volume of water vapour by condensing the vapour back to water and then draining it through the drain valve. The vacuum pump and the vapour-condenser in the vacuum cooling system are used to remove the water vapour evaporated from the cooked meats. Wang and Sun [10] analysed the effect of operating conditions of a vacuum cooler on the cooling performance for large cooked meat joints by a validated mathematical model, they concluded that the temperature of the vapour-condenser should

Influences of Temperature of Vapour-Condenser and Pressure in the Vacuum Chamber

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be above 0 because water freezes on the outside surface of the condenser when the temperature is below 0 . It is well known that the boiling point changes as a function of saturation pressure, for a boiling temperature of 0 the saturation pressure will be 609 Pa. Therefore, in order to avoid freezing, the final pressure in the vacuum chamber is usually above 609 Pa. On the base of previous literatures, in the current study, vacuum cooling of cooked meats were conducted to analyze the effects of the final pressure in the vacuum chamber below 609 Pa and the temperature of vapour-condenser below 0 on the cooling rate of cooked meats.

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2 Materials and Methods 2.1 Samples Preparation The raw bone-out pork used in experiments was brought from a local supermarket. Then, the samples were cooked in water through the oven (Type of the oven is RFP130Y, China) until the samples were at a uniform temperature. Then, the cooked meat was put and cooled in the vacuum chamber. 2.2 Experimental Setup A laboratory-scale vacuum cooler as shown in Fig. 1 was built by Shanghai Pudong Freezing Dryer Instruments Co. Ltd. (Shanghai, China). Vacuum cooler has four basic components: a vacuum chamber, a vacuum pump, a vapour-condenser and a refrigeration system. The volume of vacuum chamber was approximately 0.3m3. The rotary vane vacuum pump (Type 2XZ-2) with the pumping speed of 7.2m3h-1 and rotary speed 1400 rev min-1 was used to evacuate the air in the vacuum chamber and the vapour evaporated from the products from atmospheric pressure to the defined vacuum pressure. The final vacuum pressure in the vacuum chamber is regulated by the bleeding valve. The vapour-condenser is an evaporator in the refrigeration system and a condenser capturing water vapour evaporated from the cooked meats during vacuum cooling. The cooling coil of vapour-condenser is set up in a stainless cylindrical steel, which is enclosed with 30 mm thickness polyurethane foam to prevent heat transfer. The stainless cylindrical steel with vapor-condenser is defined as cold trap. 2.3 Data Collection

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A set of T-type copper-constantan thermocouples with an accuracy of ± 0.1 are used to record the temperature distribution of cooked meats and the temperature of the cold trap. The pressure sensor (model CPCA-130Z), a capacitance membrane gauge with an accuracy of ± 1 Pa was used to measure the vacuum pressure in the chamber. The data collection and control signals, such as pressure and temperature were conducted by I-7000, a family of network data acquisition and control modules. The control module was connected with software called “King of Combination” (Beijing Asia Control Automatic Software Co. Ltd.). In order to eliminate the error of the second conversion, the temperatures received by the computer were demarcated by a second scale standard mercury thermometer with a measurement range 0~100 .

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1-bleeding valve; 2-weight sensor; 3-sample; 4-thermal couple; 5-pressure sensor; 6-vacuum chamber; 7-electronic balance; 8-compute; 9-temperature controller; 10-coolant outlet; 11coolant inlet; 12-cold trap; 13-vacuum pump; 14-pressure controller; 15-I-7018P module Fig. 1. Schematic diagram of the vacuum cooler system

3 Results and Discussion 3.1 The Effect of Temperature of Vapour-Condenser on Cooling Rates during Vacuum Cooling The vapour-condenser, which is an auxiliary vacuum pump, is normally used to remove the large amount of water vapour generated by condensing the vapour back to water and draining the water out of the vacuum chamber. The effect of temperature of vapour-condenser on cooling rates is shown in Fig. 2. It can be seen from Fig. 2 that the cooling rate of cooked meat can increase with the reduction of temperature of vapour-condenser. During vacuum cooling, the temperature of vapour-condenser of below 0 was used. However, if the temperature of vapour-condenser was too low, the cooling rate of cooked meat can decrease. If 0.15 kg of cooked meat was cooled, the average temperature of cooked meat can be reduced from 61.4 to 4.7 within 30 min at the vapour-condenser temperature of -2 . In addition, when the temperature of vapour-condenser was further reduce from -2 to -39 , the total cooling time can be obviously reduced from 30 min to 20 min. On the other hand, if the mass of cooked meat was 0.5 kg, the temperature of vapour-condenser was reduced continuously to –71 , the average temperature of cooked meat decreased only from 74 to 26.1 within 42 min. In the same mass of cooked meat, the average temperature of cooked meat can be reduced from 60.1 to 5.7 within 50 min at the vapour-condenser temperature of -35 . During vacuum cooling, the mass of cooked

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meat has also an effect on the cooling rate, which is accorded with Wang and Sun’s [10] experimental result. Wang and Sun [10] think that the temperature of vapourcondenser should be set at around 2.5 above 0 in order to avoid freezing of water on the outside surface of the cold trap. However, author thinks that the temperature of vapour-condenser should be set below 0 . Because when the temperature of vapour-condenser is below 0 , the water vapour become frost through solidify on the surface of the vapour-condenser, water vapour can be easily trapped in the vapourcondenser. It can be found that the different temperatures of vapour-condenser below 0 have an effect on the cooling rate of cooked meat. However, if the temperature of vapour-condenser is too low, the cooling time can be increase adversely, which can be expressed by the formation of the frost on the surface of cold trap.

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Fig. 2. Effect of temperature of vapour condenser on cooling rate during vacuum cooling

3.2 The Formation Mechanism of the Frost on the Surface of Vapour-Condenser Fig. 3 shows the formation process of frost. The sensible heat is transferred from the water vapour in the vapour-condenser to the frost surface by the temperature difference driving force between the water vapour and the frost surface. Some of the transferred moisture deposits on the frost layer, causing the frost layer to grow. The remainder diffuses into the frost layer. The heat of sublimation caused by the phase change of the added frost layer is transferred through the frost layer. The latent heat

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and sensible heat transferred from the water vapour are then transferred through the frost layer by conduction. The water vapour diffusing into the frost layer changes phase within the frost layer. The frost density increases as a result of this process. The frost layer is a porous medium composed of ice crystal and air. The ice crystal has different shapes during the formation of the frost layer. Ice crystal shapes are classified into main forms: plate-like forms and column-like forms. The microscopic structure of ice crystal is shown as in Fig.4. Sensible heat transfer Latent heat transfer Frost surface Heat transfer by conduction

Phase change

Frost layer

Water vapor diffusion

Vapor- condenser surface

Fig. 3. The formation process of the frost

Fig. 4. Ice crystal shape (1) Plate-like forms: (a) plate, (b) simple sectored plate, (c) dendritic sectored plate, (d) fern-like stellar dendrite; (2) Column-like forms: (e) needle crystal, (f) hollow column, or sheath-like crystal [11]

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During the formation of the frost, the mass flux through water vapour diffusing into the frost layer can be calculated by the Clapeyron-Clausius equation. The expression is as follows [12]:

m fr =

Q0 ⎡ ⎛ ρ f ⎞ 0.5 ⎤ λ fr RT (vv − vice ) ⎢1 + ⎜⎜ r ⎟⎟ ⎥ ⎢⎣ ⎝ ρ ice ⎠ ⎥⎦ hvs + ⎛ ρf ⎞ Dv [hvs − Pv (vv − vice )]⎜⎜1 − r ⎟⎟ ⎝ ρ ice ⎠ 2 fr

Where

(1)

Q0 is the refrigeration load of vapour-condenser;

hvs is the sublimation heat of ice; R is the gas constant; T fr is the surface temperature of the frost layer; vv and vice are respectively specific volume of water vapour and ice;

ρf r

and

ρ ice

are respectively density of frost layer and ice;

Pv is the partial pressure of water vapour; Dv is the diffusivity of water vapour;

λ fr

is the thermal conductivity of the frost layer, the expression is as follows [13]:

λ fr = 0.02422 + 7.214 × 10 −4 ρ fr + 1.1797 ×10 −6 ρ fr 2

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(2)

The temperature of vapour-condenser is below 0 , The water vapour evaporated from the cooked meats will become the frost at the surface of vapour-condenser. Fig. 5 shows that the comparison of the cold trap between before and after vacuum cooling of cooked meat. It can be obviously found that the frost occurs at the surface of cold trap after vacuum cooling of cooked meat. The latent heat of sublimation is released in the cold trap, which can increase the temperature of vapour-condenser. The variation of temperature of vapour-condenser is shown in Fig. 6. It can be seen from Figs. 2 and 6 that when the initial temperatures of cold trap were at -2 , -39 and -35 , respectively, the temperatures of cooked meat were reduced to about 5 , the temperature of vapour-condenser was reduced from -2 , -39 and -35 to –70.4 , -70.4 and –71.9 , respectively during vacuum cooling. It can be found that the temperature of vapour-condenser can be reduced continuously during vacuum cooling. Because the condensation ability of vapourcondenser is not smaller than the required one, the condenser can efficiently condense all the generated water vapour during the cooling. However, when the initial temperature of cold trap reached at -71 , the temperature of cold trap had no variation during

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a. Cold trap before vacuum cooling of cooked meat

b. Cold trap after vacuum cooling of cooked meat Fig. 5. Comparison the cold trap before vacuum cooling of cooked meat with after vacuum cooling of cooked meat

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Fig. 6. The variation of temperature of vapour condenser in different experimental conditions

Fig. 7. Effect of the pressure in the vacuum chamber on the surface temperature of cooked meat during vacuum cooling

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vacuum cooling, which can be seen from Fig. 6. At the same time, the temperature of cooked meat reduced only from 74 to 26.1 within 42 min, which is because the frost and ice at the surface of cold trap result in a significant heat resistance.

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3.3 The Effect of the Pressure in the Vacuum Chamber on the Cooling Rate during Vacuum Cooling Fig. 7 gives the effect of the pressure in the vacuum chamber on the surface temperature of cooked meat during vacuum cooling. Four different final pressure, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, in the vacuum chamber were chosen during vacuum cooling. It can be seen from Fig. 7 that if the final vacuum pressure in the vacuum chamber is 0.61kPa, the surface temperature of cooked meat can be reduced from 50 to 5.2 within 40 min. When the final vacuum pressure in the vacuum chamber was reduced from 0.61kPa to 0.5kPa, the surface temperature of cooked meat can be reduced from 43.5 to 4 within 31 min. The final vacuum pressure in the vacuum chamber was kept at 0.4kPa, the surface temperature of cooked meat varied from 51.2 to 5.6 within 18 min. In the same case, the mass of cooked meat is 0.3kg, the final vacuum pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa, the cooling time will increase from 18 min to 40 min. If the mass of cooked meat increased from 0.3kg to 0.6kg, the final vacuum pressure in the vacuum chamber is further reduced from 0.4kPa to 0.3kPa, the surface temperature of cooked meat can be reduced from 41.6 to 2.7 within 25 min. On the other hand, during vacuum cooling, it can be found when the final vacuum pressure in the vacuum chamber was 0.3kPa, the minimum surface temperature of cooked meat was –0.5 , which shows that water freezes on the surface of cooked meat and has a negative effect on the cooked meat. It is well known that the boiling point changes as a function of saturation pressure, for a boiling temperature of 0 the saturation pressure will be 609Pa. However, the experimental results show that the surface temperature of cooked meat is above 0 , when the vacuum pressure in the vacuum chamber is between 0.4kPa and 0.6kPa. If the vacuum pressure in the vacuum chamber is further reduced to 0.3kPa, the surface temperature is below 0 . This means that the vacuum pressure in the vacuum chamber can be reduced to below 0.6kPa. At the same time, it should be noted that the vacuum pressure in the vacuum chamber should be above 0.4kPa.

℃

℃

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℃

℃ ℃

℃

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℃,

℃

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4 Conclusion

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The temperature of vapour-condenser below 0 and the final pressure in the vacuum chamber below 0.61kPa during vacuum cooling were experimentally analysed. The temperature of vapour-condenser, -2 , -35 , -39 and -71 , and the final pressure in the vacuum chamber, 0.3kPa, 0.4kPa, 0.5kPa and 0.61kPa, were chosen during vacuum cooling. The experimental results showed that water vapour becomes the frost on the surface of vapour-condenser when the initial temperature of vapourcondenser is below 0 , which is helpful to trap water vapour for vapour-condenser. However, if the temperature of vapour-condenser is reduced continuously to -71 ,

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Influences of Temperature of Vapour-Condenser and Pressure in the Vacuum Chamber

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the cooling rate will not increase. Therefore, the temperature of vapour-condenser should be set around -30 ~-40 . At the same time, it can be also found that the cooling time for vacuum cooling can be reduced when the final pressure in the vacuum chamber varied from 0.4kPa to 0.61kPa. In addition, the surface temperature of cooked meat was above 0 . However, the surface temperature of cooked meat occurred freezing when the final pressure in the vacuum chamber was 0.3kPa. It can be suggested that the final pressure in the vacuum chamber can be set between 0.4kPa and 0.61kPa. In a word, it is feasible that the temperature of vapour-condenser below 0 and the final pressure in the vacuum chamber below 0.61kPa. In order to reduce the cooling time, the temperature of vapour-condenser should be set around -30 ~40 and the final pressure in the vacuum chamber can be defined at from 0.4kPa to 0.61kPa.

℃ ℃ ℃

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Acknowledgements Funding for this research was provided by Henan Provincial Department of Education (P. R. China).

References 1. Briley, G.C.: Vacuum cooling of vegetables and flowers. ASHRAE Journal 46(4), 52–53 (2004) 2. McDonald, K., Sun, D.-W., Kenny, T.: The effect of injection level on the quality of a rapid vacuum cooled cooked beef product. Journal of Food Engineering 47, 139–147 (2001) 3. Burfoot, D., Self, K.P., Hudson, W.R., Wilkins, T.J., James, S.J.: Effect of cooking and cooling method on the processing times, mass losses and bacterial condition of large meat joints. International Journal of Food Science and Technology 25, 657–667 (1990) 4. Desmond, E.M., Kenny, T.A., Ward, P., Sun, D.-W.: Effect of rapid and conventional cooling methods on the quality of cooked ham joints. Meat Science 56, 271–277 (2000) 5. Sun, D.-W., Wang, L.J.: Heat transfer characteristics of cooked meats using different cooling methods. International Journal of Refrigeration 23, 508–516 (2000) 6. Wang, L., Sun, D.-W.: Modelling vacuum cooling process of cooked meat—part 1: analysis of vacuum cooling system. International Journal of Refrigeration 25, 854–861 (2002) 7. Wang, L., Sun, D.-W.: Modelling vacuum cooling process of cooked meat—part 2: mass and heat transfer of cooked meat under vacuum pressure. International Journal of Refrigeration 25, 862–871 (2002) 8. Sun, D.-W., Hu, Z.: CFD predicting the effects of various parameters on core temperature and weight loss profiles of cooked meat during vacuum cooling. Computers and Electronics in Agriculture 34, 111–127 (2002) 9. Sun, D.-W., Hu, Z.: CFD simulation of coupled heat and mass transfer through porous foods during vacuum cooling process. International Journal of Refrigeration 26, 19–27 (2003)

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10. Wang, L., Sun, D.-W.: Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints. Journal of Food Engineering 61, 231–234 (2004) 11. Na, B., Webb, R.L.: New model for frost growth rate. International Journal of Heat and Mass Transfer 47, 925–936 (2004) 12. Kondepudi, S.N., O’Neal, D.L.: Performance of finned tube heat exchangers under frosting conditions. International Journal of Refrigeration 16(3), 175–180 (1993) 13. Yonko, J.D., Sepsy, C.F.: An investigation of the thermal conductivity of frost while forming on a flat horizontal plate. ASHRAE Trans. 73(2), 111–117 (1967)

Inspection of Lettuce Water Stress Based on Multi-sensor Information Fusion Technology Hongyan Gao, Hanping Mao, and Xiaodong Zhang Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education & Jiangsu Province, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. Characteristics of reflection spectrum, multi-spectral images and temperature of lettuce canopy were gained to judge the lettuce’s water stress condition which could lead to a precise, rapid & stable test of lettuce moisture and enlarged the models’ universality. By the extraction of lettuce’s multi-sensor characteristics in 4 different levels, quantitative analysis model of spectrum including 4 characteristic wavelengths, characteristic model of multispectral image and CWSI were established. These multi-sensor characteristics were fused by using the BP artificial neural network. Based on the fused multisensor characteristics, the lettuce moisture evaluation model was established. The results showed that the correlation coefficient of multi-spectral images model, spectral characteristics model and information fusion model were in turn increased, the correlation coefficients were respectively 0.8042 0.8547 and 0.9337. It was feasible to diagnose lettuce water content by using multi-sensor information fusion of reflectance spectroscopy, multi-spectral images and canopy temperature. The correct rate and robustness of the discriminating model from multi-sensor information fusion were better than those of the model from the single-sensor information.

、

Keywords: Lettuce, Water stress, Information fusion.

1 Introduction Leaf stomata conductance, leaf water potential, transpiration rate, plant stem diameter changes and soil moisture, etc. indirectly illustrated the crop water stress and water requirement. Or the plant water stress is determined using dry weight method by collecting in vivo samples. The traditional testing methods have the shortages of low precision and affecting crop growth ,and it is not conducive to the promotion application, because in sampling and data analysis are time-consuming. Modern diagnostic methods are mainly spectroscopy, visual images and canopy temperature. However, canopy reflectance spectrum and images have interactions as nitrogen, water and leaf area index, spectrum and image are affected by the crop canopy structure, environmental factors and others. Therefore, a single detection method can not accurately and comprehensively explain the water stress. The discussion group proposes inspection of lettuce water stress based on the multi-sensor information fusion technology. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 53–60, 2011. © IFIP International Federation for Information Processing 2011

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Multi-sensor information fusion might comprehensively process multi-source information which comes from some different sensors, so it can obtain more accurate and reliable conclusions [1]. Multi-sensor information fusion can greatly avoid the limitations of a single sensor and improves the performance of the system [2]. Because the sensors provide some uncertain information, multi-sensor information fusion technology is essentially a non-deterministic reasoning and decision-making process [3]. Multi-sensor information fusion can be divided into three different layers which were the decision layer fusion, feature layer fusion and the raw data layer fusion [4]. The research is the optimization and combination of spectra, images, and canopy temperature, because the spectrum analyzer, machine vision systems and other equipments are abstracted into different types of sensors, the features are different and collect different physical quantities, the information pattern and span are comparatively large, environment and objectives with time-varying features, crop characteristics are complex, so it does not suitable for the decision layer fusion and the raw data layer fusion. More practical option is feature layer fusion, the fusion not only retain a sufficient number of original information, but also achieve a level of data compression, contributes to real-time processing[5].The concept of feature layer fusion is different features gather to form the new summary feature set, and then making decisions accordingly[6]. The research obtains lettuce canopy spectrum, image information, canopy temperature and environmental temperature and humidity, etc. Then establish spectra model, mage model and the water stress index model. Ultimately, used BP neural network training samples and verification, water stress conditions on the lettuce is rapidly and non-destructively inspected.

2 Experimental Design and Sample Training 2.1 Instrument and Equipment Spectrum measuring equipment is the United States ASD FieldSpec®3 handheld portable spectrum analyzer, the range 350~2500nm; at 350~1000nm, sampling interval is 1.4nm, resolution is 3nm; in 1000 ~ 2500nm, sampling interval is 2nm, resolution is 10nm. High-precision analytical balance weighs the quality of whole lettuce, accuracy is 0.1mg. Canopy multi-spectral image utilizes MS-3100 multi-spectral digital progressive scan camera, imaging spectral range is 400~1100nm, resolution is 1039×1392. Canopy temperature utilizes the TI50 infrared thermal imaging instrument, the range is -20~305 , accuracy is 0.07 .

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2.2 Samples Training Experiment started in Jiangsu university modern agricultural equipment and technology Venlo-type greenhouse. The variety is Italian anti-bolting lettuce. According to the Yamazaki Nutrient solution, the samples were divided into four levels, each level had 12 lettuce, so the predict set had 24 samples, calibration set had 20 samples. Four levels were: Group 1(W1) ensure adequate water were supplied throughout the

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growing season; Group 2 (W2), 3 (W3), 4 (W4) irrigated the standard formula of 75% 50%, 25% concentrate. 2.3 Experiment Design Lettuce samples (four levels) Spectral model

Multi-spectral image model

CWSI

Feature level fusion of based on BP neural network

Diagnostic assessment model of lettuce water Fig. 1. The flow chart of lettuce water stress inspection based on multi-sensor information fusion technology

3 Results and Analysis 3.1 The Quantitative Analysis of Lettuce Moisture Content Based on Spectrum Technology Fig. 2 showed the lettuce canopy reflectance spectra in the different water stress. Combined with previous studies of discussion group, and referenced to the USDA researchers came to the main biochemistry components of the spectral absorption characteristics [7], wavelength sensitive of water-related mostly concentrated in the near infrared band. As Fig. 2 shown, spectral reflectance of lettuce had significant difference in different water stress at the water sensitive bands. 0.7

Spectral reflectance

0.6 0.5 0.4 0.3 0.2 0.1 0 350

850

1350

1850

2350

Wavelength/nm

Fig. 2. Lettuce canopy reflectance spectra under different water stress

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In order to eliminate offset and drift which caused the spectral bias, highlight the hidden information and identification of samples. The first order derivative was carried out on the spectrum and conducived to extract characteristic wavelength. In first, all spectral points were divided into 4-sensitive bands: 1220-1300nm ,14101490nm, 1620-1700nm ,1900-1970nm, then removed the wavelength by stepwise regression[8] and got the sensitive wavelength of related to the lettuce canopy water stress: 1267nm 1443nm 1661nm 1921nm. So as to eliminate the impact of multicollinearity, so the four wavelengths for partial least squares regression analysis[9], When two principal component score were extracted, cumulative contribution rate greater than 0.85. Ultimately obtained PLS regression model based on four sensitive wavelengths (Xi):

、

、

、

y = 4670.62 − 3786.43X1 − 2994.85X 2 − 2990.34X 3 + 943.02X 4

(1)

Then 20 samples of spectral data tested the model, the correlation coefficient between dry water content of lettuce canopy measured and predictive value was 0.8547.

Fig. 3. Lettuce canopy reflectance spectra under different water stress

3.2 The Quantitative Analysis of Lettuce Moisture Content Based on Multi-spectral Imaging Technology The six channels lettuce canopy images were simultaneously acquired by the MS3100 multi-spectral digital progressive scan camera, they were R G B RGB IR and CIR. This method not only contributed to extract image features of all the independent channels, but also easily achieved multi-spectral image pixel-level operation and integration(Image registration was not required). Image processing based on MATLAB software. Median filtering method of 3 × 3 window would eliminated isolated noise points, reduced the image blurring, so it was used for image preprocessing. And two-dimensional maximum entropy segmentation was used for background segmentation; this method preserved more image information of crop canopy and contributed to the image feature extraction. Experiment used gray feature extraction method, finally AIR810 and AIR940 (AIRk is near infrared

、、、

、

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spectroscopy 810nm and 940nm canopy image mean gray value) significantly correlated to lettuce canopy water content. Establishing lettuce canopy water content forecast model by SPSS 13.0 for multiple linear regression analysis, including AIR810 and AIR940 image features variable: y = 24 .764 + 124 .729 AIR 940 + 63 .775 AIR 810

(2)

Then 20 samples of spectral data tested the model, the correlation coefficient between dry water content of lettuce canopy measured and predictive value was 0.8042. 3.3 Canopy Water Stress Index (CWSI) Model Establishment

The TI50 infrared thermal imaging instrument obtained lettuce canopy temperature and real-timely monitored environmental temperature and humidity. According to the CWSI empirical model by Idso in the literature [10], as follows:

CWSI =

(Tc − Ta ) − (Tc − Ta )Π (Tc − Ta )ul − (Tc − Ta )Π

(3)

(Tc − Ta )Π = A + B × VPD V P D = 0 .6 1 1 × e

1 7 .2 7 × T a T a + 2 3 7 .3

(4)

× (1 −

(5)

RH ) 100

(Tc − Ta ) ul = A + B × V PG

(6)

In the formula: Tc-the crop canopy temperature, °C; Ta-air temperature, °C; (Tc-Ta)Π-lower limit of the difference temperature between canopy and air, °C; (Tc-Ta)ul-limit of the difference temperature between canopy and air, °C; VPD-air vapor pressure deficit, hPa. A, B-experience factor; VPG-the difference of air saturated vapor pressure between when the air temperature were Ta and Ta+ A, hPa. y = -1.4647x + 4.8287 R2 = 0.8969

4 3 2

C 1 ° 0 / a 1 T -1 c T -2 -3

2

3

4

5

-4 -5

VPD/Kpa Fig. 4. The relationship of (Tc-Ta) and VDP

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The model of difference temperature between canopy and air by data analysis:

T c − T a = 4 . 8287 − 1 . 4647 VPD

(7)

In the condition of full water supply, when VPD was 5.86, the difference temperature between canopy and air was minimum, it regarded as lower limit of CWSI. So (Tc-Ta)Π was -3.7544. When lettuce was severe water stress, canopy temperature reached the maximum during the experiment, it regarded as upper limit. So (Tc-Ta)ul was 1.3241( ). Lettuce CWSI model:

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CWSI =

(8)

Tc - Ta + 3 .7544 5 .0785

3.4 The Model Based on BP Neural Network Information Fusion

BP neural network has strong fault tolerance, distributed, storage, self-learning, adaptive, self-organization, nonlinear dynamic capabilities and handle complex environments[11].Therefore, using better self-learning and adaptive capacity, lettuce water stress condition was evaluated by BP neural network. Table 1. Samples predicted values and the measured values

Sample Number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Average

Predictive Value (%) 2984.269 2712.595 2440.624 2402.126 2290.337 1934.597 1852.465 1839.795 1791.997 1780.012 1710.706 1661.028 1690.231 1577.302 1603.096 1687.403 1575.115 1436.798 1206.933 1081.009

Measured Value (%) 2812.366 2508.462 2604.845 2598.000 2499.381 2161.101 2093.596 1721.290 1623.837 1968.000 1603.889 1798.400 1864.688 1704.605 1428.125 1490.943 1449.184 1625.636 1415.647 1174.524

Relatively Error (%) 6.112 8.138 6.304 7.539 8.364 10.481 11.518 6.885 10.356 9.552 6.660 7.639 9.356 7.468 12.252 13.177 8.690 11.616 14.743 7.962 9.240

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The research used a 3-layer structure of BP neural network for feature level data fusion. There were lettuce canopy reflectance spectra characteristic wavelength 1267nm, 1443nm, 1661nm, 1921nm and the image feature parameter AIR810, AIR940, CWSI as the input, dry basis moisture content measured as the output. Error index and the training step were respectively sited to 0.001 and 0.05, hidden nodes was 10. Using BP neural network to predict the same test set (Table 1). The average relative error of predicted and measured values was 9.24%, correlation coefficient R was 0.9337.

4 Conclusion Object of study choose four different moisture content of lettuce, lettuce canopy water stress was evaluated by spectral characteristics, image feature information, canopy temperature and Environmental temperature and humidity. Spectral analysis model and the image model were established, then verifying the model, the correlation coefficient between water content of lettuce canopy measured and predictive value were 0.8547 and 0.8042. The results showed that lettuce canopy water stress evaluation method based on the spectrum, multi-spectral image and the CWSI of multi-sensor information fusion technology was feasible, and the correlation coefficient was 0.9337. Model of accuracy and stability were higher than a single information model. Results of the research for multi-sensor information fusion technology could regarded as reference to the rapid and accurate detection of lettuce water.

Acknowledgements This work was supported by a grant from the National High Technology Research and Development Program of China (863 Program)(No.2008AA10Z204 and 2008AA102208), "333 Talent Project" in Jiangsu Province.

References 1. He, Y., Wang, G., Lu, D., Peng, N.: Multi-sensor data fusion and applications. Electronic Industry Press, Beijing (2007) 2. Xiao, W., Li, X., Li, P., Feng, Y., Wang, W., Zhang, J.: Near infrared spectroscopy and machine vision information fusion soil moisture detection. Transactions of the CSAE 25, 14–17 (2009) 3. Ma, G., Zhao, L., Li, P.: Based on Dempster Shafer evidence of multi-sensor information fusion technology and its application. J. Modern Electronic Technology 19, 41–44 (2009) 4. Yan, H., Huang, X., Wang, M.: Multi-sensor data fusion technology and its application. J. Sensor Technology 24, 1–4 (2005) 5. Chen, Q., Zhao, J., Cai, J.: Based on near infrared spectroscopy and machine vision information fusion technology of multi-judge the quality of tea. J. Transactions of the CSAE 24, 5–10 (2008) 6. Wang, R.: Information fusion. Science Press (2007)

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7. Pu, R., Gong, P.: Hyper spectral remote sensing and its application. Higher Education Press, Beijing (2000) 8. Zheng, Y., Zhang, J., Chen, X., Shen, X., Zhang, T.: Based on stepwise regression of nearinfrared spectral information extraction and model. J. Spectroscopy and Spectral Analysis 24, 675–678 (2004) 9. Wang, H.: Partial least squares regression method and its application. National Defense Industry Press, Beijing (1999) 10. Cui, X., Xu, L., Yaun, G., Wang, W., Luo, Y.: Based on the temperature of the summer maize canopy water stress index model study. J. Transactions of the CSAE 21, 22–24 (2005) 11. Xiong, Y., Wen, Z., Wang, M.y.: Based on neural network spectral recognition system design and analysis. J. Spectroscopy and Spectral Analysis 27, 139–142 (2007)

Measurement of Chili Pepper Plants Size Based on Mathematical Morphology Yun Gao, Xiaoyu Li, Kun Qi, and Hong Chen College of Engineering, Huazhong Agricultural University Wuhan, China [email protected]

Abstract. Since chili pepper plant size directly reflects the state of plant growth, a method for pepper measurement of plants size was discussed here. Pepper plants were shot from above once per week in the greenhouse since being field planted in spring. The method of processing the pepper plant images was studied, in which the image segmentation of combination of color space and the image morphological operations were applied. And the major axis and minor axis of pepper plant, for describing the size of the plant, were calculated from single connected component in the image being processed. According to the method, a program for pepper plant size measurement based on MATLAB was developed. Experimental results have demonstrated that the method is more reasonable and accurate than artificial measure. Keywords: pepper plant; size measurement; segmentation; morphological operation; major axis and minor axis.

1 Introduction Chili pepper, which plays an important role in the year-round vegetable supply in china, is an important commercial-orientated crop in the country[1,2]. During the cultivation of chili pepper plants, the growth state and morphological directly influences the suitability of a plant for cultivation, its overall yield and its economic coefficient[3]. The time of each growth phase, the number of leaves, weight of fresh leaf, leaf area, thickness of leaf, size of leaf, and so on are used to describe the growth state. However, the size of plants, as the intuitive and important factor to describe the growth stage and growth state, has less been studied, because of the difficult measurement. As comparing the differences in size between the same capsicum species does help research on capsicum cultivation techniques and improve the yield and quality of pepper. In this work, we developed a method to detect the size of capsicum plants using computer vision technology. The chili pepper plants were photographed in the greenhouse for the size measurement method developed. An algorithm, using image segmentation method to separate pepper plant from the background, and image binarization method to make the image black and white, after that, the morphology method was utilized to make single frame pepper plant image into a single connected graph. Finally, the longest diameter D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 61–70, 2011. © IFIP International Federation for Information Processing 2011

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and the shortest diameter, as plant morphology parameters, of the single connected graph, were introduced. Experiments verified that the algorithm was effective, with comparing measurement data with the tape to data calculated by the algorithm.

2 Image Acquisition 50 chili pepper plants, which were planted in the spring in the greenhouse of Hubei Academy of Agricultural Science and Technology, photos have been taken for study, by using single μ300 Olympus digital camera and a tripod metal photographic PTZ. Chili pepper seedlings were transplanted from the seedbed to the greenhouses, as planting spacing of 40cm and seedling spacing of 45cm. One week later, chili pepper plants were photographed once a week for seven weeks. 150 pictures were collected each time, and three pictures were taken from one chili pepper plant. In the photo collection, the camera was placed on the tripod metal photographic PTZ, just perpendicular to the plant and shot the plant from above, as shown in Figure 1, in which H is the distance from the camera lens to the ground. Between three times shooting, the camera was rotated 120 degrees in the horizontal direction. The image resolution is 1024×768, each image is saved as a JPEG file. To improve the adaptability of the analysis method, the shooting was not under extra lighting but natural light. The first three weeks after the beginning of image acquisition, each image contained only one plant. From the fourth week, chili pepper plants grew staggered, and not suitable for image acquisition. So the study object in this paper is the images acquired from the shooting of the first three weeks. Fig. 1 shows how the pictures have been taken, in which H is the vertical distance from the actual shooting of the camera lens to the ground pepper cultivation.

，

Fig. 1. Sketch map for shooting method

3 Image Segmentation 3.1 RGB Color Image Segmentation To detect the size of chili pepper plants, pepper images need to be segmented from background. From pepper picture in Fig. 2, we can see the background of pepper

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plants mainly compos of the soil covered with plastic film, the black section on the upper right corner of the image is irrigation tube road under the plastic film. As the main color of pepper plants is green, some parts of the leaves and petioles are yellow and the color is very close to the background color of the soil and the film, which makes the image segmentation very difficult. At present, there are two main methods to use the color characteristics for the color image segmentation: one is changes the two-dimensional color images into grayscale images, and grayscale threshold segmentation algorithm is used for gray image segmentation; another is based on color segmentation, and in the color space it directly limits each RGB value of the color space and separates the chili pepper plant and the background[4,5].

Fig. 2. Image of chili pepper plant

Major color spaces, used in color region segmentation today, are RGB color space and HIS color space. The images shown in Fig. 1, is segmented for the Euclidean distance[6]. The Euclidean distance between z and m is given by D (z, m ) = z − m = [(z − m)T (z − m )]

1

2

= [( z R − mR ) 2 + ( zG − mG ) 2 + ( z B − mB ) 2 ]

1

2

(1)

Wherein m stands for the RGB column vector of average color from the region of chili pepper plant to be segmented and z stands for an arbitrary point in RGB space.

⋅ is the norm of the argument, and subscripts R,G and B, stands for the RGB values of vectors m and z. Figs.3 (a)through (d) show the segmentation results with T =25, 45 ,60 and m = [96.0202 126.0374 45.5014]'. Here m is a vector of mean RGB values in the plant region. In Figs.3 (a)through (d) show when T is too small , the deterioration of plant appears in (a). when T is too large, the background cannot be segmented well from the image . To directly set the threshold of RGB values with R(i,l)<=140&R(i,l)>= 59&B(i,l)<=105&B(i,l)>=3&G(i,l)<=195&G(i,l)>=92 can not have an well segmentation result, which shows in Fig. 4. In the study we found the yellow soil could be segmented well in HIS color space by using the threshold algorithm, but the plastic film and irrigation tube road couldn’t. The image processing result was shown in Figure 5 with H (i, l) <= 1 & H (i, l)> 0.15 & I (i, l) <0.55.

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(a) T=25

(b) T=45

(c) T=60

Fig. 3. Segmentation with T=25,45,60, respectively

Fig. 4. Segmentation in RGB color space

Fig. 5. Segmentation in HIS color space

When we used the Euclidean distance method in RGB color space on Fig.5 for segmentation by using the option 'euclidean' with T = 50, and available results was shown in Fig. 6. From the figure, the plant’s incompleteness is reduced and the background is almost removed.

Fig. 6. Segmentation using the threshold algorithm in HIS color space and the option ’euclidean’ with T=50

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3.2 Combination of Color Space Image Segmentation As in our previous studies, we found it is difficult to achieve good results for segmenting pepper images in only one color space. So the algorithm combining the segmentation in RGB color space and the segmentation in HIS color space was used here. In two spaces setting threshold of each color vector for several tests we have H (i, l) <= 1 & H (i, l)> 0.15 & I (i, l) <0.55 & R (i, l) <= 0.543 & R (i, l)> = 0.231 & B ( i, l) <= 0.333 & B (i, l)> = 0 & G (i, l) <= 0.647 & G (i, l)> = 0.331, and segmentation of Fig.2 is shows in Fig.7.

Fig. 7. Segmentation in combination of color space

Comparing with Fig.6, the background elimination of Fig.7 is similar, but the pepper plant in fig.7 in the division breakage is much better than that in Fig.6. Concerning the operation time of two methods, the preceding algorithm's operation time is 6.2970s and the latter algorithm's operation time is 4.5160s. Compared to the preceding kind, the latter one saves time at approximately 28%. If we proceed to process the image in fig.7 using the wiener filter before it is segmented, it would reduce the division breakage of the plant, but the operation time would up to 8.5630s, the result image is shown in Fig.8.

Fig. 8. Segmentation in combination of color space with wiener filter

4 Image Morphological Operations 4.1 Opening and Closing

，

After being operated by combination color space image segmentation the image has some tiny noises in the background, and division breakages in plant’s leaf surface and stems. We use morphological opening to remove the small noise in background. The

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morphological opening of A by B, denoted A D B , is simply erosion of A by B, followed by dilation of the result by B[6]:

A D B = ( AΘΒ) ⊕ Β

(2)

The operation produced the result in Fig.9. The morphological opening algorithm can remove the thin protrusions and outward-pointing boundary irregularities. Here, the small isolated object in background were removed.

Fig. 9. Result of morphological opening

Morphological opening can fill the division breakage of the plant after being segmented. The morphological closing of A by B, denoted A • B , is a dilation followed by an erosion[6]:

A • B = ( A ⊕ B )ΘB

(3)

Like opening, morphological closing tends to smooth the contours of objects. Unlike opening , however, it generally joins narrow breaks, fills long thin gulfs, and fills holes smaller than the structuring element. The following are results by using the structuring elements of 5*5 and 10*10 for closing operation on the image, which are shown in Fig. 10 (a) and (b).

(a) Using the structuring elements of 5*5

(b) Using the structuring elements of 10*10

Fig. 10. Results of closing with structuring elements of 5*5 and 10*10, respectively

4.2 One Connected Component To calculate the size of chili plants, the plant in Fig.10 needs to be transferred to a single connected component. In order to prevent chili plants becoming multiple connected components in one image, we applied labeling to process the image[7]. When

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there are more than one connected component in the image, morphological closing is repeatedly used to operate the image to fill the area between the breaking parts until the number of the connected component in the image becomes one. Comparison of results before and after operation are shown in Fig.11.

(a)original image

(b) Image using segmentation and morphological algorithm

(c) One connected component

Fig. 11. One connected component processing

In Fig.11(a), the plant is segmented excessively and one under leaf separates from the main plant. When marking the image, there are two connected components. After being operated morphological closing twice, the image is shown in Fig.11(b), where the under leaf is connected to the main plant and the number of the connected components become one. 4.3 Multiple Objects Processing When Chili pepper plants grow larger, the distance between each other becomes closer. In the third week of shooting, one image may not only have the main plant, but also have the other part of plant near to the main plant, which makes calculation of the plant size difficult. Therefore removing the object which contacts with the boundary of the image can make the connected components become one, as shown in Fig.12.

(a) original image

(b)image using segmentation and morphological algorithm

(c) removing the other plants

Fig. 12. Multiple objecst processing

In fig.12, we used the function “imclearborder” to eliminate the leaves from other plant near to the main plant which connect to image boundary, and reserved single connected phenomenon of the main plant[6].

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5 Detect the Size of the Pepper Plants 5.1 Major Axis and Minor Axis Diameter, major axis and minor axis are chili pepper plant size’s simplest descriptors. The diameter of a boundary is defined as the Euclidean distance between the two farthest points on the boundary[8]. Its formula is

Diam( A) = max[ D( pi , p j )]

(4)

i, j

Wherein A stands for the region of one connected component of plant, pi , p j stand for any two pixels in the plant region. As the diameter is a useful descriptor, the major axis is defined as the line segment connecting a single pair of farthest points on the boundary of the plant. And the minor axis is defined as the line perpendicular to the major axis. We use the lengths of major axis and minor axis as the descriptors for chili pepper plant size. When the image only includes a single connected component of the main chili pepper plant, function ‘MajorAxis’ and ‘MinorAxis’ are used to carry out the lengths of major axis and minor axis, According to the distance of shooting, the correction coefficient was used to make the lengths more precise. The equation is as follows: (5) Lr = a × Lc Wherein a stands for the correction coefficient. 5.2 Program of Measurement We developed programs to process images and calculate the sizes of chili pepper plants by using MATLAB’s Graphical User Interface Development Environment. The surface of the software is shown in Fig.13 (a) and (b).

(a) Before processing

Fig. 13. Surface of the software

(b) After processing

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Fig.13 (a) is the surface before the plant image being processed, and fig.13(b) is the surface after the image being processed. 5.3 Results Comparison

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The sizes of chili pepper plants measured in field compare to image measurement results. Manual measurement was conducted, including judging the direction of the major axis of the plant from above, using tape measure acquire the length of major axis along the direction and acquiring the length of minor axis along the direction perpendicular to the major axis.

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For example, 50 image acquired at the second week after transplant. Comparation of the results from manual measurement and the values from image measurements shows in Fig.14. And in Fig.14, (a)and(b) show two groups of major axis and minor axis values acquired through manual measurement and image measurement. Green squares stand for manual measurements, while red circles stand for image measurements. From the figure, comparing the image measurements with the manual measurements, we can find the maximum error of 50 pairs of values is less than 1.3cm. In major axis values there are 35 pairs of points, 70% of all pairs of points, which have the error less than 0.7cm. Meanwhile in minor axis values there are 32 pairs of points have the error less than 0.7cm, a rate of 64%. The main reason for errors here is human vision. The direction of major axis in manual measurements couldn’t be determined accurately. The other reason is because of precision of the tap measurement can only be up to 0.5cm. Meanwhile even the smallest wind would make the plant shaking during the field measures. In image measurements the difference between three measurements and their average is less than 0.5cm. Therefore, image measurement is more scientific and more accurate.

6 Conclusion A method for chili pepper plants size measurement has been developed. Images were segmented in combination of RGB color space and HIS color space. And then

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the image morphological operations were applied to make the plant in one image so as to form a single connected component. And the major axis and minor axis of pepper plant, to describe the size of the plant, were calculated from single connected component in the image processed. According to the method, a program for pepper plant size measurement based on MATLAB was developed. Experimental results have demonstrated that the method is more reasonable and accurately than manual measure.

References 1. Reihardt, D., Kuhlemerier, C.: Plant architecture. EMBO Reports 3, 846–851 (2002) 2. Zou, X.: Cultivation Seasons and Cropping patterns of capsicum in china. Journal of China Capsicum 3, 32–35 (2002) 3. Gao, G., Pen, G.Z., Zhao, Z.: Development on the Expert System for Cultivation and Management of the Fresh Edible Capsicum in Guizhou. Guizhou Agricultural Sciences 5, 34–37 (2007) 4. Lin, K., Wu, J., Xu, L.: A Survey on Color Image Segmentation Techn iques. Journal of Image and Graphics 1, 1–10 (2005) 5. Ohta, Y., Kanade, T., Sakai, T.: Color information for region segmentation. Computer Graphics and Image Processing 3, 222–241 (1980) 6. Gonzalez, R.C., Woods, R.E.: Digital image processing, 2nd edn. (2007) 7. Wang, Z., Hu, R., Zhang, K.: Realization of Split-Merge Algorithm in Image Segmentation or Components Labeling. MINI- M ICROSYSTEMS 9, 1649–1651 (2004) 8. Liu, X., et al.: Fast Algorithm for Major andMinor Axes of Ra isins Image. Journal of Anhui Agri. 26, 11482–11483, 11509 (2008)

Methodology Comparison for Effective LAI Retrieving Based on Digital Hemispherical Photograph in Rice Canopy Lianqing Zhou*, Guiying Pan, and Zhou Shi Institute of Remote Sensing and Information System, College of Environmental & Resources Science, Zhejiang University, Hangzhou, P. R. China Tel.: +86-571-86971149; Fax: +86-571- 86971831 [email protected]

Abstract. The paper investigates methodology comparisons for retrieving effective leaf area index (LAI) using digital hemispherical photograph (DHP) in rice canopy. A set of self-making DHP instrument equipped with a fish-eye Len is utilized to acquire DHP in rice canopy, and some self-developing DHP processing procedures are utilized to pre-process DHP and extract effective LAI from DHP (LAIDHP) rapidly. Based on Beer-Lambert’s law and gap fraction that computed from DHP, four methods of single zenith angle (SZA), Lang, Mill formula(Mf) and iterative formula(IF) are used to derive LAIDHP(effective LAI from them are called LAISZA, LAILang LAIMf and LAIIF ,respectively). LAISZA, LAILang, LAIMf and LAIIF are inter-compared and are also compared with from AccuPAR LP-80(LAIAPAR) and direct manual method (LAIdirect). It is found that, in general, LAISZA, LAILang LAIMf and LAIIF are similar to LAIAPAR, but slightly lower than LAIdirect. During their intercomparsion, LAILang is more similar to LAIAPAR than other three and LAIMf is more similar to LAIdirect, while LAISZA and LAILang are almost the same. It is implied that Lang method outperformed the other three when compared with AccuPAR LP-80 and Mill formula method outperformed the other three when compared with direct manual measure. Keywords: Rice Canopy, Digital Hemispherical Photograph, Effective LAI, Methodology Comparison.

1 Introduction Rice is one of the uppermost crops in China, and are increasingly important worldwide-both to China and to South Asian countries, and there is an obvious need to obtain accurate estimates of its leaf area index (LAI). LAI is an essential input into many models of rice growth and yield estimation as well as being an essential component of comparative studies of many canopy-level attributes such as carbon cycle, transpiration * Project supported by the National High Technology Research and Development Program (863) (2008AA10Z206). Corresponding author, Address: Institute of Agricultural Remote Sensing and Information System, Zhejiang University, Hangzhou, Zhejiang Province, P.R. China 310029. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 71–82, 2011. © IFIP International Federation for Information Processing 2011

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and water use efficiency and understory synthetic photon flux density (PPFD) capture [1].So LAI is a dimensionless quantity characterizing the canopy of an ecosystem and a key component of biogeochemical cycles in ecosystems. Also managers (farmers and foresters), ecologists, site and global modelers, request information about canopy leaf area index. Unfortunately, this interface between ecosystem and atmosphere is very difficult to quantify, due to its spatial (horizontal and vertical) and temporal variability: annual cycles and interannual variability interact with the crop structure, stratification and heterogeneity [2]. Leaf area index can be measured directly by destructive harvest or allometric approaches [3, 4].Direct measurement may be practical with short canopies such as those of many arable crops, but is usually laborious and timely consuming. Indirect methods for determining LAI are now commonly used to overcome this problem [5,6] and have an additional advantage of being non-destructive. These methods include direct measurement of intercepted radiation using line quantum sensors [7] or radiometers, inclined point quadrant [8], gap fraction techniques [3,5] and capacitance sensors [9]. Of these techniques, it is the group based on gap fraction data that is now the most widely applied. Digital hemispherical photographs (DHPs) have been widely used to measure canopy structure with the recent rapidly advancement in digital cameras and may have brought us a needed tool at a reasonable cost to estimate LAI. Digital hemispherical photography system(DHPS) made with a fisheye lens allow the acquisition of DHPs without the need of scanners to digitize images and can be quickly inspected on the camera’s viewer, or on laptop screen and in a timely fashion in the field. Moreover, for a given camera, the hemispherical images are of consistent size and position on the digital array. This retrieval can be done in a consistent manner at many zenith angles [10, 11, 12]. The model commonly used with indirect methods (including DHPS) to determine the LAI is the Poisson law. It assumes that leaves are uniformly and randomly distributed, which may be valid for homogeneous canopies[13],but does not hold for canopies with aggregative patterns [14,15].To allow the use of the Poisson law, the concept of effective LAI is proposed[16,17,18] as a result of the contribution of woody elements to the total plant cover, which results in overestimation of LAI, and clumping of foliage, which results in underestimation of LAI. So, in this paper, a set of self-made DHPS is applied to acquire DHPs of rice canopy, and some self-developing DHP processing procedures are utilized to process DHP and to compute gap fraction rapidly from them. Then four methods, single zenith angle (SZA), Lang, Mill formula(Mf) and iterative formula(IF),are utilized to derive effective LAI based on those gap fractions(effective LAI from those four are called LAISZA, LAILang, LAIMf and LAIIF , respectively). At last methodology comparison will be investigated in detail.

2 Materials and Methods 2.1 DHPS In this paper, a DHPS based a set of self-made digital photography sensor with a fisheye lens is utilized to capture fisheye photographs from rice canopy, which contents of fisheye lens, filters changer, CMOS camera and laptop computer, as shown in Figure1.

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DHP

Light into fish-eye lens from 180°×360°

Fish-eye len

Filter changer

Laptop computer power

singlechip

Filter changer button

CMOS Camera datum

Fig. 1. Digital hemispherical photography system (DHPS) for rice canopy

In Figure 1, the optical component of DHPS is the fisheye lens with a view zenith angle of 180° mounted on the CMOS camera, which can acquire visible band DHP of rice canopy. These DHPs are stored in format of JPG on the laptop computer hard disk. 2.2 Site Description and Experiment Design The study area is located in China National Rice Research Institute (CNRRI), Fuyang city of Zhejiang province. DHPs are acquired began in Sept., 2009, 30 days after early rice seedling being transplanted, and once per week in cloudy day till rice tassels. Four sampling plots are set up in the selected paddy field, and AccuPAR LP-80, DHPS and directly manual measure are carried out sequentially in each sampling plot. X parameter of AccuPAR LP-80 is initialized to 1.0 and detector is set up along two directions: obeying to rice seedling row and 45° clockwise. Mean value of two LAI-readings from two directions respectively is taken as the effect LAI by AccuPAR LP-80, which is called LAIAPAR. The DHPS is set levelly in the center of each sampling plot, and inside rice canopy with its fisheye lens oriented upward to 0.5m beneath rice canopy top, or above rice canopy with fisheye Len downwards to 0.5m away from top of rice canopy. Gap fraction is computed from DHPs as the input of calculating LAI. During direct manual measure, five rice seedlings or sixty pieces of rice leaves are picked out. Length (L) and width (W) of each leaf are measured by ruler, and leaf area (LA) is calculated by LA=L×W×0.83[8] Effect LAI from direct manual measure (LAIdirect) is equal to all rice LAI in a sampling plot divided by its area.

。

3 Methodologies 3.1 Working Flow The working flow for extracting effect LAI from DHPs of rice canopy is shown as Figure 2.

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DHP

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Fig. 2. The flow sheet

Where SZA is single zenith angle, Mf is Mill formula, IF is Iterative formula. 3.2 Digital Image Pretreatment In DHPS method, effect LAI is derived from gap fraction which computed from binary image of rice canopy. So a series of image pretreatment steps must be carried out to transfer original color photo to gray and to binary consisting of two types of pixels: leaves and background (non-leaves). In color DHP from fisheye lens upward, rice leaves and background (sky) can be distinguished more clearly in the blue band than in other two. As we know, in visible band, rice leaves absorb more blue than red and green ray, transmission and reflection of blue is the least in rice canopy. So the blue band of DHP is selected as original data for binary classification. On the other hand, in color DHP from fisheye lens downwards, rice leaves and background can be learn more clearly in the green band than in other two. In this situation, fisheye lens of DHPS is setup downwards above rice leaves, the green gray is reflected mostly by rice leaves, which is acquired by fisheye lens, and absorbed by non-leaves. So the green band of DHP is selected as original data for binary classification. After color DHP being grayed, a typical two-peak image can be obtained: pixels of foliage making for one and the background make for another in the histogram, and there is little pixel which gray value is between the two peak values, which leads to the valley between two peaks. So the valley value can be applied as the threshold to classify foliage and background pixels clearly and reasonably. In this paper, in gray image from fisheye lens upward, pixels with gray value being lower than the threshold are classified as foliage and higher as background(sky or non-foliage),and in fisheye lens downward, the higher as foliage and lower as background(sky or non-foliage)[19]. 3.3 Detection of Gap Fraction Gap fraction was estimated using an overlay defining n annuli covering the hemispherical image with their positions determined by a range of zenith angles, such as n is

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9 and zenith angles ranging from 5° to 85° with a step of 10. The location of the midpoint of each annulus can be fixed so that annuli are positioned with equal zenith angles. Gap fractions are determined for each annulus using image analysis and computed by Eq. (1):

P (θ ) =

P0 (θ ) P0 (θ ) + P1 (θ )

(1)

Where P(θ) is gap fraction at zenith angle θ,P1(θ) the fraction of foliage and P0(θ) background (none-foliage). 3.4 Methodology 3.4.1 Theory Basis In DHP method effect LAI (LAIeff) is computed from the gap fraction P(θ) following the Poisson law [20,21]: LAI eff =

− ln P (θ ).cos θ G (θ )

(2)

Where LAIeff is effect LAI, P(θ) is gap fraction at zenith angle θ, G(θ) is the mean projection of a leaf area unit in a plane perpendicular to direction θ which is directly dependent of the leaf angle distribution. There are two distinct characters about G function [22, 23]: at a view angle of 57.5°, G(θ) can be considered as almost independent of leaf inclination, e.g. θ≈57.5 , G(θ) ≈0.5,as shown in Fig.3(a);

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(a) Fig. 3. The projection function G[18]

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for a uniform leaf azimuth distribution and a constant leaf normal angle, G(θ) can be approximated as a linear function of θ in the 25-65° range. The slope of the regression (∂G(θ)/∂θ) was then related to the average leaf inclination angle(ALIA) by polynomial fitting, as shown in Fig.3(b) which is cut from Fig.3(a) by θ between 25-65°.It can be considered that a linear function may fit G function well in Figure 3(b). 3.4.2 Single Zenith Angle As described in 2.4, at zenith angle 57.5°, G(θ) ≈0.5, effect LAI can be derived independently on the leaf inclination following Eq.(3): LAI SZA =

− ln T (57.5°).cos(57.5°) 0.5

(3)

Where T(57.5°) is gap fraction computed within 55-60° zenith angles. For this particular direction, G(θ) is almost independent of leaf inclination simplifying the LAI retrieval process [24]. 3.4.3 Lang Model On the assumption that a uniform leaf azimuth distribution and a constant leaf normal angle, G(θ) can be approximated as a linear function of zenith angle θ in the 25-65◦ range. The slope of the regression (∂G(θv)/∂θv) is then related to the ALIA by polynomial fitting. Using an initial estimate of LAI based on gap fraction measurements at a 55° (close to 57.5°) zenith angle, the slope ∂G(θ)/∂θ can be estimated and then ALIA can be derived following Eq.(4):

α = 56.63 + 2.52 ×103 S − 141.47 ×10−3 S 2 − 15.59 ×10−6 S 3

(4)

+ 4.18 ×10−9 S 4 + 442.83 ×10−9 S 5

(

x=-3+ ā÷9.65

)

-0.6061

(5)

⎧ ⎪ ( x 2 + tan 2 θ )1/ 2 cos θ , ε 1 = (1 − x 2 )1/ 2 , x ≤ 1 ⎪G (θ ) = −1 x + (sin ε1 ) / ε 1 ⎪ ⎪ ⎨ ⎪ ( x 2 + tan 2 θ )1/ 2 cos θ ⎪G (θ ) = , ε 2 = (1 − x −2 )1/ 2 , x 1 1 ⎪ x+ ln[(1 + ε 2 ) / (1 − ε 2 )] ⎪ 2ε 2 x ⎩

(6)

＞

Where ā is ALIA,S is the slope ∂G(θ)/∂θ[21,23]. So the route for Lang method is followed as: detecting gap fraction P(25°) - P(75°) from DHP with zenith angle ranging from 25° to 75°; retrieving effect LAI by SZA; computing G(25°) - G(75°) by introducing gap fractions obtained in and LAIs in into Eq.(2); the ALIA ā is calculate by Eq.(4) and then filled in Eq.(5) in order to obtain x; x then is utilized in Eq.(6) and assuming θ is 57.5°,a new G(57.5°) is worked out and then filled in Eq.(3) to calculate a new LAI by SZA.

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Therefore, the LAI estimates can be refined, and the process is iterated several times until convergence. 3.4.4 Miller Formula Chen and Black[16] derived LAI from the gap fraction measured in all directions using the formula of Miller[25], which assumes that gap fraction depends only on the view zenith angle θ: π /2

LAI = 2 ∫

0

− ln(T (θ )) cosθ sin θdθ

(7)

A practical method was proposed to compute the integral of Eq. (7) from gap fraction measurements in several directions[21]: n

LAI ≈ 2∑ − ln(T (θ i )) cosθ i sin θ i Δθ

(8)

i

Where n is denotation of using an overlay defining n annuli covering the hemispherical image with their positions determined by a range of zenith angles, T(θi) is gap fraction in the ith zenith angle ring, Δθ is the zenith resolution of annulus expressed by radian. One of the main limitations with this technique is the necessity to sample the entire directional range of gap fraction variation, which might prove difficult for larger zenith angles. 3.4.5 Iterative Formula In this method, a gap fraction model contents of LAI and average leaf inclination angle(ALIA) simultaneity is built by inputting Eq. (5) to Eq.(6) then to Eq.(2). Then the gap fraction model is inversed by using an iterative optimization technique [26]. The Poisson model is used and the leaf angle inclination is assumed to be azimuthally isotropic with an ellipsoidal zenith angle distribution. Starting with a series of initially given LAI and ALIA, the gap fraction model is run in the forward direction to simulate the gap fraction. The variables LAI and ALIA are then iteratively changed, using the simplex algorithm [27], until a good agreement is met between the simulated and measured gap fraction values.

4 Results and Discussion 4.1 Variety of Gap Fraction with Zenith Angle The trend of gap fraction varies with zenith angle θ is illustrated in Figure 4,where there are five curves created by gap fractions from fisheye lens upward(labeled as “LenUp”) and one for downward (labeled as “LenDown”) ,n is the number of concentric annulus. As shown in Fig.4,gap fractions decrease while zenith angle θ increasing, which might be caused by that rice leaves captured by fisheye lens of DHPS increase while θ increasing. 4.1.1 Impact of Fisheye Lens Orientation on Gap Fraction Gap fractions from fisheye lens downward is averagely higher than upward. It might be that the reflection of leaves underlaying rice canopy cannot reach to fisheye lens of

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DHPS because of being sheltered from the topper when lens of DHPS is downward, which leads to the shadowed leaves being misclassified as background in DHP to increase gap fraction. Gap fractions from fisheye lens downward is lower than upward when zenith angle is up to the zenith(zenith angle is close to 0°).It could be the sky inverted reflections in water, captured when fish eye lens downward, are misclassified as “leaves” because of almost the same gray value as rice leaves, which result decrease of gap fractions. Gap fraction from fisheye lens upward decrease more quickly than upward while zenith angle increasing. It is may be because of the shape of rice leaf: rice leaf is gladiate and its tip is captured in DHP in a greater probability when fisheye lens downward, which leads to gap fraction decreasing. And it is on the contrary when fisheye lens of DHPS is oriented upward.

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4.1.2 Impact of Zenith Angle Resolution on Gap Fraction There involve all gap fraction information from full view of zenith angle from 0° to 90° in DHP. In this paper DHP is divided into n annuli, and n gap fractions are detected respectively. When n grows view zenith resolution reaches more higher which leads to more detail gap fraction being detected and curves undulates more sharply ,especially in zenith angle of 0<θ<10°,as shown on Fig.4. As we know, there are few pixels contented by a single zenith annulus in a relatively small zenith angle such as 0<θ<10°, so a little changing of background pixels number might bring a biggish changing of gap fraction. In this paper, gap fraction is estimated using an overlay defining n (n = 9,18,27,36,45,54,63,72,81 and 90) annuli covering the hemispherical image, and 10 zenith resolutions are reached. There are only 5 gap fraction curves of fisheye lens upward where n = 9,18,27,36, 72, 90 and 1 downward of n=18 are picked out as representative zenith resolutions. For these curves of fisheye lens upward, there is little overlapping among the curve of n=9 and the others which are closed to each other. There exists almost the same trend while fisheye lens is oriented upward.

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4.2 LAI Retrieving 4.2.1 Comparison of Four Methods In this paper LAISZA is calculated based on gap fraction detected from the annulus of 55°~60°. LAISZA from four sampling plots are 2.52, 2.53, 2.49 and 2.50 while fisheye lens is oriented upward and 2.33, 2.35, 2.30 and 2.33 while downward. They are close to LAILang which is 2.5 when lens downward and 2.31 when upward. But actually G(θ) is derived by LAISZA in Lang way, which means that Lang way is built upon SZA.LAI from other three approaches are illustrated by Fig.5,in which, LAIMf is the minimum and LAILang is as much as LAIIF.

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Fig. 5. LAIs from different lens direction, zenith angle resolution and method

4.2.2 Impact of Fisheye Lens Orientation on Effect LAI LAIs from fisheye lens upward are always higher than downward. As mentioned in 3.1.1 that shadow of rice leaf is misclassified as background to increase gap fraction in DHP from fisheye lens downward. And a higher gap fraction may leads to a relatively lower LAI according to Eq.2. 4.2.3 Impact of Zenith Angle Resolution on Effect LAI Miller formula is integral, so increasing zenith resolution may do a great deal of good to improve its retrieving precision of LAI. As demonstrated by Fig.5, LAIsMf from two Len orientations keep on increasing while n increases until convergence. LAILang vary little with variety of zenith resolution except for n=9, which indicates that Lang method is a relatively stable one. LAIIF will fluctuate around a value when zenith resolution reaches to a certain level, which means that Iterative formula method is a sensitive one. So, in the following text, zenith resolution of n=90 is applied in Miller formula and n=18 in the others. 4.3 Comparisons among DHP and AccuPAR and Direct Manual Measure LAIDHP from two lens orientations and four methods varying from 2.14 to 2.50 are lower than 2.75 of LAIdirect and close to LAIAPAR from AccuPAR LP-80,as shown in Table 1.

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Methods Single zenith angle(SZA) Miller formula DHP Fisheye Len Upward Lang Iterative formula Single zenith angle(SZA) Miller formula DHP Fisheye Len Downward Lang Iterative formula Direct manual measure AccuPAR LP-80

LAI 2.49 2.32 2.50 2.42 2.31 2.14 2.31 2.37 2.75 2.32

LAIs from DHPS and AccuPAR PL-80 are both lower than from direct measure. It may be that DHP and AccuPAR PL-80 are both based on the Poisson law which assumes that leaves are uniformly and randomly distributed, which may be valid for homogeneous canopies [13] but does not hold for crops with aggregative patterns such as rice [14,15].The heterogeneousness and aggregation of rice canopy must be taken into account if measuring a LAI with more higher precision than now.

5 Conclusions In this paper it is investigated that effect LAIs of rice canopy are calculated based on gap fractions detected from DHPs taken by a set of self-made DHPS with fisheye lens and comparisons are carried out among LAIs from DHP and AccuPAR PL-80 and direct manual measure. It is found that LAIs from DHPs are lower than from direct manual measure and close to from AccuPAR. In this paper the precision of LAI from DHP is not as well as from direct manual measure, but DHP is a quick, nondestructive and simple way to obtain LAI in situ. In addition, more parameters of rice canopy structure will be detected simultaneously from DHP if more models are applied in DHP pretreatment. So it is believed that DHP will be a widely utilized way to measure rice canopy structure parameters rapidly. In the next study for detecting rice canopy structure parameters it is argued that: gap fractions will vary with rice leaves shape during calculating because of relatively lower rice canopy; The heterogeneousness and aggregation of rice foliage will impact on the precision of extracting LAI.

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References 1. Macfarlane, C., Hoffman, M., Eamus, D., Kerp, N., Higginson, S., McMurtrie, R., Adams, M.: Estimation of leaf area index in eucalypt forest using digital photography. Agricultural and Forest Meteorology 143, 176–188 (2007)

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2. Wu, W.B., Hong, T.S., Wang, X.P.: Advance in ground based LAI measurement methods. Journal of Huazhong Agricultural University 126, 270–275 (2007) 3. Norman, J.M., Campbell, G.S.: Canopy structure. In: Pearcy, W., Ehleringer, J.R., Mooney, H.A., Rundel, P.W. (eds.) Plant Phsiological Ecology: Field Methods and Instrumentation, pp. 301–325. Chapman and Hall, London (1989) 4. Daughtry, C.S.T.: Direct measurements of canopy structure. Remote Sensing Reviews 5, 45–60 (1990) 5. Welles, J.M.: Some indirect methods of estimating canopy structure, in Instrumentation for Studying Vegetation Canopies from Remote Sensing in Optical and Infrared Regions. Remote Sensing Reviews 5, 96–101 (1990) 6. Welles, J.M., Cohen, S.: Canopy structure measurement by gap fraction analysis using commercial instrumentation. Journal of Experimental Botany 47, 1335–1342 (1996) 7. Pierce, L.I., Running, S.W.: Rapid estimation of coniferous forest leaf area index using a portable integrating radiometer. Ecology, Ecological Society of America 69, 1762–1767 (1988) 8. Wilson, J.W., Reeve, J.E.: Inclined point quadrats. New Phytologist 59, 1–8 (1960) 9. Vickery, P.J., Bennet, I.L., Nicol, G.R.: An improved electronic capacitance meter for estimating herbage mass. Grass and Forage Science 35, 247–252 (1980) 10. Chen, J.M., Black, T.A., Adams, R.S.: Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand. Agricultural and Forest Meteorology 56, 129–143 (1991) 11. Frazer, G.W., Trofymow, J.A., Lertzman, K.P.: Canopy openness and leaf area in chronosequences of coastal temperate rainforests. Canada Journal of Forest Research 30, 239–256 (2000) 12. Fournier, R.A., Landry, R., August, N.M., Fedosejevs, G., Gauthier, R.P.: Modeling light obstruction in three conifer forests using hemispherical photography and fine tree architecture. Agricultural and Forest Meteorology 82, 47–72 (1996) 13. Levy, P.E., Jarvis, P.G.: Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel. Agricultural and Forest Meteorology 97, 199–212 (1999) 14. Lemeur, R., Blad, B.L.: A critical review of light models for estimating the shortwave radiation regime of plant canopies. Agricultural and Forest Meteorology 14, 255–286 (1974) 15. Nilson, T.: Inversion of the frequency of gaps in plant stands. Agricultural and Forest Meteorology 8, 25–38 (1971) 16. Chen, J.M., Black, T.A.: Measuring leaf area index of plant canopies with branch architecture. Agricultural and Forest Meteorology 57, 1–12 (1991) 17. Chen, J.M., Cihlar, J.: Plant canopy gap-size analysis theory for improving optical measurements of leaf area index. Applied Optics 34, 6211–6222 (1995) 18. Chen, J.M.: Optically-based methods for measuring seasonal variation of leaf area index in boreal conifer stands. Agricultural and Forest Meteorology 80, 135–163 (1996) 19. Cattleman, K.R. (ed.), Zhu, Z.G. (Trans.): Digital Image Processing. Publishing House of Electronics Industry, Beijing (1998) (in Chinese) 20. Monsi, M., Saeki, T.: Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. The Journal of Japanese Botany 14, 22–52 (1953) 21. Welles, J.M., Norman, J.M.: Instrument for indirect measurement of canopy architecture. Agronomy Journal 83, 818–825 (1991) 22. Weiss, M., Beret, M., Smith, G.J., Jonckheere, I., Coppin, P.: Coppin: Review of methods for in situ leaf area index (LAI) determination Part II. Estimation of LAI, errors and sampling. Agricultural and Forest Meteorology 121, 37–53 (2004)

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Molecular Methods of Studying Microbial Diversity in Soil Environments Liu Zhao1,2, Zhihong Ma1,2, Yunxia Luan1,2, Anxiang Lu1,2, Jihua Wang1,2, and Ligang Pan1,2,* 1

Beijing Research Center for Agrifood Testing and Farmland Monitoring, 100097 Beijing, China 2 Beijing Research Center for Information Technology in Agriculture, 100097 Beijing, China Tel.: +86 10 51503031; Fax: +86 10 51503406 [email protected], [email protected]

Abstract. Microorganisms live in all parts of the biosphere and are critical to nutrient recycling in ecosystems. In recent years, the development of methodologies for the analysis of microorganisms and microbial ecology, at the molecular level, has progressed phenomenally. This review introduces and compares the various molecular methods for studying microbial diversity in soil environments, and the advantages and disadvantages of current methods are proposed as well. Keywords: Microbial diversity; SIP; T-RFLP; RAPD; DGGE; Soil.

1 Introduction Microorganisms are ubiquitous in the environment and fulfill a range of important ecological functions, particularly those associated with nutrients cycling processes and maintenance of ecosystem health in soil[1]. Soil contain an estimated 109 prokaryotes and more than 2000 genome types per gram of soil, with an average genome type representing less than 0.05% of the soil community[2-3]. Until a few decades ago, soil microorganisms could only be studied by microscopic observation or culture-dependent methods. In less than a decade, using nucleic acid probe technique for the detection of microorganisms had exploded. Nowadays, molecular ecology techniques based on sequence comparisons of nucleic acids can be used to provide molecular characterization while providing a classification scheme that predicts natural evolutionary relationships[4-5]. These laboratory-based works have been spectacularly successful in revealing details of soil microbial diversity. So we attempt to draw together some of these studies, with emphasis on the advantages and disadvantages of current molecular methods, for studying microbial diversity in soil environments. *

Corresponding author.

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2 Stable Isotope Probing Stable isotope probing (SIP) has been coupled with nucleic acid methods to provide a culture-independent method of linking the identity of bacteria with their function in the environment[6-7]. Soil is either incubated after adding a 13C-labeled substrate or a plant is labeled with 13C-CO2. Soil DNA or RNA is then extracted and centrifuged in a density gradient to separate 13C-labeled nucleic acids from those containing. Then separated, labeled DNA can be amplified using PCR. Analysis of the PCR products, through cloning and sequencing for example, allows the microbes that have assimilated the labeled substrate to be identified[8-9]. SIP-based approaches hold great potential for linking microbial identity with function, but at present a high degree of labeling is necessary to be able to separate labeled from unlabeled marker molecules.

3 Terminal-Restriction Fragment Length Polymorphism Terminal-restriction fragment length polymorphism (T-RFLP) has multiple advantages for its rapid gain in popularity: data are quantitative and comparable between laboratories, the final electrophoresis step can be performed with automated sequencing equipment at core sequencing facilities[10]. This method provides a picture of the community including incorporates diversity and phylogenetic details. The profiles can be generated by using the procedure from physical-capture[11], fluorescence scanner[12] or 32P-labelled primers[13]. Because of its relative simplicity, T-RFLP has been applied to the analysis of soil microbial diversity, for instance, fungal ribosomal genes[14-15], bacterial 16S rRNA genes[16-17], and archaeal 16S rRNA genes[18]. In addition, TRFLP has been used for the analysis of functional genes such as those encoding for nitrogen fixation and methane oxidation[19-20].

4 Fluorescence in Situ Hybridization Fluorescence in situ hybridization (FISH) is a relatively new technique utilizing fluorescently labeled DNA probe to detect genes of microorganisms in soil samples. Apart from allowing direct visualization of bacteria in the environment, FISH also has the added advantage of being able to detect active cells by targeting rRNA. This method has been reliably used for identification and quantification of ammoniaoxidizing bacteria[21-22]. Several studies have used FISH coupled with microautoradiography (FISH-MAR), this combined approach allows in situ identification and provides information on substrate utilization in complex microbial communities[23-24].

5 Microarray Microarray for microbial community analysis has been classified into three main categories depending on the combination of probe types and target molecules exploited: community genome arrays, rRNA-based oligonucleotide microarrays and functional gene arrays[25-26]. Tiquia constructed a 50-mer oligonucleotide microarray

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using 763 genes involved in nitrogen cycling[27]. The increased use of cultivationindependent metagenomic approaches employing large-insert cloning could lead to an important extension of the CGA approach for large genomic fragments from uncultivated microorganisms[28-29]. In the meantime, adapting enzymatic signal amplification approaches for microarray analysis[30-31], the use of high sensitivity microarray hybridization detection devices hold much promise for enhancing the sensitivity of direct detection of extracted rRNA[32-34].

6 Random Amplified Polymorphic DNA Random amplified polymorphic DNA (RAPD) use DNA products by PCR which are based on random priming of the target DNA, these primers are usually 10 bases in length and are designed to a number of random target sites[35]. Huai studied the genetic variation and spatial distribution of the ectomycorrhizal fungus, the 33 sporophores studied belonged to distinct genotypes[36]. The advantage of this approach is that it requires very little sample material and obtains results rapidly. However, this method is less susceptible to base changes in the target DNA. Thus usually the PCR is performed at low stringency for the first few cycles. This technique allows the generation of product when mismatches between template and primers occur. Hence, similar patterns generated using this technique better reflect phylogeny of the phages. One disadvantage of this technology is the results may be difficult to repeat by other users[37-38].

7 DNA Fingerprinting DNA fingerprinting is used to distinguish differences in the genetic makeup of microbial populations from different samples. The advantage of this technique is that it enable high sample throughput and can be used to target sequences that are phylogenetically or functionally significant. Nowadays, the most common used techniques are denaturing gradient gel electrophoresis (DGGE) or temperature gradient gel electrophoresis (TGGE). They have been used to profile fungal microbial communities from many diverse environments[39-40]. DGGE fingerprints of total DNA from rhizosphere revealed that there was a relationship between fungal community composition and rhizosphere development[41]. Fungal community diversity was studied by PCR-DGGE followed by sequence analyses of ITS fragments in soil samples[42]. Although DGGE is a promising technique, it can still underestimate fungal diversity. The number of bands depends on the resolution of the gels, this takes time to optimize and is difficult to reproduce[43]. This has already been demonstrated in previous studies that phylogenetically distant taxa can have comigrating bands and that one band does not mean one unique phylotype[44-45]. A new DNA-based fingerprinting approach, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), can be used to separate PCR amplicon of the ITS regions[46]. But this technique does not lend itself to high sample throughput. However, its improved ability to discriminate between soil communities and retrieve sequence information make it a powerful technique for elucidating key differences in community structure.

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8 Conclusions and Future Directions Soil has been dubbed “The Final Frontier”. Current knowledge pertaining to the diversity and distribution of soil ecosystem is still rudimentary. In recent years, obviously improvement in traditional approaches combined with various molecular techniques, such as molecular and phylogenetic inventorying via clone libraries[47], quantitative real-time PCR[48], pyrosequencing[49], quantitative membrane hybridization[50] has provided new data on these aspects. Nonetheless, we feel that these sophisticated methods are highly relevant to the existing knowledge of the role of microorganism in ecosystem processes, but the application of the techniques to this end is far from complete. From recent molecular ecological studies, we have seen that most carbon- and nitrogen- cycling gene sequences are divergent from those of the model organisms on which most of our existing appreciation of biogeochemical cycles are based. However, our understanding of ecologically relevant microorganism involved in these cycles is limited. Hence we are aware that further understanding of how they fit into the complexities of ecosystems will require both bottom-up and top-down approaches. Acknowledgments. This work was supported by the public foundation from Beijing Municipal Rural Affair Committee (20080901), National High Technology Research and Development Program 863 (2010AA10Z403, 2007AA10Z202) and Beijing Municipal Science and Technology Commission Program (Z09090501040901).

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Monitoring the Plant Density of Cotton with Remotely Sensed Data Junhua Bai1,2,3, Jing Li2, and Shaokun Li1,* 1

Institute of Crop Sciences, Chinese Academy of Agriculture Sciences, Beijing 100081, P. R. China Tel.: +86 10 82108891 [email protected] 2 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, P. R. China 3 College of Agricultural Sciences of Shihezi University, Shihezi Xinjiang, 832003, P. R. China

Abstract. PDC (Plant Density of Cotton) was an essential parameter for estimating the cotton yield and developing the zone-management measurements. This paper proposed a new method to retrieve PDC from the satellite remote sensing data. The thirteen fields of Xinjiang Production and Construction Corps (XPCC) (total 630 hm2) were selected as the study area, where the sowing date, emergence date, and PDC were investigated. Based on the investigation data the linear models to estimate PDC are established using EVI and DEVI respectively. The results indicated that the difference of seedling size caused by the emergence time decreased the estimation accuracy of PDC. To improve the estimation accuracy the partition functions were established in terms of sowing date. DEVI is capable of reducing the influence of soil background significantly and it can bring the monitoring time forward from June 9th to May 24th in this research. The results indicated that the optimal time monitoring PDC would be from squaring to full-flowering of cotton growing period. A demonstration to monitor PDC was taken on June 9th in the 148th farm of XPCC. It can be concluded that the emergence time and the non-cotton background were the main factors affecting the monitoring accuracy of PDC, and the partition function with the emergence time could improve the estimation accuracy, and DEVI could make the monitoring time forward, and the optimal monitoring time was from the squaring stage to the full-flowering stage. This research provides an efficient, rapid and intact way to monitor PDC, and it is significant for operational application at a regional scale. Keywords: Cotton, Plant Density, Remote sensing.

1 Introduction PDC (Plant Density of Cotton) is one of key factors affecting cotton yield, and is the most important index for analyzing cotton growth and taking the management *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 90–101, 2011. © IFIP International Federation for Information Processing 2011

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measurements. The cotton yield in Xinjiang accounts for about 30% in China and 8% in the international cotton trade. PDC is a key item in all measurements for the cotton farmland management (W. F. Zhang et al., 2004; X. L. Hou et al., 2006). The low PDC weakens the applications of cultivation technology for increasing cotton yield. Agriculture is one of important applications of satellite remote sensing technology, and the numerous researches have been done on the area estimation, yield estimation and growth monitoring (O. Wendroth et al., 2003; P. C. Doraiswamy et al., 2004; X. M. Xiao et al., 2006). In cotton monitoring, the method of spectrum matching has been used to extract the cotton pixel in the regional scale(J. Li et al., 2005), and the image texture of geo-statistical has been introduced into monitoring cotton growth(C. J. Liao, et al., 2006), and the hyper-spectrum can be used to inverse the biomass and leaf area index quantitatively (D. L. Zhang et al., 2007; J. H. Bai, et al., 2007), V. Alchanatis (2005) used the hyper-spectrum image to distinguish the weeds from cotton field, and the action of water stress is compared with the root disease by infrared sensor (N. R. Falkenberg et al., 2007). And although PDC plays an important role in the farmland practices, few researches was done on PDC monitoring by satellite data. Normal Difference Vegetation Index (NDVI) is one of the most popular parameters used to evaluate the crop growth condition (J. W. Rouse, 1974; F. Baret, G. Guyot, 1989, 1991). And some researches show that NDVI is susceptible to the influence of the non-vegetation background, and it saturates when the vegetation cover increases to the some extent (Q. Wang et al., 2005; A. E. K. Douaoui, 2006). Other indices were constructed to make up for the inadequacy, and the Enhance Vegetation Index (EVI) is one of the most popular in them. At present, the field investigation is almost the only way to obtain PDC. The disadvantage of this way is that it can only get the point data, and consumes much time and energy. In this paper, we analyzed the multi-temporal satellite images coupling the field investigation data from the emergence to full-flowering stage of cotton to (1) construct the linear model to estimate PDC, (2) analyze the main factors affecting the monitoring accuracy of PDC and perfect the model, (3) obtain the optimal period to monitor PDC, (4) demonstrate the PDC monitoring method in the regional scale.

2 Methods 2.1 Study Area The study area is located in the148th farm of XPCC, beside the Manas river valley ((45°00′N 86°11′E; 44°46′N 86°23′E). The climate type is the typical continental-arid climate, and the sunshine rate is about 80% during the cotton growing period. The cotton planting proportion accounts for over 90% in the farm. Generally it seeds from the early April to the middle April, and Squaring at the early of June, flowers at early July. The row space is usually 55cm+20cm+55cm and plants space is usually 9.6cm. At the middle of July, the vegetation coverage fraction rises to over 80%, and the top-ending measurement was taken to control the unlimited growing character of cotton. In 2007, the sowing date is mostly from April 13th to 19th.

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Fig. 1. Location of the experiment

2.2 Investigation and Computation for PDC PDC was investigated from June 5th to 15th, in 2007 and 2008. In order to make the sample data representative, we chose the investigation points far from the field patch boundary to avoid the boundary effect on the crop growing. Three sampling units were investigated in every investigation points. The sampling points were positioned by GPS. The area of sampling units is approximately 26.7m2. We visited the landowner of each sampling fields to record the seeding and emergence date. The sowing time was divided into three partitions including the early, middle, and late of the sowing date 52 sample points are obtained through investigating thirteen fields (630 hm2), including 40 sample points from the middle sowing date, 4 from the early sowing date, and 8 from the late sowing date. The data consisting of three sowing date are employed evenly into the establishing and testing model. PDC could be calculated as:

Nu =

n1 + n 2 + n 3 × 375 3

(1)

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Where Nu was PDC per hectare(×105 plant hm-2),

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ni (i = 1, 2, 3) was the number of

plants in the sampling unit. 2.3 Remote Sensing Images and Pre-processing LANDSAT-5/TM images in April 22th, May 24th, June 9th, June 25th, and July 11th, 2007 were used. The geo-location was done by ENVI4.2 with the geo-referenced panchromatic waveband image from LANDSAT-7 as the basic image. The atmospheric correction was done using the AgRSIS software. The atmospheric radiation transferring model uses 6S model and the aerosol optical thickness was inversed by the algorithm of dense dark vegetation. The pixel size was 28.5m×28.5m. 2.4 Vegetation Index EVI =

ρ n ir − ρ red ×G ρ n ir + C 1 × ρ red − C 2 × ρ b lu e + L

(2)

Where ρnir, ρred, ρbule was reflectance in near-infrared (NIR), red and blue waveband respectively. C1 and C2 were the correction coefficients of atmospheric resistance in red and blue; L is the canopy background brightness correction factor and G is the gain factor. The coefficients adopted in the EVI algorithm are, L=1.0, C1=6.0, C2=7.5 and G (gain factor) =2.5 (Huete et al., 1994; Huete et al., 1997). To further explore the suitable remotely sensed parameters, DEVI is the difference of EVI between the growth stage and the bared soil stage:

D EVI = EVIi − EVIe

(3)

2.5 The Evaluation of Estimation Accuracy The Relative Extremenum Predication Error (REPE) is used to evaluate the estimation accuracy of PDC, REPE is calculated as REPE =

RM SE N U m ax − N U m in n

R M SE =

∑ (N i =1

u

p − N ut )2

n −1

"

(4)

(5)

Nu max, Nu min is the maximum and minimum value in the testing models, respectively; Nup, Nut is the estimation and measured value, respectively.

3 Results 3.1 PDC Estimation with EVI PDC estimation based on the data from three emergence stages. As shown in Fig.2, from May 24th to July 11th, EVI rose with the cotton growing. The average EVI

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for each growing stage is 0.11, 0.27, 0.53 and 0.82. On May 24th, the range of EVI distribution is narrow and it shows insignificant linear relationship between EVI and PDC. However, EVI distribute more widely on June 9th and June 25th, and it shows the obvious linear relationship. EVI on June 25th is higher than on June 9th, the correlation degree isn’t kept increasing tendency as time goes on, EVI difference on July 11th is less than on June 25th and the linear isn’t significant. From the above analysis, we could deduce the emergence time caused the difference of seedling size, which affects the linear between PDC and EVI. Furthermore, the mixed pixels with non-cotton information decrease the relation on May 24th and July 11th, and the relations aren’t nearly interrupted on July 9th and July 25th. The correlation coefficients between PDC and EVI haven’t reached significant level on May 24th and July 11th and reached significant level on June 9th and June 25th, and correspondingly R2 is 0.23 on June 9 and 0.30 on June 25. Therefore, the estimation models on PDC with EVI as below:

△

June 9th : Nu=2.61EVI+1.22

R2=0.23**

(6)

June 25th : Nu =2.41EVI+0.65

R2=0.30**

(7)

R21, R22 showed the coefficient of determination from three sowing dates and middle sowing date. the late sowing; × the early sowing; ■ the middle sowing; below.

◇

at the edge of farmland, and the same as

Fig. 2. Relationship between PDC and EVI

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PDC estimation based on the data from middle emergence stage. As shown in Fig.2, coefficient of the linear equation between PDC and EVI rise to 0.75 for June 9th and 0.63 for June 25th when the data from middle emergence stage is only used, but unfortunately, the determination coefficient hasn’t still reach the significant level on May 24th and July 11th. The estimation models on PDC with EVI as below: June 9th : Nu = 4.364EVI+0.686 June 25th : Nu = 3.185EVI+0.148

R2=0.75** R2=0.63**

(8) (9)

Fig. 3. Relationship between PDC and DEVI

3.2 PDC Estimation with DEVI PDC estimation based on the data from three emergence stages. EVI on May 24th, June 9th, June 25th and the July 11th, respectively minus EVI on April 22th get DEVI on the corresponding time. It is shown in Fig.3, the determination coefficients of the linear equation between PDC and DEVI are 0.34 reaching the great significant level on May 24th, and on the contrary the linear disappears when EVI estimated PDC on May 24th. In addition, the determination coefficients rise from 0.23 to 0.27 on June 9 and from 0.3 to 0.34 on June 25th. And the linear still disappears on July 11th. The estimation models on PDC with DEVI as below:

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May 24th : Nu = 12.13DEVI+1.13

R2 = 0.34 **

(10)

June 9th : Nu = 2.82DEVI+1.31

R2 = 0.27**

(11)

June 25th : Nu = 2.65DEVI+0.64

R2 = 0.34**

(12)

PDC estimation based on the data from middle emergence stage. From Fig.3, the linear correlations between PDC and DEVI from middle emergence stage are optimized on May 24th, June 9th and June 25th, and the determination coefficients reach the extremely significant level and rise to 0.46, 0.76 and 0.67 distinctly. Unfortunately, the coefficient on July 11th doesn’t reach the significant level. The estimation models on PDC with DEVI as below: May 24th : Nu = 10.95 DEVI+1.147

R2 = 0.46**

(13)

June 9th : Nu = 4.114 DEVI+0.980

R2 = 0.76**

(14)

June 25th : Nu = 3.174 DEVI+0.329

R2 = 0.67**

(15)

Table 1. Testing about the different models of estimating PDC

Time

May 24

June 9

June 25

Ⅰ

The estimating The regress equations between measured RMSE methods and estimated values

Ⅲ Ⅳ Ⅰ Ⅱ Ⅲ Ⅳ Ⅰ Ⅱ Ⅲ Ⅳ

REPE

y = 0.184x+1.507 (R2 = 0.181*)

0.301

27.1

y = 0.454x+0.939 (R2 = 0.418**)

0.248

22.7

y = 0.252x+1.393 (R2 = 0.293**)

0.280

25.7

y = 0.711x+0.446 (R2 = 0.656**)

0.205

18.7

y = 0.282x+1.342 (R2 = 0.274**)

0.282

25.4

y = 0.727x+0.408 (R2 = 0.660**)

0.207

18.7

y = 0.322x+1.279 (R2 = 0.466**)

0.253

23.2

2

**

0.208

19.1

2

**

y = 0.307x+1.317 (R = 0.431 )

0.259

23.3

y = 0.599x+0.636 (R2 = 0.647**)

0.213

19.2

y = 0.559x+0.718 (R = 0.664 )

Ⅱ

Noting: shows the testing result based on EVI from the early, middling and late emergence stage; shows the testing result based on EVI form the middling emergence stage; shows the testing result based on DEVI from the early, middling and late emergence stage; shows the testing result based on DEVI from the middling emergence stage.

Ⅳ

Ⅲ

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3.3 The Validation of Estimated Models and Its Application The validation of estimation models. As Table 1 shows, the veracity of regression models from (6) to (14) is tested, all determination coefficients between the estimated and measured value are validated, but it is noticeable that the determination coefficients of and from DEVI on May 24th also pass through the significant testing. The RMSE on PDC is 0.301×105 plant hm-2 and REPE is 27.1%, which indicates that DEVI would bring the monitoring time forward, on the contrary, when EVI is used to estimate PDC on June 9th and June 25th, RMSE is 0.280 and 0.253×105 plant hm-2, respectively, and REPE is 25.7% and 23.2%, respectively. However, the estimation accuracy of models is greatly improved on May 24th, June 9th and June 25th when the data of middle emergence stage is used to estimate PDC. Among them, RMSE and REPE of on June 9th is the lowest value.

Ⅲ

Ⅳ

﹒

﹒

Ⅰ

Fig. 4. Monitoring PDC with remotely sensed data from LANDSAT-5 in the 148th farm of Xinjiang, china

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Fig. 5. Scene from cotton farmland with the PDC difference

The demonstration for monitoring PDC with LANDSAT-5. It is concluded from analysis above that DEVI can make the PDC monitoring time bring forward to the late May, and the accuracy on June 25th is higher than on June 9th and May 24th. However, and on June 9th (Table 1) have the double properties with the earlier time and the higher veracity, and has the advantage of only using the single-time image, which will enhance the feasibility of estimating PDC. Therefore, on June 9th can be used as the optimal plan to estimate PDC. Monitoring PDC in the 148th farm by , the results from Fig.4 indicate that PDC has the strongly spatial correlation and the distributed continuity. The farmland area for cotton in the 148th farm in 2007 is approximate 15811.7 hectare, and 17.1% for ≤1.20×105 plant hm-2, 10.2% for 1.20~1.35×105 plant hm-2, 13.1% for 1.35~1.50×105 plant hm-2, 13.6% for 1.50~1.65×105 plant hm-2, 12.6% for 1.65~1.80×105 plant hm-2, 10.9% for 1.80~1.95×105 plant hm-2, 8.7% for 1.95~2.10×105 plant hm-2, 6.0% for 5 -2 -2 2.10~2.25×10 plant hm , 3.8% for 2.25~2.40×10 plant hm , 4.0% for ≥40×105 plant hm-2. Farmlands with the different PDC can be distinguished (Fig.5).

Ⅰ Ⅲ

Ⅰ

Ⅰ

Ⅰ

﹒

﹒ ﹒

﹒

﹒

﹒

﹒ ﹒

﹒

﹒

4 Discussion The above analysis shows that two situations may affect the monitoring accuracy. Firstly, when the coverage fraction of bared soil was high, the reflectance signal of vegetation was weak, the soil properties dominated the difference of pixel reflectance, and it was difficult to retrieve PDC accurately. Secondly, the cotton coverage increased from zero to almost 100 percent when the growth stage developed from the seedling stage to full-flowering period. Under the condition of the high cotton coverage, the contribution of soil to the pixel reflectance decreased, and the saturation of vegetation index happened. As shown in the results on May 24th EVI is not sensitive to the PDC difference significantly. DEVI could remove the noise of the soil background and highlight the

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vegetation information. We construct the relationship between PDC and DEVI to monitor PDC, which brings up the PDC monitoring time. Compared with on May 24th, the cotton coverage on June 9th and June 25th was increased remarkably, the proportion of cotton signal rose gradually, and the PDC could be monitored through the green index of cotton canopy population. The analysis also indicated that it was impossible to estimate PDC effectively in any way when the cotton canopy population in the full-blooming stage had the highest land cover, and especially the cotton coverage rose to over 80%, and the canopy population was fuzzy between pixels because of the saturation phenomenon. Moreover, it was difficult to monitor PDC by remote sensing because of another reason. The canopy population was controlled by PDC before the flowering stage, and yet after the cotton grown into the full-flowering stage, the compensation effect of cotton plants appeared by a series of management measures of top-ending, fertilizer-water and chemical controlling, and so the population with the low PDC might develop into the big one, that meant the population with the low PDC had the higher leaf area index and biomass than the population with the high PDC. As a result, the cotton canopy population was simultaneously controlled by the plant size and PDC. Therefore, PDC couldn’t monitored successfully after the full-flowering stage (at July 11th), which was decided by the defect of remotely sensed technology and the characteristic of cotton growth. The cotton farmers couldn’t seed at the same time because of the limitations of human and mechanism, which could cause the difference of cotton plant size in the early stage of cotton growth, the result added the difficulty of estimating PDC with the high accuracy, the information of sowing date should be known to increase the monitoring accuracy.

5 Conclusions This paper proposed a method to monitor PDC by satellite remote sensing images. The results showed that satellite images can be used to monitor PDC at the regional scale although the sowing date and the spectrum saturation have an influence on the accuracy of PDC monitoring. The analysis indicates that the optimal PDC monitoring period is late May by DEVI. The optimal growing stage with the highest estimation accuracy to monitor PDC is from the squaring stage to the early-flowering stage, especially the late-squaring stage could be satisfied with the estimation accuracy and time required by the farmland management measurements simultaneously. The method to monitor PDC by satellite images could be a potential way to investigate PDC at the regional scale.

Acknowledgments The research was supported by the National High Technology Research and Development Program of China (2006AAl0302, 2006AA102207, and 2006AA102272), National Nature Science Foundation of China (40801143), and Nature Science and Technology Innovation Foundation of Shihezi university (ZRKX200748).

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References 1. Alchanatis, V., Ridel, L., Hetzroni, A., Yaroslavsky, L.: Weed detection in multi-spectral images of cotton fields. Computers and Electronics in Agriculture 47(3), 243–260 (2005) 2. Baret, F., Guyot, G.: Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment 46(2-3), 161–173 (1991) 3. Bai, J.H., Li, S.K., Wang, K.R., Zhang, X.J., Xiao, C.H., Sui, X.Y.: The Response of Canopy Reflectance Spectrum for the Cotton LAI and LAI Inversion. Scientia Agricultura Sinica 40(1), 63–69 (2007) 4. Bai, J.H., Li, S.K., Wang, K.R., Sui, X.Y., Chen, B., Wang, F.Y.: Estimating Aboveground Fresh Biomass of Different Cotton Canopy Types with Homogeneity Models Based on Hyper Spectrum Parameters. Agricultural Sciences in China 6(4), 437–445 (2007) 5. Ben-Dor, E., Levin, N., Singer, A., Karnieli, A., Braun, O., Kidron, G.J.: Quantitative mapping of the soil rubification process on sand dunes using an airborne hyperspectral sensor. Geoderma 131(2-3), 1–21 (2006) 6. Boegh, E.H., Broge, S.N., Hasager, C.B., Jensen, N.O., Schelde, K., Thomsen, A.: Airborne multi-spectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment 81(2-3), 179– 193 (2002) 7. Doraiswamy, P.C., Hatfield, J.L., Jackson, T.J., Akhmedov, B., Prueger, J., Stern, A.: Crop condition and yield simulations using Landsat and MODIS. Remote Sensing of Environment 92(4), 548–559 (2004) 8. Douaoui, A.E.K., Nicolas, H., Walter, C.: Detecting salinity hazards within a semiarid context by means of combining soil and remote-sensing data. Geoderma 134(1-2), 217–230 (2006) 9. Falkenberg, N.R., Piccinni, G., Cothren, J.T., Leskovar, D.I., Rush, C.M.: Remote sensing of biotic and abiotic stress for irrigation management of cotton. Agricultural Water Management 87(1), 23–31 (2007) 10. Hou, X.L., Zhang, D., Wang, X.J., Li, P., Sheng, J.D.: Effect of different nitrogen fertilization on yield and nitrogen using of super-high density cotton system. Cotton Science 18(5), 273–278 (2006) (in Chinese) 11. Huete, A., Justice, C., Liu, H.: Development of vegetation and soil indices for MODISEOS. Remote Sensing of Environment 49(3), 224–234 (1994) 12. Huete, A.R., Liu, H.Q., Batchily, K., Van Leeuwen, W.J.D.: A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment 59(3), 440–451 (1997) 13. Huang, J.F., Wang, X.Z.: Study on cotton phenology and climate in Xinjiang. Journal of Arid Land Resources and Environment 13(2), 90–95 (1999) 14. Li, J., Liu, Q.H., Liu, Q., Liu, L.F., Bai, J.H., Li, S.K.: Method research of pixel discrimination about CBERS-02 satellite image based on spectral knowledge. Science in China Series E 35(suppl.), 141–155 (2005) 15. Liao, C.J., Wang, C.Y., Li, H., Yang, P.R.: Cotton growth monitoring during chemical control stage using geo-statistical image texture: a cases study of Shihezi. Transactions of the CSAE 22(8), 135–139 (2006) 16. Rouse, J.W., Haas, R.W., Schell, J.A., Deering, D.W., Harlan, J.C.: Monitoring the vernal advancement and retrogadation (Greenwave effect) of natural vegetation. NASA/GSFC Type III Final Report, Greenbelt, MD, USA (1974) 17. Wang, Q., Adiku, S., Tenhunen, J., Granier, A.: On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sensing of Environment 94(2), 244–255 (2005)

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Motion Blurring Direction Identification Based on Second-Order Difference Spectrum Junxiong Zhang1, Fen He2, and Wei Li1,* 1

College of Engineering, China Agricultural University, Beijing, 100083, P.R. China 2 Chinese Academy of Agricultural Engineering, Beijing, 100125, P.R. China {Zhang_junxiong,hefen_2005,cau2004}@163.com

Abstract. An identification method for uniform linear motion blurring direction based on second-order difference spectrum was proposed. The power spectrum of the blurred image was calculated after the Laplace second-order difference, and then the power spectrum was processed by the homomorphic filtering and the circular low-pass filtering. The blurring direction was attained by linear fitting of the frequency points with highest amplitude selected from the spectrum image. The experiments were carried out by using the blurred images which were simulated by the Lenna standard images with the blur extent being 20 pixels, and the mean square error of detection were 1.32° without additional noise and 2.27° with Gauss noise that the variance was 0.01. When using the real blurred images, the accuracy of the detection was -0.54°. This proposed method is proved to be available for motion blur of random plane direction and adaptable for noise. Keywords: Image processing, Direction identification, Second-order difference, Motion blur, Fourier spectrum.

1 Introduction Motion blur occurs when there is relative motion between the camera and the object in the process of images collection. And it always exists in dynamic, real time image inspecting systems. In order to restore the blurred images, we often need to know Point Spread Function (PSF), which becomes the key of identification of motion blurring direction and extent. According to the Fourier transform features of uniform linear motion PSF, the zero value dark stripes with equal interval existed in the power spectrum of the blurred images and the direction of dark stripes is perpendicular to the direction of motion blurring. In 1976, Cannon used this characteristic to estimate the uniform linear motion PSF of blurred images, while this method was sensitive to noise, it was usable when images’ signal-to-noise ratio is high [1]. Chang and Savakis proposed bi-spectrum and residual spectrum matching methods, which overcome Cannon method’s disadvantages [2], [3]. But the bi-spectrum method need large numbers of data observed which have three-order stability, and the residual spectrum matching *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 102–109, 2011. © IFIP International Federation for Information Processing 2011

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method need original image spectrum, noise variance, PSF styles, and other transcendental knowledge. Yitzhaky got some achievements in PSF estimation of motion blurring and vibrational blurring images, adopted 2×2 differential operator to identify motion blurring direction, and calculated the blurred extent according to differential image autocorrelation function [4], [5], [6], [7]. But this method’s effect was better when the motion blurring direction was in the range of 0°to 45°. Chen thought that any planar motion blurring PSF was generalized tri-diagonal matrix, and firstly carried out the motion blurring direction identification [8], [9], [10]. Some other researches identified blurred direction through Radon transform of image Fourier spectrum [11], [12]. In this paper, a novel motion blurring direction identification method was proposed based on blurred image second-order difference Fourier spectrum, and motion blurred images collected in experiments and standard images were validated and the accuracy was detected.

2 Motion Blurred Image Frequency Character The process of image motion blurred degeneration is the convolution of original image f ( x, y ) and PSF h( x, y ) adding noise n( x, y ) to get degeneration image

g ( x, y ) , and it is shown by formula (1), g ( x, y ) = h ( x, y ) ∗ f ( x, y ) + n( x, y )

(1)

The both sides of formula (1) are Fourier transformed, and according to convolution theorem, the generation model’s frequency expression is G (u, v) = H (u, v) F (u, v) + N (u, v)

(2)

Where G (u, v) , H (u, v) , F (u, v) and N (u , v) were the Fourier transform of g ( x, y) , h( x, y ) , f ( x, y ) and n( x, y ) respectively. In particular, for uniform linear motion in horizontal direction, and without noise influence, L − 1 ( L is blurred extent) dark stripes perpendicular to the motion blurring direction occurred in power spectrum of blurred images, and the motion blurring direction was obtained according to the dark stripes’ direction. Figure 1(a) is the static fruit image, and (b) is the Fourier power spectrum of (a). (c) is the image that (a) was simulated with blurred extent being 11 pixels along the horizontal direction, and (d) is the Fourier power spectrum of (c). (e) is a practical collected blurred image, and (f) is the Fourier power spectrum of (e). When neglecting the noise, the dark stripes perpendicular to motion blurring direction obviously existed in Fourier power spectrum of blurred image. But in practical image collecting, noise is inevitable, dark stripes in vertical direction can be observed constrainedly in Figure 1(d). The reason is that in original image Figure 1(a) the system noise after motion blurred was processed equably, and its effect was reduced. But for practical collected image, in Figure 1(e), the system noise was not neglected, in Figure 1(f), the dark stripes were not obvious, then, it was hard to estimate the motion blurring direction according to the dark stripes’ direction.

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(a)

(b)

(c)

(d)

(e)

(f)

Fig. 1. Fruit image and its Fourier power spectrum

3 Motion Blurred Direction Identification Observed from Figure 1(d) and (f), the linear motion blurring weakened the image high frequency signal, but in motion blurring direction and its vertical direction, the image high frequency detail part changing extent were different. Though the dark stripes in frequency spectrum image were hard to be observed, the middle vertical bright stripes were obvious oppositely, which showed that the high frequency information were strengthened along the motion blurring direction, according to this, then the motion blurring direction can be identified. For the bright part in power spectrum image was circular or fusiform, which was hard to be identified directly, and the accuracy can not be assured. If through high pass filtering and restraining the low frequency part, the high frequency part in bright stripes can be strengthened, so that the bright stripes direction was easy to be identified. When using high pass filtering to process the image, the 8 neighborhood Laplace operator was convolution of image in spatial domain. g L ( x, y ) = g ( x, y ∗ Lap8

(3)

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Where Lap8 is a non-direction second-order difference operator, and the second-order difference image of the blurred image could be obtained after the convolution operate. The second-order difference image was Fourier transformed to get GL (u , v ) , which range was large. And to compare easily, it was need to be homomorphic filtered. G fL = log10 ( GL ( x, y ) )

(4)

Lap8 difference operation was used for blurred image in Figure 1(c), and the result was shown in Figure 2(a). After Fourier transform and homomorphic filtering, the zero frequency was moved in image middle and the result was shown in Figure 2(b).

(a)

(b)

(c)

(d)

Fig. 2. Detection of blurred image simulated

In Figure 2(a), the two edges of fruits superimposed in horizontal direction. Compared with Figure 1(d) and Figure 2(b), the whole high frequency part was strengthened, which was caused for the second-order difference being sensitivity to noise. But the middle vertical bright stripes in Figure 2(b) were more obvious than Figure 1(d), and the motion blur direction can be judged by detecting the bright stripe direction. In order to detect the direction of middle bright stripes in Figure 2(b), the brightest points should be picked up, and then using the least squares method for linear fitting. The number of brightest points should be appropriate. When the number is too large, the influence is strong and the operation quantity is great. The few number influences the reliability. The brightest points can be chosen by threshold segmentation, but it is hard to determine the right threshold. In this paper, the bright points can be obtained by threshold segmentation, sorting, and filtering. Firstly, estimate a threshold to segmentation (choosing double average grey value of power spectrum image as threshold), segment the Figure 2(b) using threshold 85, and the result was Figure 2(c). Then account the number of brightest points. Ensuring the number needed for linear detecting, and reducing the data quantity after sorting,

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the brightest points number is 50 to 300, if not in this range, the threshold should be adjusted and then segment the image. Lastly, sort and filter the brightest points after being arranged from high to low according to the grey value, and choose the front brightest 51 points (according to the experiment, when choosing 40 to 100 points, the results was better) for linear fitting y = ax + b using the least squares method. According to the symmetry of Fourier transform, the linear intercept b was 0. The motion blur direction can be calculated by linear slope a , ⎧90 − a tan(a) ⎩−90 − a tan(a)

α =⎨

a≥0

(5)

a<0

Where the clockwise angle was positive, and anticlockwise was negative. Figure 2(c) was the front 51 brightest points filtered. Figure 2(d) was the linear fitting result, and the motion blur direction was 0.63°. The method before-mentioned was available for the blurred image with high signal-to-noise ratio, but for low signal-to-noise ratio, it was need to be improved. Figure 3(a) was the result that the real blurred image collected was processed by Lap8 difference calculation, Fourier transform and homomorphic filtering.

(a)

(b)

(c)

Fig. 3. Detection of real blurred image

In blurred image, noise can be processed by space domain filtering, but at the same time, it also will weaken the edge information. For the influence of noise, the high frequency part of Figure 3(a) has large intensity. The brightest points obtained by threshold segmentation distributed in high frequency part, and if using the brightest points to linear fitting, the estimating result was wrong. The high frequency part influenced by noise distributed in peripheral of spectrum image, so a circular low-pass filtering was used to weaken the influence of high frequency part. In spectrum image, make the center of a circle as the image center, remove the area outside the circle with diameter being d which decided by the intensity of high frequency chosen, and threshold segment the area inside the circle. Figure 3(b) was the segmentation result that d was 140 pixels and the threshold was 85. The brightest pixels were selected and fitted by least squares method, and lastly the direction was received. In Figure 3(c), the direction of motion blurring was -0.54°. Concluding the above steps, identifying image motion blurring direction was shown in Figure 4.

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Using 8-neighbourhood Laplace operator for convolution

Attaining Fourier power spectrum after filtering

Homomorphic filtering of power spectrum

Carrying out circular low-pass filtering (optional)

Choosing the brightest pixels through threshold segmentation

Using the least squares for linear fitting

Choosing the brightest part through sorting

Obtaining the motion blur direction angle

Fig. 4. Flow chart of motion blurring direction detection

4 Identification Accuracy Test The standard image Lenna was chosen to test the algorithm correctness and validity. Firstly, Lenna was blurred with motion blur extent being 20 pixels, the blur direction being 90° to -80°. Then the algorithm in Figure 4 (Do not need circular low-pass filtering) was utilized to identify the motion blur direction. Figure 5 was the partial result when the motion blur direction was -30°, and the whole identification results were in Table 1, which showed the mean square error of detection was 1.32°. In order to validate the application of the algorithm for low signal-to-noise ratio motion blurred image, the standard image Lenna was blurred with motion blur extent being 20 pixels , the blur direction being 90° to -80°, and adding Gauss noise with variance being 0.001. Then the algorithm in Figure 4 (need circular low-pass filtering) was utilized to identify the motion blurring direction, and the result was shown by Table 2. Table 2 showed the mean square error of detection was 2.27°, which was lower than 1.32° without additional noise. When adding noise and using low-pass filtering, the number of brightness can not reach 50 to 100, which will influence the fitting accuracy.

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(a)

(b)

(c)

(d)

Fig. 5. The detection of L=20pixel, α=30° Table 1. Motion blur direction identification Theoretic direction(°)

90

80

70

60

50

40

30

20

10

Result(°)

90.36

81.29

70.82

58.20

51.24

41.28

31.61

21.69

11.78

Error(°)

0.36

1.29

0.82

-1.80

1.24

1.28

1.61

1.69

1.78

Theoretic direction(°)

0

-10

-20

-30

-40

-50

-60

-70

-80

Result(°)

-0.13

-10.88

-20.91

-30.59

-40.11

-50.86

-61.19

-71.93

-82.07

Error(°)

-0.13

-0.88

-0.91

-0.59

-0.11

-0.86

-1.19

-1.93

-2.07

Table 2. Motion blur direction identification with additional noise Theoretic direction(°) Result(°)

90

80

70

60

50

40

30

20

10

90.63

80.57

69.03

60.68

51.36

40.18

32.36

24.25

14.23

Error(°)

0.63

0.57

-0.97

0.68

1.36

0.18

2.36

4.25

4.23

-40

-50

-60

-70

-80

Theoretic direction(°) Result(°)

0

-10

-20

-30

2.24

-12.03

-24.35

-30.98

-40.70

-52.52

-61.11

-71.00

-81.95

Error(°)

2.24

-2.03

-4.35

-0.98

-0.70

-2.52

-1.11

-1.00

-1.95

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Seen from Table 1and Table 2, when motion blurring direction was parallel to original image edge, the blurred image still had strong boundary information, and its identification accuracy was high. On the contrary, if the blurred direction was vertical to original image edge, the boundary information was weak, the accuracy was low.

5 Conclusions Uniform linear motion blurring can strengthen the Fourier power spectrum of image perpendicular to the direction of motion. According to this, the blurred image can be removed a great deal of low and high frequency information after second-order difference high-pass filtering and the circular low-pass filtering. Then the motion blurring direction was attained by linear fitting of the frequency points with highest amplitude selected from the spectrum image. This proposed method is proved to be available for motion blur of random plane direction and adaptable for noise. Acknowledgments. The project supported by the foundation (No.20050019005 and No.200800191014) of Specialized Research Fund for the Doctoral Program of Higher Education (P. R. China).

References 1. Cannon, M.: Blind deconvolution of spatially invariant image blurs with phase. IEEE Transactions on Acoustics, Speech and Signal Processing 24(1), 58–63 (1976) 2. Chang, M.M., Tekalp, A.M., Erdem, A.T.: Blur identification using bispectrum. IEEE Transactions on Signal Processing 39(10), 2323–2325 (1991) 3. Savakis, A.E., Trussell, H.J.: Blur identification by residual spectral matching. IEEE Transactions on Image Processing 2, 141–151 (1993) 4. Yitzhaky, Y., Kopeika, N.S.: Evaluation of the blur parameters from motion blurred images. In: 19th IEEE Conference, pp. 216–219 (1996) 5. Yitzhaky, Y., Kopeika, N.S.: Identification of blur parameters from motion blurred images. Graphical Models and Iimage Porcessing 59(5), 310–320 (1997) 6. Yitzhaky, Y., Mor, I., Lantzman, A., et al.: Direct method for restoration of motion-blurred images. Journal of the Optical Society of America 15(6), 1512–1519 (1998) 7. Yitzhaky, Y., Milberg, R., Yohaev, S., et al.: Comparison of direct blind deconvolution methods for motion-blurred image. Applied Optics 38(20), 4325–4332 (1999) 8. Chen, Q.R., Lu, Q.S., Cheng, L.Z.: Identification of the motion blurred direction of motion blurred images. Journal of National University of Defense Technology 26(1), 41–45 (2004) (in Chinese) 9. Chen, Q.R., Lu, Q.S., Cheng, L.Z.: Identification of Motion-blur direction from motion blurred image via directional derivation using C spline interpolation and weighted average. Computer Engineering and Applications 29, 1–5 (2004) (in Chinese) 10. Chen, Q.R., Lu, Q.S., Cheng, L.Z.: Motion blur direction identification in motion blur image by Laplacian. Computer Applications 24(9), 4–6 (2004) (in Chinese) 11. Zhao, L., Jin, W.Q., Chen, Y.N., et al.: A New blind restoration of motion blurred images based on super-resolution method. Acta Photonica Sinica 37(11), 2355–2359 (2008) (in Chinese) 12. Lin, M., Li, C.H., Huang, J.H.: Parameters estimation of motion blurred images based on Radon transform. Computer Technology and Development 18(1), 33–36 (2008) (in Chinese)

Multi-agent Quality of Bee Products Traceability Model Based on Roles Yue E1, YePing Zhu1, and YongSheng Cao2 1 Research Institute of Agriculture Information, Chinese Academy of Agricultural Science, Key Laboratory of digital agricultural early warning technology of ministry of agricultural, 12, ZhongGuanCunNanDaJie,Beijing, China [email protected] 2 Institute of Crop Science, Chinese Academy of Agricultural Science, 12, ZhongGuanCunNanDaJie, Beijing, China

Abstract. Role is an important concept in research on how to develop method of Agent-Oriented software. In this paper, To study the background is bee product quality traceability system based on Multi-Agent, base on bee product quality critical control point, and describes a role-based multi-level framework of the method. Through Agent get different roles, and it get a group of services related to the process of quality of bee products tracing. At the same time, through the dynamic role change to realize the collaborative process between Agent management. Multi-Agent system model based on roles provide new methods and ideas about the control of the quality and safety of bee products. Keywords: Role, MAS, Bee Produces, Collaboration, Traceability.

1 Introduction Quality of bee products traceability including quality of tracking, quality tracing, the core of the information collection, tracking, monitoring and control. Bee Products (such as honey, Royal Jelly, etc.) from field to home, it have a number of links includes the acquisition of bee products, bee product processing, transport and sale, This process can be divided into bee breeding, daily management, honey collection, processing equipment, processing personnel, processing procedures, packaging, storage, transportation, honey varieties, environmental pollution, pest control treatment, collection tools, acquisition time, equipment, sanitation, materials, health status, technical levels and so on. Each link exists between each other the exchange of information. Due to geographical differences (the management are widely distributed in different regions), the time difference (there is a huge time difference about access to products, access to relevant information), making information management has become very complicated. From an engineering perspective, the bee product quality tracking and traceability system is a wide-area Internet-based technology and distributed artificial intelligence system. Agent technology can be used to solve the distributed system, it has many good features based on Agent and Multi-Agent technology application system, such as D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 110–117, 2011. © IFIP International Federation for Information Processing 2011

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initiative, intelligence, interaction, collaboration and mobility and so on. Therefore, the quality of bee products tracking and traceability system can be decomposed as a dynamic system, no matter how large the scale, in theory, the system can be broken down into a number of interrelated sub-systems (such as acquisition system, processing systems, transport systems, marketing systems, etc.). There are also relatively independent subsystems in quality management in the actual work, the bee for the production, acquisition, processing, transportation and sales departments have the corresponding entities. On the other hand, As a result of its own characteristics, Critical Control Point(such as pests and diseases, medication, heavy metals) play a very important role in the quality of bee products tracking and traceability system, the advantages and disadvantages of these entities directly related to the quality of bee products. These entities can completed their own tasks and goals to achieve optimal, they have independent thinking and ability to fulfill its mandate, they can cognitive state that all other entities of the current, and can accept the request and orders of other entities to change their behavior mechanism. In this paper, a multi-agent cooperative module is proposed, that is a role-based dynamic multi-agent cooperative module. This module introduces a role concept into the multi-agent cooperative system. It rebuilds a library of role information in every agent, thus realizing multi-agent dynamic cooperation.

2 Role and Binding Mechanism In general, Agent for the operation of a common goal, each Agent must continually analyze their environment, and to interact with each Agent in a multi-agent systems. At any time, there is a number of different external stimulate and behaviors need it to decision-making, and require a lot of time and communication resources. No doubt that this complex multi-Agent Learning System. In this case, a good solution is to give each Agent to determine their respective roles, each Agent has its own characteristics, and collaboration with the Agent is dynamically generated, dynamic constraints, thereby reducing traffic, improving the efficiency of learning. Based on this thinking, the article proposes a role-based dynamic multi-agent collaboration system. 2.1 Role Role concept from sociology, the basic point is limited to the role of individual behavior, the relationship between role limited the interaction between individuals in the system. In the study of multi-Agent systems, the role as a bridge connection with the micro-macro model, some researchers introduced the concept of the role of system analysis and design. In the multi-Agent system, to the role defined as follows[1]: 1. From the conceptual point of view, the role is a constraint. In this restriction, Agent involved in some of the interactions and evolution in some way. 2. From the implementation point of view, roles are some of the properties and behavior and binding on the Agent.

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The current study suggests that the relationship between the role and Agent with the following typical properties: 1. Dynamic: In the Agent life cycle, we can add new role and undo the original role. 2. Dependence: role of the Agent can not exist independently, it must rely on an Agent. 3. Multiplicity: the same Agent may assume different roles in the same period, and the same Agent may also take different roles in different times. As the Agent may assume different roles and bring a different point of view, the Agent reflect more comprehensive and detailed knowledge. 2.2 Binding Mechanism To coordinate the implementation of multiple Agent in the collaborative environment is a collaborative mission of the basic problems. In the Agent team, each Agent responsible for one or more role, through the dynamic transformation of role, the team can more effective implementation, in order to adapt to the environmental changes, to improve overall system performance. Agent role binding allows multiple Agent cooperative coordination in the implementation of tasks. In the multi-Agent system, it can take dynamic binding mechanism to achieve the transformation of role. Role binding is divided into three types: 1. Role Assignment: Agent play a new role after it complete the current mission. 2. The role of redistribution: Agent interrupt the current role and play another role. 3. Role of the exchange: Two Agent exchange of role in the implementation.

3 MAS Model Based on Role 3.1 Agent Model The model is multi-Agent collaborative based on the role. Functions of each part of Model are described below:

Fig. 1. Bee product quality traceability system Agent Model

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1. Apperceive is perceived external environment and internal state changes about Agent, and those changing circumstances the reactors. 2. Reactor receive the message from the apperceive, and those message submitted to the Execute. 3. Execute get message of the reactor, it obtained rules from Rules of conduct group, It obtained role from the role library if it need to add the role, It obtained data from the database if it need the data of Agent, and finally the results give Purpose. 4. Purpose is responsible for the task flow output. 5. Rules of conduct base is stored within the definition of the rules of the Agent. 6. Database stored data carried by Agent. 7. Role assignment is qualified for the role of verification and extraction and distribution about Agent. 8. The role base used to store the role definition. Each activity was defined as a single Agent in the process of traceability of the quality of bee products, the Agent has a role, and to collaborate and interact with other Agent. For example, buyer can be defined as the buyer Agent, the Agent role is to receive beekeepers request and respond, if agreed to acquire, the buyer Agent continue to process the request to processed Agent, and then processed Agent with other relevant Agent interaction. These Agent information exchange with the other Agent (such as beekeepers Agent, Sales Agent, etc.) through for communication interface, and then call the role-base specific role, analyse and process the exchange of information, thereby on exchange with the other Agent to respond. The exchange of information means that Agent of different task holders to the successful and satisfactory completion of an activity and exchange of information in the process of traceability of the quality of bee products. Behavior results is that the Agent based the role of the task, through the Agent exchange coordination with the requesting Agent, jointly with the other Agent to complete the activity behavior. The Agent through the role-base to describe it play the role, an Agent may have one or more roles, so the Model can adapt to changing roles in traceability of the quality of bee products system, strengthen the model agility and expansibility. For example, Processing Agent plays the role of the original honey production tasks, if the business process changes, the processing Agent in addition to played the original honey production tasks, coupled with the acquisition of the role of the buy honey, then just in the process the role-base Agent added to the original honey acquisition role, do not need to modify the traceability model. There are many Agent in traceability of the quality of bee products system, but in order to facilitate the discussion, we put them into an abstract class for the 3 main: decision-making Agent, Management Agent, Operation Agent. This classification is based on the multi-level framework principle, and consider the consistency of role assignment. Multi-level framework features reflect the structure between Agents, Superior Agent control subordinate Agent behavior, multi-level framework of multi-Agent system can satisfy the real-time communication.

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Fig. 2. Bee product quality traceability system architecture based on multi-level Framework

3.2 The Definition of the Role in Agent Agent of social behavior is essential for building multi-Agent Collaboration model, the role of Agent behavior abstraction, reflected status and interaction in group (organization or society). So we must set up an appropriate role model to study the social of Agent. In order to more fully and accurately reflect the role of the concepts, this paper defines the role model include obligation, authority, rule, activity, information, description. 1. Obligation is the role must fulfill obligations, it is the role in Agent assume tasks. 2. Authority is the role of responsibility for ensuring the realization, including the use of resources, access control permissions. 3. Rule is role must comply some norms, it also illustrates the role of supplementary obligations. 4. Activity is a group of private action about role, the implementation of these actions contribute to the role of its responsibilities. The so-called private action is implement these activities do not to involve interaction with other role. 5. Information is essential for each role domain knowledge and to help it achieve its role and function. 6. Description is the role of a conceptual description and introduction. 3.3 Role Dynamic Transformation The beginning of the process of traceability of the quality of bee products, systems based on the demand assigned role to Agent. Implementation process, Agent interact with others agent through its role of responsibility and authority, provide the appropriate services. But as the environment or demand changes, it was asked to provide more services, but also need some new services, Therefore, the role needs to be transformation. In order to achieve Agent dynamic transformation role in collaborative activities, the paper proposes role assignment methods to dynamic transformation role. One the one

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hand, the role assignment methods confirm role eligibility verification and appointed about Agent, On the other hand, the realization of the role to create, modify, delete, and matching management. The current role of the Agent set for the Role-Old, new role of dynamic transformation role for the Role-New, it called the goal role, and RoleData is role-base. Based on the above description, describe the Agent role transformation process is as follows: 1. Agent request a group of services to the role assignment. 2. The role assignment search the role-base, and get a group similar role, and then recommend to the Agent. 3. The Agent select the most suitable role as a target role, and request play the role to the role assignment. 4. The role assignment validate conversion relationships and play the role of eligibility verification about the Agent. 5. The role assignment delete the original role assigned, and to establish new the role of the designated subject and target. Thus, The role assignment help the Agent achieve the new target role and Agent dynamic transformation role in collaborative activities.

Fig. 3. Role Transformation

4 Example ∗ The first step: The problem of input, Agent initialization. The initial state, the system environment including user Agent, Management Agent. 1. user interaction Agent is the system and user interface, receiving user input of information, when the task is completed, the results displayed to the user. It create the role list base on the user information , and dissemination through the role bulletin board, and then wait for other Agent apply for the role. Now we assume the role list base included drug residues, processing technology, disease, three role notice.

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2. Management Agent is a shared communication area, at the same time, it responsible for the management and provision of resources. ∗ The second step: The resolve of decision making. It has been in the environment of the Agent, inquiry into the environment role bulletin board, looking for own functions and resources to meet the requirements of the role and to determine the application for it own role. This time, Agent have a real function ( Role-based Agent), such as drug Agent, Processing Agent, Disease Agent. ∗ The third step: The problem solving though Agent of collaboration. 1. The system started when all roles in role bulletin board application by Agent. 2. Due to the role of its "limitations", Agent not complete with its own functional role, such as Disease Agent running process, it need to generate better understanding of bee disease, its culture environmental information, but it is not ability to resolve, so the disease Agent apply to role bulletin board, on the one hand, it can join the existing operational environment to work together by generating new Agent (environmental Agent).On the other hand, completed Work Agent (such as: At this point, processing Agent has completed its mission in sleep state), according to "their" character (willingness) and the role-base functions, re-apply for new roles (environmental Agent) for system operation. 3. Management Agent real-time detection information in the role bulletin board in order to make the appropriate action (such as many Agent apply for a role, Agent work has been completed, etc.). 4. The results of each Agent through Management Agent to user interaction Agent. ∗ The fourth Step: The results of information integration.User interaction Agent get results to the user form a friendly man-machine interface. This cycle until the completion of the task. With process management, supply chain management, value chain development and modern information and communication technology revolutionary. Traceability has been given more meaning. Traceability has evolved into a dynamic supply chain management and enterprise application integration based on the product track and trace, it become main tools about data management, process management, process control, decision support. Application of Agent technology, Users will need to find the information to inform Agent, and let it into the appropriate environment and collaboration and completion of treatment. Such as quality of bee products traceability system, it achieve an intelligent and automated so that consumers from tedious search for the goods, browse, compare, select and negotiate such links. Therefore, it has important research value that Multi-Agent Quality of Bee Products Traceability Model Based on Roles.

5 Conclusion In this paper, multi-Agent system as a social organization, the tasks and objectives within the system is resolve the role, the Agent binding role to complete the mission

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and objectives. we achieve role transformation and collaboration based on role in this study, for further study of the relationship about Agent and role, roles and role relations and implementation mechanism.

Acknowledgements This research was supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science and Technology of China (nyhyzx07-041). National Natural Science Foundation about Agent-based quality control of agricultural products (60972154).

References 1. Li, Y., Ming, Z.W.: Intelligent and Cooperative Information Systems. Publishing of Electronics Industry, Beijing (2008) 2. Luger, G.F.: Artificial intelligence. Publishing of Electronics Industry, Beijing (2008) 3. Long, W., Yi, Z.: Key Techniques to Realize Cooperation of Mobile Agent in MAS Environment. School of Computer Science and Engineering 32(2), 158–163 (2009) 4. Trappey, A.J.C., Trappey, C.V., Hou, J.-l.: Mobile agent technology and application for online global logistic services. Industrial Management & Data System 104(1/2), 169–183 (2008) 5. Ming, Z.J., Peng, Z.X.: An agent-oriented requirement analysis and modeling method 4, 33–35 (November 2009) 6. Jing, Z.: Bee product quality and safety analysis of the key technology research and development. China Apiculture 57(12), 30–32 (2006) 7. DeLoach, S.A.: Engineering Organization-based Multiagent Systems. In: Garcia, A., Choren, R., Lucena, C., Giorgini, P., Holvoet, T., Romanovsky, A. (eds.) SELMAS 2005. LNCS, vol. 3914, pp. 109–125. Springer, Heidelberg (2006) 8. Chang, M.-H., Harrington Jr., J.E.: Agent-based models of organizations. In: Handbook of Computational Economics II (2006) 9. Yi, S.C.: Computing based on Agent. Publishing of Tsinghua University (2007) 10. Zhi, S.Z.: Advanced Artificial Intelligence. Publishing of Science (2006)

NIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM Shujuan Zhang, Dengfei Jie, and Haihong Zhang [email protected]

／

Abstract. In order to achieve non-destructive measurement of varieties in persimmon, a fast discrimination method based on Vis NIRS spectroscopy was put forward. A Field Spec 3 spectroradiometer was used for collecting 22 sample spectra data of the three kinds of persimmon separately. Then principal component analysis (PCA) was used to process the spectral data after pretreatment. The near infrared fingerprint of persimmon was acquired by principal component analysis(PCA) and support vector machine (SVM) methods were used to further identify the persimmon separately. The result of PCA indicated that the score map made by the scores of PCI PC2 and PC3 was used, and 8 principal components (PCs) were selected as the input of support vector machine (SVM) based on the reliabilities of PCs of 99 888 .51 persimmon samples were used for calibration and the remaining 15 persimmon samples were used for validation. A one-against- all multi-class SVM model was built, and the result showed that SVM possessing with the RBF kernel function has the best identification capabilities with the accuracy of 100 . This research indicated that the mixed algorithm method of principal component analysis(PCA) and support vector Machine(SVM) has a good identification effect, and can work as a new method for quick, efficient and correct identification of persimmon separately.

，

，

．％％

Keywords: Vis-NIR Spectroscopy, support vector machine (SVM), Persimmon, Principal component analysis (PCA).

1 Introduction Visible_Near Infrared Diffuse Reflectance (Vis_NIR) Spectroscopy analysis technology can be used for qualitative and quantitative data analysis with full band or multiwavelength spectrum. With many advantages of the spectra technology, such as dispense with pre-treatment, fast, pollution-free, no damage, multi-component analyzing synchronously, good reproducibility and online analysis, it has been widely used in quality detecting studies of agricultural products[1-10]. At present, some scholars have researched the Varieties of some kinds of fruit by near-infrared spectroscopy, for example, Peach [2] Soy sauce [3] Yogurt[4]and Juice [5]. But there is no research paper to study the varieties of persimmon. Persimmon, as a characteristic fruit of our country, has many characteristics, such as artistic contour, sweet, succulent and rich nutrition. It contains a lot of carotene, vitamin C, glucose, fructose, calcium, phosphorus, iron and other minerals. So

、

、

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 118–123, 2011. © IFIP International Federation for Information Processing 2011

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persimmon has very high culinary and medicinal properties. It can be used to eat as the fresh food, to make wine and to make vinegar. It also can be processed into persimmons biscuits, dried persimmons and persimmon cake. Therefore, the persimmon enjoys the “holy in the fruit” of reputation. In recent years, with the rural industrial structure adjustment, the persimmon has become an important source of increasing income for farmers in some areas. However, as the case stands, the domestic and foreign markets of fresh persimmon sales have not developed fully. As native products, the storage and grading of persimmon can not meet the market demands. Therefore, the developments and research of detecting technology for the quality of persimmon are important. In particular, Quality detection of persimmon for Simple, rapid, nondestructive is very necessary. Initial establishment of persimmon varieties of forecast model was calculated, using Near-Infrared Spectroscopy, based on principal component analysis and support vector machine combined data mining approach.

2 Materials and Methods 2.1 Software and Analysis Equipment The experiment equipment is composed by computer, spectrometer, and halogen lamp and correction whiteboard. The spectrum sampling has been collected by diffuse reflectance methods using the FieldSpec3 spectrometer made by America ASD (Analytical Spectral Device) company. The interval of spectral sampling is 1nm, the range of sampling is 350 ~ 2500nm, the times of scanning are 30, the resolving power is 3nm and the view angle of probe field is 25°. The halogen light of 14.5V is used to match with the spectrometer. Spectral data is exported into the form of ASCII code for processing and the analysis software is the ASD View Spec Pro, Unscramble V9.7, Matlab7.3 and DPS (Data Procession System for Practical Statistics). 2.2 Sample Source and Spectral Data Acquisition Three kinds of persimmon including “covered persimmon”, “Cynanchum persimmon” and “red persimmon” have been purchased from the market. The 22 samples have been collected for each kind of persimmon and the total are 66 samples. All the samples are randomly divided into modeling sets including 51 samples and prediction sets including 15 samples. The spectral data are collected by spectrometer located at the top of persimmon. The distance between the top of persimmon and spectrometer is 50mm, the view angle of probe field is 25° and the time for scanning each sample is 10. 2.3 Spectral Data Preprocessing In order to remove the high-frequency random noise, baseline drift, sample asymmetry and the impact of light scattering, the move average smoothing method with 9 smoothing points was used for spectral pre-processing, which can remove the high frequency noise effectively. Then the data was processed by MSC (Multiplicative Scatter Correction) [6]. All of the pretreatment process carried out in the Unscrambler V9.6. The typical near-infrared spectroscopy curve of three kinds of persimmon is shown in Fig.1.

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0.7 Disc-shaped persimmon

0.6 )R /1 0.5 gl (e 0.4 cn ab 0.3 ro sb A 0.2

Bovine heart-shaped persimmon

0.1 Red persimmon

0 350

850

1350

1850

2350

Wavelength(λ/nm) Fig. 1. Visible_Near infrared reflectance spectroscopy of three different varieties of persimmon

2.4 Principal Component Analysis of Three Varieties of Persimmon Spectral curve has a larger noise form the first side to the end. Spectrum of 350 ~ 2050nm band has been selected for eliminating noise. PCA (principal component analysis) was used in this study. In the Fig.2, X-axis samples the PC1 (first principal component scores), Y-axis samples the PC2 (second principal component scores), Zaxis samples the PC3 (third principal component scores). Classification performance

Fig. 2. Principal Component scores scatter plot (PC1×PC2×PC3) of persimmon

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of persimmon is shown from the Fig.2, and the characteristics curve of three different varieties of persimmon was described. However, the distinction wasn’t obvious between the edges of the sample. To improve prediction accuracy, the prediction model of three persimmons was established using SVM and PCA in this experiment.

3 Test Results and Analysis 3.1 Principal Component Analysis Sample spectral bands have total 2150 points from 350 ~ 2500nm.Using full spectrum, computation is large, spectral information is weak in some of the regional samples and correlation is lack between the composition of the sample and the nature. The purpose of PCA is data reduction. Under the premise of more variable spectral information without loss of participate, the original variables are replace using a smaller number of new options, and many information of overlapping chemical are excluded[11]. Therefore, the Unscramble V9.6 software is used, principal component of three persimmons were analyzed. The accumulated credibility of first 8 principal components of was got in Table 1.It has reached 99.888%. So the original wavelength can be interpreted by the 8 principal components. Table 1. Accumulative reliabilities of the first 8 Principal components PCs

PC1

PC2

PC3

PC4

PC5

PC6

PC7

PC8

Accumulative reliabilities /%

84.988

95.515

97.742

99.301

99.550

99.722

99.821

99.888

3.2 Support Vector Machine Identification of Persimmon SVM is a new pattern recognition method. The principle risk minimization of structural is uses, Training error and generalization are both, and many advantages are expressed in solving small sample, nonlinear, high dimension and local minimum problems. The 51 samples of the principal component scores as SVM training set, the remaining 15 samples were taken as the prediction set of SVM, and the radial basis function (RBF) is used as a core function to identify varieties of persimmon. Optimal parameter is found using Grid-search function. Namely, main parameters of gamma (γ) and the RBF of kernel function in the sig2 (σ2) are found. The appropriate gamma (γ) can increase the ability of SVM model. The parameter sig2 (σ2) can control the error of regression function, and directly affect the noise immunity of the SVM model. Generalization ability, as learning and prediction of the model, are largely determined by those two parameters. The SVM is realized by the Matlab 7.3. Classification performance of the RBF kernel function was listed in the Table 2. From Table 2, the forecast error of the SVM model was less than ± 0.1, and the recognition rate has reached to 100%. Compared with the conventional model of discriminate analysis, the SVM model of near infrared spectral classification was more accurate.

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Sample number

Real value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 1 1 1 1 2 2 2 2 2 3 3 3 3 3

Predicted value 0.9608 1.0566 1.0131 0.9829 1.0191 2.0263 1.9957 2.0493 1.9331 1.9269 2.9930 3.0981 3.0272 2.9040 2.9606

Bias -0.0392 0.0566 0.0131 -0.0171 0.0191 0.0263 -0.0043 0.0493 -0.0669 -0.0731 -0.0070 0.0981 0.0272 -0.0960 -0.0394

4 Conclusion Identification of persimmon varieties were studied based on Near Infrared Spectroscopy visible. On the basis of Visible-near infrared spectroscopy and spectral data pretreatment, the variety identification model of persimmon was established using PCA and SVM. The Principal Components of PCA as input set, the Gaussian radial basis (RBF) kernel function of SVM as choose, the model of varieties of persimmon has been established. Experimental results show that the model prediction error of unknown varieties of persimmon was less than ± 0.1, and the recognition rate has reached to 100%. The model was accurate, predicted better results, and improved the speed and accuracy. New way of persimmon detection was provided for future spectroscopy.

Acknowledgements Funding for this research was provided by Shanxi Provincial Department of Science and technology (NO.2007031109-2).

References 1. Zhao, J., Zhang, H., Liu, M.: Non-destructive determination of sugar contents of apples using near infrared diffuse reflectance. Transactions of the Chinese Society of Agricultural Engineering 21(3), 162–165 (2005) 2. Li, X., Hu, X., He, Y.: New approach of discrimination of varieties of juicy peach by Near Infrared spectra based on PCA and MDA model. Journal of Infrared and millimeter waves 25(6), 417–420 (2006)

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3. Tong, X., Bao, Y., He, Y.: Study on Fast Discrimination of Brands of Soy Sauce Using Near Infrared Spectra. Spectroscopy and Spectral Analysis 8(3), 597–601 (2008) 4. He, Y., Feng, S., Li, X., et al.: Study on Fast Discrimination of Varieties of Acidophilous Milk Using Near Infrared Spectra. Spectroscopy and Spectral Analysis 26(11), 2021–2023 (2006) 5. Zhang, H., Zhang, S., Jie, D., et al.: Research on Varieties of Sea Buckthorn Juice by NearInfrared Diffuse Reflectance Spectroscopy Based on the PCR and PLS. Journal of Shanxi Agricultural University (Nature Science Edition) 30(1), 46–48 (2010) 6. Li, G., Zhao, G., Wang, X., et al.: Nondestructive measurement and fingerprint analysis of apple texture quality based on NIR spectra. Transactions of the Chinese Society of Agricultural Engineering 24(6), 169–173 (2008) 7. Zhang, S., Wang, F., Zhang, H., et al.: Detection of the Fresh Jujube Varieties and SSC by NIR Spectroscopy. Transactions of the Chinese Society for Agricultural Machinery 40(4), 139–142 (2009) 8. Li, X., He, Y., Qiu, Z.: Application PCA-ANN Method to Fast Discrimination of Tea Varieties Using Visible/Near Infrared Spectroscopy. Spectroscopy and Spectral Analysis 27(2), 279–282 (2007) 9. Liu, Y., Sun, X., Chen, X.: Research on the Soluble Solids Content of Pear Internal Quality Index by Near-Infrared Diffuse Reflectance Spectroscopy. Spectroscopy and Spectral Analysis 28(4), 797–800 (2008) 10. Wang, G., Zhu, S., Kan, J., et al.: Nondestructive Detection of Volatile Oil Content in Zanthoxylum Bungeagum Maxim by Near Infrared Spectroscopy. Transactions of the Chinese Society for Agricultural Machinery 39(3), 79–85 (2008) 11. He, Y., Li, X., Shao, Y.: Discrimination of Varieties of Apple Using Near Infrared Spectra Based on Principal Component Analysis and Artificial Neural Network Model. Spectroscopy and Spectral Analysis 26(5), 850–853 (2006) 12. Li, G., Wang, M., Zeng, H.: An Introduction to Support Vector Machines and other Kernel-Based Learning Methods. Publishing House of Electronics Industry, Beijing (2004) 13. Deng, N., Tian, Y.: New Method of Data Minning_Support Vector Machine. Science Press, Beijing (2004)

One Method for Batch DHI Data Import into SQL-Server A Batch Data Import Technique for DateSet Based on .NET Liang Shi* and Wenxing Bao School of Computer Science and Engineering, Beifang University for Ethnics, Yinchuan Ningxia, P.R. China 750021 [email protected]

Abstract. In the cow breeding process, the users also pay attention to the cow’s DHI information, especially in the keen market competition, if they want to improve the milk production, to improve the value of the milk price, to test analysis the information of DHI is of the utmost importance. However, the traditional DHI information often determines and returns the result to users by the Excel by a professional DHI department. Users only can open the tables one by one to inquire and analysis the data, but also when users need to compare with the recently month data, the have to open a large quantity of Excel tables so that it’s not only waste times but also waste energy. This treatise design a method which is based on the .NET environment’s DataSet method to implement the batch import for the DHI in the Excel into the database, it’s also achieve the breeding information management system and the data of DHI integrated management. The application results show that this method is easy to operate and credibility of operation, it can also achieve the history information inquire and it is worth practically generalizing. Keywords: batch import, database, cow breeding, DHI.

1 Question There is a common issue for every farm on how to improve the economic benefits by making use of the information software for cultivation during their daily cattle breeding work. Nevertheless, by just putting into place the cultivation software, although the staff expense could be reduced relatively, the productivity improved as well as a convenient and handy practical operation achieve, current software for cultivation management are basically covering every step of daily work, which means there is quite a few concerns the information of DHI. That is because DHI must be granted by the authorized institutes and after that the result could then be returned. However, the result that goes back to the farms is in the format of Excel. The culturist has to open them one by one to check and analyze the data. In order to have a comparison with the data months ago, sometimes the culturist has to open several excel files and therefore it will be inconvenient for information searching. The problem is that this kind of operation stands against the target of cultivation management, which takes time and costs because the culturist has to spend a lot time on DHI information analyzing. Therefore, the question is that if there is a method for the user to facilitating data *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 124–130, 2011. © IFIP International Federation for Information Processing 2011

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reading and managing as easy as the way he uses the cultivation management software, hence a considerable boom on his productivity. From a global view, among all those domestic management software for cattle cultivation, the one developed by NanJing FarmStock Technology Co., Ltd. has the function mentioned above. From the aspect of the completion of different functions, it works well. But from the view of handling convenience, it is not good enough, which makes the amateur uses feel inconvenient. Another implementation method is to use the import and export programs provided by Microsoft SQL-Server or other three-party software. However that requires the culturist having some professional knowledge. [1] Therefore if is possible to implement the function by just clicking a button for the amateur users? In this paper we provide a method which realizes a batch import for DHI data by clicking the button in the interface of the management system for the users. After that many operations could be carried on including automatic search, data analysis etc. The users will be attracted by the friendly interface feeling easy and convenient.

2 Noun Introduction in Paper A. DHI DHI is the abbreviation of “Dairy Herd Improvement” and also known as “Test for Production Performance”. There have been similar institutes among the developed countries in dairy industry like Canada, USA, Holland, Sweden and Japan. DHI reports are capable of providing decision-making supports for the management on dairy farm as well as complete and accurate resource for breeding task. In China the DHI testing system was founded in 1994, which has the milk supervision centers located in Xi’an, Shanghai and Hangzhou co-built by domestic relative organization and a SinoCanadian integrated breeding programs. [2] The aim of DHI popularization is to accomplish a specific and effective management on farms trough plenty of data analysis, which will have a global view along with clear individual performance to replace the extensive and empirical model of cattle breeding based on data and scientific management by testing the productivity of each cow. [3] B. Microsoft Excel The Microsoft Excel is one of the Microsoft Office modules, which is kind of tabling software that is designed and runs on the OS of Windows and Apple Macintosh. Its handy operation model gives amateur users the capability of data storage and data analysis that are applied into their daily work. C. Introduction to Language SQL and the Database of SQL-Server SQL is the acronym for Structured Query Language. SQL is the general language for all database systems. Users could be able to run same operations trough SQL sentences on different database systems. SQL-Server is a relative database managing system. It is embedded with sets of BI tools that provide data management at enterprise level. Its database engine provides a more reliable storage function for relative and structured databases, which gives the

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user the capability of constructing and managing data applications of high proficiency for his business. D. DataSet in .NET DataSet is the core concept of ADO.NET. DataSet can be recognized as a database in the memory, it is a set of isolated data and doesn’t rely on the database. DataSet is also the main module in ADO.NET, which acts as a cache in the memory for the data collected from the data source. [4] DataSet runs on memory. It improves the data R-W speed and protects the disk well. Therefore we have profoundly facilitated applications with high running speed and stability. 1. Isolation. DataSet is isolated from many sorts of data sources. 2. Offline (disconnected) and connected. 3. The object of DataSet is data view that can be illustrated in the format of XML. It is a view to show the relations between data.[5]

3 The Program Design and Efficiency Analysis 3.1 The Flow Chart for System Design

Fig. 1. The flow chart for system design

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3.2 Specific Procedures to Achieve and the Main Pseudo-code to Interpret ExecleDs(string filenameurl

，

string table)

/*customized function, import the Excel data into DataSet*/ {OleDbConnection conn = new OleDbConnection(strConn); /*Connect to Excel data engine*/

，

OleDbDataAdapter odda = new OleDbDataAdapter("select * from [Sheet1$]" conn); /*current selected data sheet*/ odda.Fill(ds return ds;

，

table);

/*add data into the data set*/

/*return the data set*/

} clicked() {/*execute the inserting trough click event*/ try

/*define the exceptions*/

{DataSet ds = ExecleDs(savePath, filename); the customized function*/ DataRow[] dr = ds.Tables[0].Select(); DataRow array*/

/*call

/*define a

for (int i = 0; i < dr.Length; i++) {/*read and evaluate the excel field insert the evaluated data into the database, use insert into to make it implemented*/ string insertstr = "insert into cyjlb" of the information into the sheet*/

/*batch import

SqlCommand cmd = new SqlCommand(insertstr, cn); /*connect to database*/ cmd.ExecuteNonQuery();

/*execute*/

} catch (Exception ex) { } /* catch the system exception, if there is any exception, then display the information*/ } }

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Fig. 2. The result

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3.3 Program Running Result The users only need to select the import date, click the import button and then the data in Excel file will be imported into the database. The summary and imported information will be shown in the list. Click each cell to show the details. We can also search data by import month as well as other conditions. It is convenient for users to compare data of months and the system can generate charts based on imported basic information, which is a similar function that Excel has. 3.4 The Program Efficiency Analysis Although we can program for a data import into SQL-server database, the coding work is very complicated. Besides when handling a large quantity of data, the writing speed could be reduced. However, if we use DataSet as an intermediate buffer component, we will save a lot of import time. It is also need one for loop, and the time complexity is O(n). Thus the efficiency will be improved. It is proved that, after testing and using the system, trough this method we can save plenty of time for data batch import.

4 Application Analysis The DHI information is most needed by the culturists during their work. It also reflects the milk output and the economic benefits that a farm has. It gives users an easy way to do many sorts of searching if we have DHI information batch imported into the database. Meanwhile, the methodology of batch data insert into the database is a good reference for programmers. In this way the users will no more need to know any professional tools and they just click a button, hence the data stored in Excel files is batch inserted. And at the same time, this methodology provides a data import way that makes the agricultural information compact, the users free from learning professional knowledge and importing data only based on basic data, which therefore makes time saved and the productivity improved.

5 Conclusion The technique of data batch import is very important in practical information construction and development. It is a key to make operations easy for the amateur users. This paper provides a method for data batch import with the support of the DHI import function applied in cattle cultivation systems, which is so practical that makes the amateur users that are not majored in computer satisfied to do the operation of heavy data import. Meanwhile, this technique shows a method and acts well as a reference for programmers to implement data batch import into databases.

Acknowledgement This work is supported by the National Key Technology R&D Program of China under Grant NO. 2007BAD33B03.

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References 1. Mingrisoft: Visual C# Development Technique Comprehensive Work, pp. 533–534. Posts & Telecom Press, Beijing (2007) 2. Shuzhen, T., Yuezhou, Z.: The organization, implementation and application of DHI. J. China Dairy Cattle (2), 35–37 (1999) 3. DHI-effective tool for dairy management, http://www.dahlj.com/gl_nnpdsc.asp?lb=86 4. Troelsen, A.: Pro C# with.NET 3.0, Special Edition, pp. 649–650. Apress, New York (2007) 5. DataSet, http://baike.baidu.com/view/624618.htm?fr=ala0_1

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas* Yu Fu1, Jianhua Yang1, and Tingting Zuo2 1

College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, P.R. China {fuyu-1986,yang.haag}@163.com 2 Tianjin Electric Power Corporation, Tianjin, 300010, P.R. China

Abstract. Small-sized hybrid wind-hydro-solar power generation systems may be designed to solve the power supply problem in some rural areas. Optimal design models are developed to design the hybrid generation systems including battery banks and to provide the optimum system configuration. It is ensured that the annualized cost of the systems is minimized while satisfying the required loss of power supply probability. A genetic algorithm is used to find the optimum configuration. Moreover, four membership grades of the systems, such as the reliability, the economical efficiency, the complement and the environmental benefit, are created by a linearly-weighted fuzzy algorithm to search the configuration. The optimal algorithms are applied to a practical project. The optimal design of the project is gotten and analyzed, which shows that the complement and the environmental benefit are taken full advantage of, and the corresponding system cost is minimized with enough power supply reliability. Keywords: hybrid renewable energy system, loss of power supply probability, genetic algorithm, linearly-weighted fuzzy algorithm.

1 Introduction Wind, hydro and solar energy are inexhaustible, renewable and clean energy sources. Some remote areas cannot be supplied by large power grids; however, there are enough resources of wind, hydro and solar energy available. Therefore, hybrid power generation systems can be built by making full use of the complement of these nondepletable energy sources to solve the power shortage problem in the areas. Present researches of hybrid power generation systems focus mainly on the combination of photovoltaic (PV) arrays and wind turbines [1, 2], and the best compromise point between system power reliability and system cost is rarely considered in their design. In this paper, the mathematical models of optimal sizing design for the smallsized hybrid power generation system are given. The wind, hydro and solar energy sources are used in the system. Based on the models, a genetic algorithm (GA) and a linearly-weighted fuzzy algorithm are used to get the optimum system configuration *

Supported by National Key Technology R&D Program for the 11th Five-Year Plan (2006BAJ04B03).

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 131–138, 2011. © IFIP International Federation for Information Processing 2011

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that can achieve the required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS).

2 Models of Hybrid System Components and Battery A hybrid wind-hydro-solar power generation system consists of PV arrays, wind turbines, hydro turbines, battery banks, an inverter, a controller, cables and other accessory devices. In order to predict the hybrid system performance, each component needs to be modeled and then the generation system can be evaluated to meet the load demand. The models of three generation components of the system including wind turbines [3], hydro turbines and PV arrays [4] are proposed in Ref. [5]. Then, the output power at a given time can be calculated according to the weather data [5, 6]. The battery [7] can only be charged to rated capacity, and be limited by the maximum permissible depth of battery discharge when discharging. Therefore, modeling the charging-state of battery is necessary. At any time the state of battery is related to the previous state of charge and to the energy production and consumption situation of the system during the time from t-1 to t [8]. During the charging process, when the total output of the generation units, i.e. the PV modules, the hydro generators and the wind generators, are greater than the load demand, the battery bank capacity available at time t can be described by

Ebat (t ) = Ebat (t − 1) ⋅ (1 − σ ) + ( E g (t ) − E L (t ) / η inv ) ⋅ ηbat .

(1)

where Ebat(t) and Ebat(t-1) are the battery bank capacity available at time t and t-1, respectively, ηbat is the battery efficiency, σ is the self-discharge rate of the battery bank, Eg(t) is the energy generated by the generation units, EL(t) is the load demand at time t, and ηinv is the inverter efficiency. On the other hand, when the load demand is greater than the energy generated, the battery bank is in discharging state. Therefore, the battery bank capacity available at time t can be expressed as E bat (t ) = Ebat (t − 1) ⋅ (1 − σ ) − [ E L (t ) / η inv − E g (t )] .

(2)

At any time, the storage capacity is subject to the following constraints:

E bat min ≤ E bat (t ) ≤ Ebat max .

(3)

where Ebatmax and Ebatmin are the maximum and minimum allowable storage capacity, respectively. Ebatmax can be taken as the nominal storage capacity Cbatn, and Ebatmin can be given by Ebat min = DOD × Cbatn . where DOD (%) represents the maximum permissible depth of battery discharge.

(4)

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas

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3 Power Reliability Model Based on LPSP Concept LPSP [8, 9] is defined as the probability that an insufficient power supply results when the hybrid system, including the PV arrays, the wind turbines, the hydro turbines and the battery storage, is unable to meet the load demand. Values of LPSP range from 0 to 1. A LPSP of 0 means that the load is always satisfied, and a LPSP of 1 means that the load is never satisfied. LPSP can be expressed as

∑ {E ( t ) − [ E T

LPSP =

L

t =1

g

( t ) + Ebat ( t − 1 ) − Ebat min ]η inv } T

∑ EL ( t )

.

(5)

t =1

where T is the operation time, normally T is one year. When LPSP is computed, the input data set consists of daily or hourly solar irradiation on a horizontal surface, the mean values of the ambient temperature, the wind speed and the water current velocity, the load power demand during the year, the technical specifications of the system components, and so on.

4 Economic Model Based on ACS Concept ACS [10] is composed of the annualized capital cost Cacap and the annualized maintenance cost Camain. The cost of five main components needs to be considered, such as the wind turbines, the hydro turbines, the PV arrays, the battery and the other devices. Then the ACS can be expressed as ACS = C acap + Camain .

(6)

The annualized capital cost of each component takes into account the installation cost, and is calculated by C acap = C cap ⋅

i ⋅ (1 + i ) Yproj . (1 + i ) Yproj − 1

(7)

where Ccap is the initial capital cost of each component, Yproj is the component lifetime, i is the annual real interest rate. The lifetime value Yproj of some devices can be referred to Ref. [11]. The annualized maintenance cost can be estimated by two different ways. First, under normal circumstances, the maintenance cost of wind turbines within service life is 20% to 25% of the total cost, while the hydro turbine is 10%, the PV module is 5%, and the battery is 10%. Second, it can be considered to be a static value which depends on practical statistics.

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5 System Optimization Model with GA GA is an advanced search and optimization method [12, 13] and is generally robust in finding global optimal solutions, particularly in multimodal and multi-objective optimization problems. An optimization model is developed here to obtain the optimum size or the optimal configuration of a hybrid wind-hydro-solar system including a battery bank in terms of the LPSP technique and the ACS concept using GA. The flow chart of the optimization process is illustrated in Fig.1. ,QLWLDOJXHVVRI 3:73+7339&EDW

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+\EULGJHQHUDWLRQV\VWHPPRGHO )LWQHVVIXQFWLRQHYDOXDWLRQ ˅/363 /363WDUJHW˗˅0LQ$&6YDULDWLRQV

&ULWHULRQUHDFKHG˛

1R 6HOHFWLRQRSHUDWLRQ˖PLQLPXP$&6 &URVVRYHUDQGPXWDWLRQRSHUDWLRQRI*$ 1HZJHQHUDWLRQRIFRQILJXUDWLRQ 2SWLPDOFRQILJXUDWLRQRI 3:73+7339&EDW

Fig. 1. Flow chart of the optimal sizing model using GA

The decision variables in the optimization process are the wind turbine power PWT, the hydro turbine power PHT, the PV module power PPV and the battery capacity Cbat. Daily or hourly weather data of typical year includes the solar radiation on the horizontal surface, wind speed and water current velocity, and so on. Combining the ACS with the LPSP based on a penalty function, the fitness function Ffitness can be expressed as F fitness = ACS + M ⋅ [max(0, LPSP − 0.5%)]2 .

where M is penalty factor.

(8)

Optimal Sizing Design for Hybrid Renewable Energy Systems in Rural Areas

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The power outputs of the generation units are calculated by using their specifications as well as the weather data. The system configuration is then optimized by employing the genetic algorithm, which dynamically searches for the optimal configuration to minimize the fitness function. For each system configuration, LPSP of the system is examined to judge whether the requirement, i.e. LPSP target, can be satisfied. Then, the configuration of smaller fitness is subjected to the following crossover and mutation operations of GA in order to produce the next generation population until the criterion is satisfied. The optimal configuration can finally be found by achieving the lowest ACS while satisfying the LPSP requirement.

6 System Optimization Model with Linearly-Weighted Fuzzy Algorithm The system optimization model based on linearly-weighted fuzzy algorithm [5, 14] considers four optimization objectives: the reliability, the economical efficiency, the complement and the environmental benefit. Each configuration has four membership grades according to the four objectives: r = (r0 , r1 , r2 , r3 ) .

(9)

Different users have different requirements to the four objectives, so a set of weight is created: A = {a 0 , a1 , a 2 , a 3 } .

(10)

Thus, the objective function is expressed as min f = r0 ⋅ a 0 + r1 ⋅ a1 + r2 ⋅ a 2 + r3 ⋅ a3 .

(11)

When the objective function is minimized, the optimal configuration is found. The membership grade of reliability is the same as the LPSP, and the membership grade of environmental benefit is proposed in Ref. [5]. 6.1 Membership Grade Model of Economy

Based on the ACS model, the cost per kWh of the hybrid generation system can be expressed as CkWh =

ACS . E year

(12)

where CkWh is the power generation system cost per kWh, Eyear is the annual energy production of the hybrid generation system.

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The membership grade function of economy is:

μ1 = 1 −

CkWh . Cacp

(13)

where Cacp is maximum cost per kWh acceptable to users. 6.2

Membership Grade Model of Complementary

The complementary of the hybrid system is measured by the difference in magnitude between the power consumption needed by the load and the total energy production generated by the generation units. The membership grade function of the complement can be expressed as

μ3 =

min( E year , E yearL ) . E year + E yearL

(14)

where EyearL is the total load power consumption for one year.

7 Result and Discussion The proposed methods are applied to design one demonstration project, which is built to supply power for a village committee in a remote village of a county, north China. The village is centered at 40°23′ north latitude and 116°52′ east longitude, 71.8 m above sea level. At the measured site, the effective wind power density is 43.26 W/m2, the average annual wind speed is 2.37 m/s, the average annual solar radiation is 5545.6 MJ/m2. The useful waterfall height is 65 m, the average water current velocity is 0.036 m3/s in the summer and autumn. Based on the models of the hybrid wind-hydro-solar generation systems developed in this paper, the optimal configuration of the system fulfilling the requirements is found out. The calculation results are shown in Table 1, where method 1 is the algorithm based on GA, and method 2 is the algorithm based on the linearly-weighted fuzzy algorithm. Table 1. Optimal sizing results Results of method 1 Wind Turbine Power (W)

2036.1

Hydro Turbine Power (W)

2925

Results of method 2 1000

×2

3000

2×23 ×

PV Array Power (W)

731.5

3

Battery Bank Capacity (Ah)

469.2

120 4

ACS (RMB Yuan)

8627.99

7265

LPSP

0.4991%

0.4810%

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Optimal sizing results of method 2 are the specifications of the products available from manufacturers. As shown in Table 1, similar optimal configurations of the two methods are gotten. But the annualized cost of system of method 2 is smaller because environment benefit is in consideration. The design of the hybrid generation system is not simply a combination of wind, hydro and solar generation units. In order to make the most efficient use of energy resources and save investment, optimal methods in consideration of complementary characteristic among wind energy, solar energy and hydro energy should be used to find a scientific and reasonable configuration of each unit. At the same time, a battery bank is absolutely necessary to ensure system power reliability. Without it, the load demand can hardly be met continually. The LPSP concept used in this study is a statistical parameter decided by users. It is clear that higher power reliable systems are more expensive than lower requirement systems. A smaller LPSP, i.e. higher reliability of the systems, results in high cost of the system and vice versa. Choosing an optimal system configuration according to the system power reliability requirement can save investment and avoid blind capital spending. Moreover, the loss of load probability [15] may be used as the reliability criteria for the hybrid renewable energy system, too. Similar results can be gotten.

8 Conclusions The optimum design sizing methods for the hybrid wind-hydro-solar systems based on the genetic algorithm and the linearly-weighted fuzzy algorithm are developed. The two methods can be used to search the system optimum configuration which can achieve the desired LPSP with minimum annualized cost of system. Taking requirements of the users and environmental benefit into consideration, the model based on linearly-weighted fuzzy algorithm is more adaptive to different designing condition. The proposed methods are applied to design a demonstration project of the hybrid wind-hydro-solar system. With the daily measured field data of the studied project in one year, the energy contribution of the generation units, the battery working state and the energy balance of the system are investigated. The good complementary characteristic among wind energy, solar energy and hydro energy is found out.

References 1. Tina, G., Galliano, S., Raito, S.: Hybrid solar/wind power system probabilistic modeling for long-term performance assessment. Solar Energy 80(5), 578–588 (2006) 2. Markvart, T.: Sizing of hybrid PV-wind energy systems. Solar Energy 59(4), 277–281 (1996) 3. Rosen, A., Sheinman, Y.: The average output power of a wind turbine in a turbulent wind. Journal of wind Engineering and Industrial Aerodynamics 51(3), 287–302 (1994) 4. Datta, M., Senjyu, T., Yona, A., Funabashi, T., Kim, C.H.: Photovoltaic output power fluctuations smoothing methods for single and multiple PV generators. Current Applied Physics 10(2), 265–270 (2010)

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5. Zuo, T.-T., Yang, J.-H., Shao, B.-R.: Multi-objective optimal design of hybrid windhydro-solar power generation system. Journal of Agricultural Mechanization Research 21(9), 25–28 (2008) (in Chinese) 6. Yang, H.-X., Burnett, L., Lu, J.: Weather data and probability analysis of hybrid photovoltaic–wind power generation systems in Hong Kong. Renewable Energy 28(11), 1813– 1824 (2003) 7. Guasch, D., Silvestre, S.: Dynamic battery model for photovoltaic applications. Progress in Photovoltaics: Research and Applications 11(3), 193–206 (2003) 8. Diaf, S., Diaf, D., Belhamel, M., Haddadi, M., Louche, A.: A methodology for optimal sizing of autonomous hybrid PV/wind system. Energy Policy 35, 5708–5718 (2007) 9. Ai, B., Yang, H.-X., Shen, H.: Optimum sizing of PV/wind hybrid system (I) CAD method. Acta Energiae Solaris Sinica 24(4), 540–547 (2003) (in Chinese) 10. Yang, H.-X., Zhou, W., Lou, C.-Z.: Optimal design and techno-economic analysis of a hybrid solar–wind power generation system. Applied Energy 86(2), 163–169 (2009) 11. Chen, Y.-M.: Fixed assets directory of State Grid Corporation. China Electric Power Press, Beijing (2005) (in Chinese) 12. Bala, B., Siddique, S.A.: Optimal design of a PV-diesel hybrid system for electrification of an isolated island—Sandwip in Bangladesh using genetic algorithm. Energy for Sustainable Development 13(3), 137–142 (2009) 13. Yang, H.-X., Zhou, W., Lu, L., Fang, Z.-H.: Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. Solar Energy 82(4), 354–367 (2008) 14. Jeong, K.S., Lee, W.Y., Kim, C.S.: Energy management strategies of a fuel cell/battery hybrid system using fuzzy logics. Journal of Power Sources 145(2), 319–326 (2005) 15. Deshmukh, M.K., Deshmukh, S.S.: Modeling of hybrid renewable energy systems. Renewable and Sustainable Energy Reviews 12(1), 235–249 (2008)

Overall Layout Design of Iron and Steel Plants Based on SLP Theory Ermin Zhou, Kelou Chen, and Yanrong Zhang School of mechanical and electrical engineering, East china jiaotong University, Nanchang, China

Abstract. Based on the total production process of iron and steel plant, and with the general design requirements for the major operating units, the major operating sites’ overall layout relationship of the iron and steel plant is determined. Based on the production workshops, auxiliary plants, power plants, transportation facilities, the functions of departments, subsidiary production and living services departments' within and outside association, the situation of materials' into and out, systematic optimization layout planning is proposed. With the size and position relationship of the operating units, and other relative limiting condition and common practices, overall layout design of the factory is developed. Keywords: Systematic layout planning, SLP, Iron and steel plant, Overall layout.

1 Introduction Many steel companies in China are currently conducting or planning a new expansion of production base, for the internal and external logistics of steel plant are volume, and the status of a serious lack of iron ore, scientific selection of the steel plant site, rational distribution of the various operating units. To find the best spatial location of all facilities for the plant, the enterprise's materials, equipment and human resources are efficiently used, improve the utilization of various resources, to make the most productive, to catch up with the trend of China's iron and steel enterprises development and to fulfill the requirements of a large number of imported iron ore and the special region development.

2 The Work Sites Overall Layout of Iron and Steel Plants 2.1 The Principle of Overall Layout of Iron and Steel Plants Site in the established arrangement of plants within the various venues, to adapt to urban planning and industrial development zone request, local natural conditions combined rational use of land, fully arranged in various venues, and create synergies. (1) to adapt to urban planning; (2) the rational use of land; (3) The arrangements D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 139–147, 2011. © IFIP International Federation for Information Processing 2011

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for venues; (4) to create collaborative conditions; (5) the effective protection of the environment. 2.2 The Design Requirements of the Major Operating Units (1) Production areas (plant): iron and steel plant production area is the main part of the overall layout, should give priority to select the appropriate location. According to the nature of the steel production plant, A larger percentage of transportation outside the factory, so should close to interface points of the main mode of transportation outside the factory: stations, terminals, roads in the vicinity; production area is large consumption of water and electricity facilities, should close to the water supply and Power supply; Production areas are also produced pollution venue, should be installed in the minimum space required frequency of clean wind upwind side; around the production area may be set aside for the development of room for expansion. (2) Transport Facilities Location: When using the waterway transport outside the factory, we must select the appropriate to the domestic terminal building sites, particularly the use of raw materials for large ships dock, to be near the production area, and a reasonable convergence conditions. As much as possible make the unloading of materials directly to the production areas, finished packing can be directly transported to the terminal. When use the rail transportation, Should on the basis of convergence conditions, logistics direction, working conditions of the transfer, to select a similar size of the marshalling factories, should leaves the factory to the possible expansion of the scale synchronization. Production areas and cities, terminals, stations, water sources, sewage treatment plants, substations, residue field, construction site, residential sites and adjacent areas such as enterprise collaboration should be roads. (3) Water and power facilities: the water sources of iron and steel plants are mainly come from the natural water sources such as river or sea. Either surface or groundwater sources, if used as domestic water, should leaves health protection regulations required distance to prevent pollution. Sewage treatment plant should layout in the direction of water discharge, close to the water into point. Treated sewage discharge outlet should be located downstream of water points, and has more than 100m from the health protection. (4) Health Protection belt: between steel mill plant and other sites or adjacent enterprises, according to the concentration of pollutants emitted by factories, according to local environmental assessment requirements set a certain width of the area of health protection. In the zone, according to functional requirements for green, light pollution, the facilities can be arranged, but not often live in housing arranged. (5) Java games: slag and industrial waste disposal sites should be selected in the main plant slag and industrial waste discharge direction, make full use of lowlying land, wasteland, valley and beach land. Stockpiling sites may require unified planning, acquisition phases, the stockpiles of the early stage generally not less than 10 years. Slag and industrial waste should be fully comprehensive utilization, save resources, reduce the impact of keeping occupied land, which is the trend of development. (6) Residential area: subject to health protection standards and does not affect plant development, should close the factory layout. Between residential areas and the

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plant should not be arranged the railway and busy highways, residential roads across the region can not be transited, between neighboring cities, passenger stations, terminals and the plant should be roads. (7) Construction Site: construction site set up for the construction of various facilities. Therefore, its location should choose the fixed end of the factory building, and plant a certain distance between the left does not affect plant development. To facilitate the use of permanent rail, road, water, electricity and other facilities.[2] Based on the above basic principles and general layout of the design requirementsof the major operating units, combined with Baosteel Zhanjiang New Area in the special conditions and the geographical situation of the factory location, environmental factors, to determine the overall layout of the various sites on the iron and steel plant, shown in figure 1.

Fig. 1. Steel plant general layout diagram of venues

3 Systematic Layout Planning of Iron and Steel Plant 3.1 SLP Method Introduction Systematic Layout Planning was proposed by Richard • Muther of American, and had been widely used. This approach applies not only to the factory layout design and production systems, also can be used for logistics centers, shops, hospitals, schools, and other service facilities layout design [3]. When the SLP method is applied to the overall layout of industrial facilities design, commonly used procedure is to analyze the relationship between production process, and then the logistics of the operation unit and non-flow analysis, and make a weight, to obtain a comprehensive relationship between them, Finally, according to the intensity of the various operating units draw their location diagram.

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3.2 Production Process In the iron and steel plant, supply of raw materials and fuel from raw materials prepared workshops to the sinter plant and coking plant workshop, Produced sinter and coke been supplied to the iron workshop, supply the production of hot metal to steel-making plant, supply the liquid steel continuous to casting plant, supply the production of steel ingot to steel rolling plant, Shipped out the production of finished steel products, form of the whole plant production processes. The relationship between the workshop is the production process relations, we must keep this process, in general can not be changed. 3.3 Logistics Relations (Quantitative Relations) The design according to an annual output of 10 million tons of steel to consider, according to the scale of production, material consumption indicators and quality of materials to calculate the materials number of input and output. According to the quantity of material flow between the production plants, power plants, ancillary facilities, residential areas, establish the material flow start and end table, letters on behalf of the workshop(A-Raw material, B-Sintering, C -Coking, D-Iron, E -Steel, FRolling, G- Thermoelectricity, H-Oxygen, I- Gas, J- Substation, K- Region, L- Residential), show in table 1. Table 1. Material flow start and end table (ten thousands tons / year) END A

B

C

D

E

A

1640

670

370

120

B C

28

F

G

H

I

J

K

L

START

D E

310

1560 465

140

945 965

F G H I J K L

270

87 20 15

15

20

According to material flow start and end table, according to the amount of the whole plant logistics distribution, material flow can be divided into six relations level, show in table 2.

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Table 2. Material flow classification table Serial number 1 2 3 4 5 6

Material flow (ten thousands tons / year) Largest logistics >1000 Large logistics 600̚1000 Logistics moderate 300̚600 amount of Logistics small 50̚300 amount of Logistics less <50 No amount of 0 logistics

Relationship level A E I O U X

According to material flow start and end table and material flow classification table, set out the logistics amount, material name and relationship level between the workshop, and then according to the logistics amount and the relationship level, drawn logistics relationship diagram, shown in Figure 2. From the logistics relationship diagram can be seen the relationship level of each workshop, just the proximity degree, and as a basis for layout.

Fig. 2. Logistics relationship diagram

3.4 Non-logistics Relationship Steel plant is basically the non-logistics relationships are the following: (1) interference of environmental pollution; (2) security aspects, such as fire, explosions, and traffic accidents, etc.; (3) Energy supply and demand requirements; (4) ease of production management, as the case may change. Relationship between level of non-logistics division, according to the qualitative factors identified above, need to study in detail the relationship between the various workshops, in accordance with the hierarchy of importance of the relationship, show in table 3.

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E. Zhou, K. Chen, and Y. Zhang Table 3. Non-logistics relationship levels Nature of the relationship Absolutely necessary to close specially needs to close Need to close Generally to close Unimportant Do not need to close

Relationsh ip level A E I O U X

Explain Consider merger Requirement to close Demand to close A certain distance Not limited to distance No relationship or interference

Steel plant relations of non-logistics factors, just close to ground level, generally there are following several, show in table 4. Table 4. Non-logistics relationship factors Code

relations of non-logistics factors

1

The requirements of production management relations Power supply requirements

2 3

Security and environmental protection

4

Maintenance and support facilities requirements

According to Table 3, and the importance of their classified nature of relations between requirement levels, rendering non-logistics relationship diagram, shown in figure 3.

Fig. 3. Non-logistics relationship diagram

Overall Layout Design of Iron and Steel Plants Based on SLP Theory

Fig. 4. Integrated relationship diagram Table 5. Relations dense representation Level

Coefficient

Line express

A

4

Absolutely necessary

E

3

Particularly important

I

2

Important

O

1

General

U X

0 -1

Unimportant Unimportant or do not want to

(No tag)

Close degree

Fig. 5. The location-related map of operating units

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3.5 Comprehensive Relationship The overall layout of the steel plant is main by the logistics relations, supplemented by non-logistics relationships, both to reduce operating costs, reduce production costs and ensure the safety and the environment clean. Considering the logistics relations and non-logistics relationship, to set the level values: A = 6, E = 5, I = 4, O = 3, U = 2, X = 1; between the logistics relations and non-logistics relationship weight by 2: 1 calculation; calculate the value of the integrated relationship and divide into six grades by size, A: 18, E: 14 ~ 17, I: 11 ~ 13, O: 8 ~ 10, U: 4 ~ 7, X 3, according to the calculated integrated relationship-level value and the relationship between their level, rendering comprehensive diagram, shown in figure 4. By the more comprehensive relationship, the relationship between the operating units to use as shown in table 5 to determine the connection type, mapping the location of the various operating units of the relevant plan, shown in figure 5.

4 Determine the Overall Layout of the Steel Plant 4.1 Determine the Area of Main Workshops Reference the range of available data from design department, accessibility to consider the development of space, roughly determine the plant land area of the main workshop of annual capacity of 10 million tons steel (with green and the surrounding road), show in table 6. Table 6. The main workshop area of land (ten thousands m2) Serial number

Shop name

Area

1

Materials workshop

320

2

Sintering plant

120

3

Coking plant

180

4

Iron plant

150

5

Steel workshop

150

6

300

7

Rolling shop (plate, thread, tubes, wheel, etc.) Thermal power station

8

Oxygen station

20

9

Gas Station

40

80

4.2 The General Layout Plan to Determine The above analysis of various aspects, according to the main production plant and the operating relationship between the location of units, reference auxiliary plant, power plant, residential area, port, railway station interface, and finally determine the overall layout of the design of steel plant design, shown in figure 6.

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。

Fig. 6. The overall layout plan steel plant 1-raw materials;2-sintering;3-Coking;4-iron;5steel;6-rolling;7-Thermal;8-Oxygen;9-gas; 10-Transmission;11-the total area;12-residential and commercial areas;13-Terminal;14-raw materials Station; 15-the entrance port;16-Trans Station;17-export terminal;18-finished products;19-Sports;20-artificial lake; 21-Development Reserve;22-Railway;23-lawn,flower beds, etc.; 24-bush;25-residue field;26-water station.

5 Summary Due to the large flow of materials in steel plant, logistics factors in the overall layout design accounts for a large proportion, therefore, this design started centers around the steel mills logistics factor. From the operating unit focused on the logistics relationship and both of non-logistics relationships, obtained comprehensive relations, and by the operating unit of area, green requirements to make the overall layout plan steel plant. Emphasis analyzed from the two aspects of logistics and non-logistics relationship between the various operating units, obtained comprehensive relations, and by the operating unit of area, green requirements to make the overall layout plan of steel plant. The overall layout of the steel plant is difficult to achieve perfection, for the specific internal and external conditions, natural and geographical factors and regional development, etc. Harmful gases and waste water is also not possible have no blunder, more serious pollution should be taken into account, such as focus on solving the sulfur, carbon gases and water.

References 1. Liao, Z.Y.: Industrial Construction General Graphic Design. China Building Industry Press, Beijing (1982) (in Chinese) 2. The preparation group: Steel mills general plan reference, Metallurgical Industry Press, Beijing (1978) (in Chinese) 3. Sun, X.Q.: Systematic Layout Planning of Logistics Center Design. Science and Technology Theory and Management 10, 117–119 (2005) 4. Liu, G.F., Chen, X.L.: Systematic Layout Planning in the production system optimization, vol. 3, pp. 125–128 (2008)

Performance Forecasting of Piston Element in Motorcycle Engine Based on BP Neural Network Rong Dai College of Engineering and Technology, Southwest University, Chongqing, China [email protected]

Abstract. The piston performance affects the performance of the Motorcycle. In order to forecast the piston performance effectively, the performance forecast model based on BP neural network is presented. According to the characteristics of piston performance, the training samples are made up of the orthogonal experimental data, which are utilized to ensure higher generalization of BP neural network. And then the trained BP neural network is used to forecast test example. At the same time, the value of test samples is compared with the output of BP network model results. The results show that the output precision of BP neural network is high, and using the BP neural network to forecast the piston performance is practicable and effective. Keywords: Motorcycle, Engine piston, BP neural network, Forecast.

1 Introduction Piston of motorcycle engine is an important part. Its design that is good or bad is related to the engine fuel consumption, power, oil consumption, noise and many other indicators. In the practice of design piston, piston is often developed through repeated tests and experiments relying on experience, this method requires a long development cycle and more cost. Design errors of other theoretical approaches are worse, and they are not competent for the practice of piston design. Parameters that are affecting the performance of pistons are more, their values are difficult to determine. In order to seek a better performance of the piston, and often orthogonal test approach can be adopted for experimental design and analysis. Orthogonal test method not only can reduce the times of tests, but also can fully reflect the impact of each factor to achieve more reasonable optimization program with less cost. Although the prediction method based on neural networks can not determine the certain mathematical function, but it can give the algorithm and the structural parameters, the connections rights that inherent laws between input and output are reflected are determined by training, the network output can be predicted through them. The neural network can not only predict the experimental results, but also establish the foundation for performance simulation analysis. Because of possessing these advantages, many scholars have conducted the application research in neural network forecasting, such as literature [1-3]. Based on the above analysis, this paper uses BP neural network method to predict the performance of motorcycle engine piston. In order to overcome the shortcomings D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 148–157, 2011. © IFIP International Federation for Information Processing 2011

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that BP network is difficult to be trained, network can be trained by using orthogonal experimental data to reduce the training samples and to improve the quality of training, and the output of network is analyzed comparatively to verify the accuracy of the network.

2 BP Neural Network 2.1 BP Neural Network Structure The basic element of neural networks is processing nodes, after adding the input obtained from other nodes with a weight value, which produces the output value through the activation function. Processing nodes are organized into a layer, which generally associated with next layer fully, and there is no interconnection in one layer, as is shown in Fig.1. The input layer as inputting data to the network has not any operations in this layer. After the input layer, followed by one or more actual processing layers, the final processing layer is called the output layer that provide output data, between the input layer and output layer is a layer called the hidden layer. With multi-layer structure (usually three), this structure can allow the network to identify the nonlinear data.

Fig. 1. BP neural network

Using neural network to process nonlinear data includes two phases: training and recall. The typical process is training network by back-propagation algorithm, until the error value between desired output and actual output of the network reach the objective minimum. Once training is completed, the network can process data through the recall as a non-linear processor [4-6]. 2.2 BP Learning Rule BP algorithm consists of two parts: signal forward transmission and error backpropagation. In the forward course, the input signal pass to the output layer from the

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input layer, hidden layer. the state of neurons in each layer only influences the state of next layer neurons. If the output layer has not the expectable output, then calculated the error of output layer, and then turned back propagation through the network connection, the error signals along the original path are anti-passed back to modify the weight of every layer neurons to achieve the desired goal. BP network, the whole learning steps:

① ② ③

Initialization, to the various connection weights Wij, Vjt and the threshold θj, γt, i = 1,2, ... n; j = 1,2, ... P; t = 1,2, ... q; k = 1, 2, ... m, gives the random value between [-1, +1] .

[

]

[

k k k Randomly selects a model couple: Ak = a1 , a 2 ,...a n , Yk = y1 , y 2 ,... y n for the network.

[

k

k

k

]

]

Using the input mode Ak = a1 , a 2 ,...a n , the connection weights Wij and the threshold θj calculate the input value Sj (activation value)of middle layer neurons, then using Sj through the activation function

f ( x) =

k

k

k

1 1 + e−x

(1)

Calculate the output value bj of each element in the middle layer.

b j = f (s j )

(2)

n

s j = ∑ Wij ⋅ a i −θ

④

j

i =1

Where:

Using the output value bj of middle layer, the connection weights Vjt and the thresholds γt, calculate input value lt (activation value) of each element in the output layer, then using lt calculate each element's response ct of the output layer by activating function,

c t = f (l t )

Where:

⑤

lt =

p

∑

V

jt

⋅b

j

− γ

j =1

t

(3)

(t = 1,2, ... q)

[

]

Using the expectable output mode Yk = y1 , y 2 ,... y n , the network actual output ct, calculates correction error

(

)

k

k

k

k t

d of each element in the output layer:

d tk = y tk − ct ⋅ ct (1 − ct )

(t = 1,2, ... q)

(4)

Performance Forecasting of Piston Element in Motorcycle Engine

⑥

With d t , Vjt, bj ,calculates the correction error of the middle layer:

⎡q ⎤ e kj = ⎢∑ dt ⋅ V jt ⎥b j (1 − b j ) ⎣ t =1 ⎦

⑦

151

(5) (j = 1,2, ... p)

k

With d t , bj, Vjt, γt ,calculates the new connection weights and thresholds between middle layer and output layer next time:

V ji ( N + 1) = V ji ( N ) + a ⋅ d tk ⋅ b j

(6)

γ t ( N + 1) = γ t ( N ) + a ⋅ d tk

(7)

Where, N is times for learning, a is learning coefficient.

⑧

⑨ ⑩

ek a k

With j , i , Wij, θj, calculates the new connection rights between input layer and middle layer next time:

Wij ( N + 1) = Wij ( N ) + β ⋅ e kj ⋅ aik

(8)

θ j ( N + 1) = θ j ( N ) + β ⋅ e kj

(9)

③ ③

Randomly selects the next learning model couple for the network, return to the step, until all m-model couple have been trained completely. Again from the m-learning couple randomly select a model couple , return to the step, until the network global error function E is less than a pre-set limit value (network convergence), or learning times are more than the pre-set value ( network failed to converge). For the whole input mode, the network's global error E: m

E = ∑ Ek = k =1

⑪

1 m 1 m q (Yk − c) 2 = ∑∑ ( y tk − ct ) 2 ∑ 2 k =1 2 k =1 t =1

(10)

End of the study In the above learning steps, - as "forward propagation process" of input learning mode, - as "back-propagation process" of the network error, and as the completion of the training and the convergence process. Fig. 2 shows BP network learning process diagram.

⑦⑧

③⑥

⑨ ⑩

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start Connection weight and threshold initialization Provide learning model couple to network Calculate input and output of middle layer neurons Calculate input and output of each cell in the output layer Calculate correction error of each cell in the output and middle layer Adjust connection weight between middle and ouput layer ,adjust output threshold of output layer neurons Updating learning and input mode couple

no

all m-model couple are training completely yes Updating learning times no

Global error<E or learning times>N yes Study end

Fig. 2. BP network learning process diagram

2.3 Improvement of the Training Function BP network training adopts gradient descent method in order to obtain minimum in error flat, which has its limitations and inadequacies, mainly reflected in: 1) the learning rate is too small lead to the training time was too long; 2) local minimum. In order to overcome the slow convergence of the algorithm, there are many scholars who have done research and made a number of improved BP algorithm, such as the weight adjustment algorithm with momentum term, namely, momentum term is added in the weight adjustment algorithm formula (11), (12):

V ji ( N + 1) = V ji ( N ) + η (V ji ( N ) − V ji ( N − 1)) + a ⋅ d tk ⋅ b j

(11)

Performance Forecasting of Piston Element in Motorcycle Engine

Wij ( N + 1) = Wij ( N ) + λ (Wij ( N ) − Wij ( N − 1)) + β ⋅ e kj ⋅ aik

153

(12)

3 Performance Prediction Model for Engine Piston Based on BP Neural Network 3.1 Construction of Prediction Model Selecting a 4-cylinder gasoline engine piston as a research object, the piston diameter about 47.49 ~ 65mm, piston skirt length about 50.1mm, the piston head has burning chamber. According to the theoretical calculations and past experience, consideration in the design should be focused on the clearance between the piston skirt and cylinder liner, the clearance between piston head and cylinder liner, eccentricity of piston pin, length of piston skirt, cylinder liner surface roughness RMS(root-mean-square) for the impact of friction power, noise, power and fuel consumption[7]. In the orthogonal experimental design, selecting experimental factors and levels is shown in Table 1according to work experience. In the orthogonal test, we should try to select the smallest orthogonal table [8], which can reduce the workload of sampling and trial. So orthogonal experiment of the piston selects L16 (45) orthogonal table, such as Table 2.The number of test samples is 16, the times of tests is also 16 less than full-factorial test times:45 =1024, which reduce 1008 times (at the same time reducing 1008 samples) and greatly reduce trial costs, time and workload. To find out the influence of various factors for the indexes, the test results in Table 2 (measured value)are analyzed, the difference is greater, the impact of factors on the indicators is the greater. Such as friction power, the difference analysis result is shown in Table 3. According to the difference value sorting in decreasing, the order of various factors affecting friction power is obtained: A> B> C> D> E. The smaller is the friction power, the better is performance. The optimal project of friction power indicator is that: A4B4C3D2E3. Not only the times of orthogonal tests can be reduced, but also the synthetical impacts of different factors on performance can be fully realized through it. However, the implicit knowledge that is implied in these data need to be mined further in order to improve the system intelligent level. Table 1. Factors and level table factors clearance clearance between length of eccentricity of between piston piston head and piston skirt piston pin level skirt and cylinder cylinder liner/µm /mm /mm liner/μm B D C A 1 10 40 0.05 40 2 50 140 0.55 47 3 90 240 1.05 54 4 140 340 1.55 61

cylinder liner surface roughness RMS /µm E 0.10 3.10 6.10 9.10

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Table 2. Piston orthogonal experiment results and BP neural network training and testing samples and learning results measured values

based on BP network

No. A B C D E friction fuel friction power Noise power consumption power /kw dB(A) /kw (g/kw.h) /kw 1 1 1 1 1 1 2.93 64.04 259.3 108.1 2.9308 2 1 2 2 2 2 2.68 64.17 259.2 108.2 2.6781 3 1 3 3 3 3 2.4 64.1 259.5 106.8 2.3994 5

2 1 2 3 4 3.04

64.06 259.6

108.2 2.3293 108.1 3.04

6 7 8 9 10 11 12 13 14 15 16

2 2 2 3 3 3 3 4 4 4 4

64.07 259.7 65.02 258.5 6.3.85 259.1 64.02 259.6 63.99 259.6 64.37 259 64.18 259.2 63.78 260.6 63.78 259.2 64.19 259.5 64.09 259.5

108.5 108 108.2 108.3 108.2 108.1 108.4 107.6 108.2 108.6 109.2

4

1 4 4 4 4 2.33 2 3 4 1 2 3 4 1 2 3 4

1 4 3 3 4 1 2 4 3 2 1

4 1 2 4 3 2 1 2 1 4 3

3 2 1 2 1 4 3 3 4 1 2

2.93 2.43 2.17 2.52 2.36 2.23 1.92 1.77 1.7 1.61 1.41

63.02 259.5

Fuel Power Noise consumption /kw dB(A) (g/kw.h) 64.04 259.30 108.1032 64.17 259.197 108.1968 64.098 259.502

106.872

63.07

259.4975

108.1992

64.238 259.6004

108.1032

2.9308 2.4304

63.874 259.697 65.018 258.521

108.4968 108.000

2.1696 2.5200 2.3603 2.2299 1.9202 1.7686 1.7001 1.6121 1.4390

63.88 64.018 63.99 64.372 64.18 63.78 63.78 64.06 64.09

108.1992 108.300 108.1992 108.1008 108.4008 107.5992 108.1992 109.404 109.2

259.1006 259.6004 259.6004 258.0019 259.1972 260.6 259.1993 259.4282 259.4996

Table 3. The results of difference analysis Friction Power A

B

C

D

E

K1

2.585

2.565

2.375

2.245

2.267

K2 K3 K4 R

2.643 2.257 1.623 1.020

2.417 2.167 1.958 0.607

2.313 2.197 2.222 0.178

2.212 2.302 2.347 0.135

2.260 2.255 2.325 0.070

In the working of the piston, its performance (friction power, noise, power and fuel consumption) mainly is affected by the factors those are the clearance between piston skirt and cylinder liner, clearance between piston head and cylinder liner, eccentricity of piston pin, length of piston skirt, cylinder liner surface roughness RMS. Therefore, the friction power, noise, power and fuel consumption are investigated as the indexes. The performance of piston is comparatively analyzed through testing, then the structure of BP neural network prediction model is finally identified as: 5×11×4 (respectively, the input layer nodes, hidden layer nodes and output layer nodes).

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The input variable of every node in the input layer are respectively the clearance between piston skirt and cylinder, or clearance between piston head and cylinder liner, or eccentricity of piston pin, or length of piston skirt, or cylinder liner surface roughness RMS. The output variables of every node in the output layer respectively are friction power, or noise, or power, or fuel consumption. Training samples will affect the generalization ability of the network, which is generally selected to reflect the circumstances and relation, and can serve as an effective adjustment of connection weights. Orthogonal test method adopts "orthogonal table" to arrange a multi-factor experiment and analysis scientifically. Its advantage is mainly that in a lot of testing projects(also known as test conditions), a few testing projects owing to the strong representation are selected, Each testing project has a strong representation to reflect fully the general situation in the region preferred. Present research [9] also shows that, because orthogonal test method is a balanced dispersion and neat comparability, network structure constructed by using orthogonal test method can effectively reduce training samples and improve the training accuracy. Therefore, in order to select the small training samples for containing enough information, prediction model structure of piston performance based on BP network adopts No. 1 to NO.14, NO.16 samples in Table 2 to construct learning samples. At one time, NO.15 sample is tested as a predictor, not used for training the network. Learning samples, prediction sample and learning outcomes are shown in Table 2. 3.2 Implementation of BP Neural Network Prediction Model Learning samples (input, output) in the Table 2 are normalized, xbji = (xji-xji.min) / (xji.max-xji.min), in the equation, xbji is NO. i input variable of NO. j sample in the normalized samples, xji is NO. i input variable of NO. j sample in the raw data.; xji.max and xji.min are respectively the maximum and minimum of NO. i input variable of NO. j sample in the raw data. To enable the data uniformly distributed in [0,1] ,then neural networks can be trained, activation functions of the hidden layer and output layer are also Sigmoid function (see formula (1)). Learning coefficient between input layer and hidden layer is taken 0.6, learning coefficient between hidden layer and output layer is taken 0.5, momentum coefficient is taken 0.6, the global error E is taken 0.001, error function tend to stabilize after 4416 times iteration, BP program is shown in Fig.3, the final learning outcomes are shown in Table 2. At the time, according to the connection weights and the threshold determined by the network model to reflect the inherent law of input samples, the corresponding prediction model will be obtained, and thus parameters and network structure determined by the model is regarded as the actual prediction model parameters. After the above algorithms constantly adjust the parameters of the network, complicated function that affects indicators and performance can be obtained to meet the accuracy requirements. Such a function can not be expressed in simple function expression, which can only be stored in the computer in the form of a network with weight value and threshold as parameters.

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Fig. 3. BP neural network training and prediction

Table 4. Prediction results of BP network

Based on BP Network Sample No. 15

Friction power relative error (%) 0.13

power relative error (%) 0.20

fuel consumption relative error (%) 0.027

noise relative error (%) 0.74

Predictive testing sample is utilized to predict in the trained BP network, the relative errors between prediction results and measured values are shown in Table 4. It is discovered that in Table 2 and Table 4, fitting accuracy and prediction accuracy based on BP network are high, the relative errors are less than 1% to meet the required precision. Because the neural network can approximate a nonlinear function to any precision, it can establish the function among the friction power, power, fuel consumption, noise and various factors. Network prediction results also show that the forecasting performance of the established network is better, performance of the engine piston can be well predict, to a certain extent, which can improve prediction accuracy.

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4 Conclusion To a large extent, performance of piston directly impact on the motorcycle engine, there are many factors affecting the performance of the piston, and their values are more difficult to determine, so effective ways are urgently needed to find for identifying the reasonable combination values of the various factors, so as to shorten the product development cycle. Based on this, the relationship between factors and performance indicators is established through the use of BP neural network, which has been a high precision network output. The foundation for the simulation analysis of various factors is established through the BP neural network prediction model that has been trained, i.e. the impact of different combination on performance has been studied. The results show that adopting BP neural network approach to predict the performance of the piston is feasible and effective. Acknowledgement. This work is supported by “the Fundamental Research Funds for the Central Universities.”(N0.XDJK2009C005) and supported by “the Doctoral Fund of Southwest University”(N0.SWU109043).

References 1. Jiang, Y.H., Zheng, X.D., Feng, L.: Application of Artificial Neural Network to the Saccharification Technology of Monascus Waxberry Wine Optimization. Systems EngineeringTheory & Practice 5(5), 136–140 (2003) 2. He, S., Xiong, G.L., Zeng, Q.L.: A Method for Estimating Product Assembly Cost Based on Neural Networks. Mechanical Science and Technology 21(4), 662–665 (2002) 3. Genel, K.: Application of Artificial Neural Network for Predicting Strain-life Fatigue Properties of Steels on the Basis of Tensile Tests. International Journal of Fatigue 26, 1027–1035 (2004) 4. Wang, X., Wang, H., Wang, W.H.: Artificial neural network theory and applications. Northeastern University Press, Shenyang (2000) 5. Guo, D.Y., Liao, X.Y., Lei, W.Y.: Neural Network and Its Application in Mechanical Engineering. Chongqing University Press, Chongqing (1998) 6. Gao, J.: Artificial Neural Network Theory and Simulation Instance. Mechanical Industry Press, Beijing (2007) 7. Wang, F.Z., Liu, Y.B., Yue, D.P.: Orthogonal Test and Design of a Diesel Piston. Small Internal Combustion Engine and Motorcycle 36(6), 56–59 (2007) 8. Chen, K.: Experiment design and analysis. Tsinghua University Press, Beijing (1996) 9. Huang, K., Chen, S.F., Qi, X.: Neural Network Optimal Design Based on Orthogonal Experiment Method. Systems Engineering-Theory Methodology Application 13(3), 272–275 (2004)

Performance Monitoring System for Precision Planter Based on MSP430-CT171 Lianming Xia, Xiangyou Wang*, Duanyang Geng, and Qingfeng Zhang School of Agricultural and Food Engineering, Shandong University of Technology Zibo, Shandong, China [email protected], [email protected], [email protected], [email protected]

Abstract. Based on full consideration of the working conditions in the filed, a monitoring system for precision planter was designed using the technology of sensor, single-chip Microcomputer and wireless transmission. In this system, photoelectric sensor and MSP430 were used to detect the performance of the seeder-metering device and process the data respectively. It mainly detected the miss index, multiple index, seeding amount, seeding rate, the plant spacing and other performance parameters. In the sound-light alarm system, a indicator light was used to show the fault type and location, then the detected data and fault can be displayed on the LED. In order to acquire and analyze the data at realtime, a wireless transmission system based on Bluetooth was designed. FSBT485Abluetooth module was used to communicate with the host computer. To ensure the uniformity of drilling and sowing, a stepping motor was used to control the seeding rate in the laboratory. The results showed that reliability of the sound-light alarm system was 100% and the photoelectric sensor could detect more than 95% seeds passed it. Keywords: Monitoring system, Precision planter, photoelectric sensor, Singlechip microcomputer, Bluetooth technology, Stepper motor.

1 Introduction The seed-metering device is the core component of precision planter. Precision planting is defined as the placement of single seeds in the soil at desired plants spacing [1, 2]. The seed-metering devices which are often used can be divided into two categories such as mechanical and pneumatic. Whether either seed-metering device is used, the flow process of the seeds is all invisible. So, we can not determine whether the sowing quality is good or not only by vision and hearing. With the sensors, the intelligent monitoring system for precision planter can monitor the sowing process and alarm in different ways. Once the faults happen, the system can tell the location and kind of the faults timely. So, it is of great economic and practical significance for studying the monitoring technology for precision seed-metering device. By far, we could monitor the metering quality by the methods such as manual *

Corresponding author.

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monitoring, photoelectric effect, piezoelectric effect and high-speed photography [3, 4 and 5]. Based on the technologies of single-chip microcomputer, sensor, information and virtual instrument, the monitoring systems for precision planter have gained great progress in recent years. In this paper, a performance monitoring system for precision planter based on MSP430 is designed.

2 Structure and Working Principle of the System In this system, the infrared photoelectric sensor is installed near the furrow opener. In order to better guarantee the accuracy of monitoring, three or four photoelectric sensors are installed on the seed tube symmetrically and evenly. Every seed tube represents one row and an indicator light is used to show the performance of the planter [6, 7]. When the pulse signals produced when the seeds pass through the sensor are transmitted to single-chip microcomputer, it decides whether the pulse signals are normal or not. Sound and light alarm signals will be sent out when seeds absence, the grain box is empty and the metering devices are blocked. The indicator light is extinct when the planter is at normal operation and the light will blink when there is something wrong with the planter such as seeds absence [8]. The schematic is showed in the Fig.1.

Fig. 1. Structure of the monitoring system

3 Design of the Monitoring System The monitoring system is mainly composed of photoelectric sensor, alarm system, display system and wireless data transmission system.

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3.1 Design of Power Circuit In order to work under normal conditions, MSP430 must be supplied with 3.3 voltages by the system. Because of the unstable voltage of the 12v level on the precision planter, it is necessary to be converted to 3.3v. The power circuit is showed in Fig. 2.

Fig. 2. Power circuit of MSP430

3.2 Circuits of the Sensors The photoelectric sensor system is composed of photoelectric coupled device and LM339. The coupled device is composed of infrared LED and phototransistor. LM339 is the integrated circuit of four voltage comparator. When the seeds pass through the range covered by the LED, the phototransistor will close and up level is output. Then the level will be low after filter by LM339. The situation is contrary when there is no seed passing through the range covered by the LED. The output pulse signals are connected to port P2 which is the external interrupt of MSP430 [9]. The circuit of the photoelectric sensor is showed in Fig.3.

Fig. 3. Circuit of the photoelectric sensor

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3.3 Design of the Sound and Light Alarm Circuit The system will alarm when the grain box is empty, the seed tube or the furrow opener is blocked by debris and the metering devices stop working. At the same time, the indicator light will blink to tell the driver that there is something wrong with the planter. The circuits are showed in Fig. 4 and Fig. 5.

Fig. 4. The circuit of sound alarm

Fig. 5. The circuit of light alarm

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3.4 The Circuit of Display The function of the display circuit is to show the data and indicate faults. A LCD with better backlight effect is used in order to display the screen contents clearly. In this system, we choose LMC240128ZK as the display module. The LCD module has 8-bit parallel data interface and it is directly connected to the pin of MSP430. The pins of the module can be divided into data pin, control pin and power pin. Its circuit is showed in Fig. 6.

Fig. 6. The circuit of display

3.5 The Wireless Data Transmission System In this paper, a wireless data transmission system based on Bluetooth is used because of its high efficiency, reliability and security. The system is composed of signal collection and transmission units, MSP430, the hopper computer and the Bluetooth communication device. The key of the system is to choose the Bluetooth module. In this system, the Bluetooth module used is FS-BT485A serial adapter [10, 11]. The block diagram of the wireless data transmission system is showed in Fig. 7.

Fig. 7. The block diagram of the wireless data transmission system

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4 Design of the Software System The function of the software system is: To initialize the single-chip microcomputer and other chips; to call data collection sub-program; to call sound and light alarm sub-program when there is something wrong with the planter; to detect whether the amount of sowing is appropriate; to adjust the sowing speed and metering speed to keep them consistently; to call the sound and light alarm sub-program; to display the data on the LCD for the driver to watch or reference; to analyze and store all the performance parameters; to read the data on the hopper computer freely; to call the sub-program of wireless data transmission. The flow chart is showed in Fig. 8.

Fig. 8. Flow chart of the software system

5 Experimental Test and Analysis Due to constraints, experiments are only conducted in the laboratory, and the experiments included data collection and alarm testing which were under artificial conditions.

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The materials used in this paper were the monitoring system for precision planter, infrared photoelectric sensors, Bluetooth device, PC, a stepper motor, some maize and wheat seeds. In the test, the performance of the monitoring system could be tested by counting the number of seeds passing through the sensors and the number of chosen seeds before the test. At the same time, we could test the working conditions of the monitoring device by making some faults under artificial conditions. The results were showed in Table1. Table 1. The test of the performance of the sound and alarm system

In the alarm test, the furrow opener and the metering devices were blocked 180 times and 200 times respectively. At the same time, the stepper motor was constantly started and stopped 220 times in the case of seeds presence. The results of alarm tests showed that the reliability of the alarm system was 100%. In the data collection tests, photoelectric sensors were used to detect the numbers of maize and wheat. The results were showed in table 2. Table 2. The results of the data collection test

From the results, it could be concluded that the photoelectric sensors detected 95% wheat seeds and 96% maize seeds passing through it. It could be seen that the seeds detected were less than 100% because of the anti-dust properties and seed-tube coverage of the photoelectric sensor. And which reduced the precision and reliability of the monitoring system. So, it is necessary to improve the sensor’s anti-dust ability and coverage of the seed tube. The following work is to solve these problems and this will improve the practical ability of the monitoring system.

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6 Conclusions According to current domestic and international planter monitoring systems' status and development trends, combined the theory of automation and the information technology, a design scheme using single-chip computer as the control center and Bluetooth wireless transmission for the secondary was proposed. For the present situation of precision planter, the performance of the existing planter can not fully meet the requirements of agriculture, so it is necessary to design monitoring systems with better performance. First of all, the monitoring system should have better stability, reliability and universal property. Secondly, integrated monitoring system equipped with compensator should be designed based on the technologies of mechatronics and automatic control [12]. Monitoring system with automatic compensator is the main developing trend in the following years.

References 1. Singh, R.C., Singh, G., Saraswat, D.C.: Optimisation of Design and Operational Parameters of a Pneumatic Seed Metering Device for Planting Cottonseeds. Bio systems Engineering 92(4), 429–438 (2005) 2. Datta, R.K.: Development of Some Seeders with Particular Reference to Pneumatic Seed Drills. The Harvester, Indian Institute of Technology, Khargpur, India 16, 26–29 (1974) 3. Zhang, X., Zhao, B.: Automatic Reseeding Monitoring System of Seed Drill. Transactions of CSAE 24, 119–123 (2008) 4. Hu, J., Li, X.: Magnetic Field Characteristic Analysis for the Magnetic Seed-metering Space of the Precision Seeder. Transactions of CSAE 21(12), 39–42 (2005) 5. Zhou, L., Zhang, X.: Monitor System of Precision Seeder Based on Capacitive Sensor. Journal of Agricultural Mechanization Research 11, 37–39 (2009) 6. Gong, L.: Development of Automatic Monitor System for Seed-cell Fill on Inside Precise Meter. Journal of Agricultural Mechanization Research 09, 47–49 (2008) 7. Dong, H., Zheng, J., Yang, J.: The Single-chip Control Applied in the Seeding-machine. Journal of Agricultural Mechanization Research 11, 181–184 (2006) 8. TI Corporation: MSP430 Universal synehronous Receive/Transfer communication interface. Taxes Instrument 1, 132–138 (1999) 9. Raju, M.: UltraLow Power RC Timer Implementation Using MSP430. Texas Instruments (2000) 10. Lee, W.: An efficient scheduling scheme for bluetooth scatternets using the sniff mode. In: Kahng, H.-K., Goto, S. (eds.) ICOIN 2004. LNCS, vol. 3090, pp. 103–113. Springer, Heidelberg (2004) 11. Petrioli, C., Basagai, S.: Degree-Constrained Multi-Hop Scatternet Formation for Bluetooth Networks. Mobile Networks and Applications 9(1), 33–47 (2004) 12. Xia, J., Zhou, Y., Zhang, P.: The Testing Technique Research of Seed Absence on Precise Seed-Meter Based on Virtual Instrument. Journal of Huazhong Agricultural University (Natural Science Edition) (04), 540–544 (2008)

Pervasive Agricultural Environment Monitoring System Based on Embedded Database* Hu Zhao, Sangen Wang**, and Dake Wu Southwest University, tiansheng road 216, chongqing, China [email protected], {wangsg,keda5}@swu.edu.cn

Abstract. According to space constraints of the traditional agricultural environment monitoring system, a small database called SQLite was transplanted into the ARM and Linux operation which could store and manage the field information. Users can get information anytime and anywhere through the GSM (Global System for Mobile Communications) network and the WSN(Wireless Sensor Network) by this design, instead of inquiring data from the PC(Personal Computer) which was widely used now. Sensors embedded in the environment, information spread in the air and captured by the handheld instruments, which is a typical application of pervasive computing. Keywords: The pervasive computing, SQLite, WSN, ARM, GSM.

1 Introduction Wireless sensor network of the field monitoring was constructed by Han Huafeng(2009) to transmitting the field acquired data to the designated database server on PC based on the ZigBee protocol, and data warehousing and decision support was used at the national agriculture statistics service by Yost M(2000). The most recently study was the WSN(Wireless Sensor Network) system based on the in-field soil water content monitoring designed by Jackson T, Mansfield K, Saafi M and so on(2007). Data of field were transmitted to the monitoring center through the GPRS network or Internet, and processed by the database system on PC in these systems. The field or greenhouse could be monitored remotely by the operator only in the monitoring center. These systems were expensive and complicate. So they were used only by universities and research institutes, but not in the vast rural areas. The era of pervasive computing is coming with the development of technology. People can get information anytime and anywhere by using a handheld device. It has already been used widely in the Mobile TV, 3G phones and so on. In this article, a pervasive agricultural environment monitoring system based on embedded database was proposed to achieve the following objectives: *

Project supported by “the Key Program of Chongqing Natural Science” (CSTC2009BA1006) and “the Fundamental Research Funds for the Central Universities” (Grant No. XDJK2009C141). ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 166–176, 2011. © IFIP International Federation for Information Processing 2011

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The greenhouse environmental data are transmitted to control module through WSN; The embedded system can collect, store and process the data independently; The system can send SMS to remind the users automatically at appointed time or the moment of abnormal; Users can query real-time and history data anytime and anywhere through mobile phones.

2 Hardware Design The system is composed of control module and monitor units. The control module include MCU (Microprogrammed Control Unit), store module, GSM module and RF module; The real-time environmental information is detected and transmitted to the control module by monitor units scattered around the greenhouse. Structural diagram of the system is shown in Figure 1.

Fig. 1. Structural diagram of the system

The S3C2410 is used to manage data and control other modules in the control module. The NOR FLASH of 16MB is the program memory of the system with the read-write file system. Environmental data will be protected in the FLASH when the power is off suddenly. There are 6 Banks in the S3C2410, and 2 of them were assigned to the ROM, SRAM and SDRAM respectively. The SDRAM of the system is 2 HY57V561620CT of 32M bytes. The TC35 of Siemens works as SMS Module, and the RFM12 transmits and receives data as the wireless modules. The monitors units include the MCU, sensors and the RF module. They spread around the greenhouse detecting the environmental information. The core of monitor units is a PIC16F877A with the strong anti-interference ability and rich resources.

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2.1 Control Module The ARM processor has becoming the preferred choice for the embedded system design now. The S3C2410 is selected as the MUC considering the factors of the processing speed, storage space, interface and the power consumption. The core of the S3C2410 is an ARM920T and it can save cost and improve the performance with advantages of low prices, high performances and high level of integration. The DS1302 provide accurate date and time. 2.2 WSN Module The star-type network topology was adopted for the WSN module. The Unique ID was corresponded to the different monitoring units. A number of monitoring units spread around the greenhouse which sent one packet per second. The control module received data by identifying the ID code circularly. For example, the control module received and processed the data of the second units only after receiving the first by identifying the ID code, and so on. The architecture of the WSN system is shown in Figure 2.

Fig. 2. Architecture of WSN system board

The RFM12 is a low costing ISM band transceiver RF module implemented with unique PLL. Its work signal bands ranges from 315/433/868/915MHZ, complying with FCC, ETSI regulations. The SPI interface is used to communicate with the MCU for the parameters setting. The data are sent into internal registers of RFM12 at the rising edge of the clock when the chip selecting signal nSEL is low. The synchronization header is sent at first, and then the data needed are sent. The next data can be sent when the SDO pin is high. The nIRQ pin is pulled low to notice the MCU when the RFM12 receives data. The MCU then reads the data stored in the buffer temporarily. The receiving register is closed when the receiving procedure finish. The greenhouse’s information includes the air temperature and humidity, soil temperature and humidity, light intensity and CO2 concentration. Digital sensors have the priority because of the calibration-free and high precision. It also makes it easy for multisensor fusion. The main performances of sensors are described as following:

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Air temperature and humidity sensors—Used to detect the air temperature and humidity, and the model is the SHT10(Basic type), the main performances: the humidity measuring range 0 100%rh, temperature measuring range -40 +123.8 , Temperature accuracy ±0.3 (25 ), humidity accuracy ±1.8%RH, Response time 8s, power supply 2.4 5.5V, and the Digital output(I2C bus). Soil temperature and humidity sensors—Used to detect the soil temperature and humidity, and the model is the SHT10P(Protection type), the main performances: the humidity measuring range 0 100%rh; temperature measuring range -40 +123.8 ; Temperature accuracy ±0.3 (25 ); humidity accuracy±1.8%RH; Response time 8s; power supply2.4 5.5V; and the Digital output(I2C bus). Light intensity sensor—Used to detect the light intensity, and the model is the BH1710FVC. The main performances: the measuring range 0 65,535Lx; Sensitivity±15%; power supply 2.4 3.6V; and the Digital output (I2C bus). CO2 concentration sensor—Used to detect the CO2 concentration, and the model is H-550. The main performance: the measuring range 0 5,000ppm; sensitivity ±30ppm±5%(0 50 ); response time≤30s; power supply DC9V 18V; Current consumption 50mA(mean value); and the Digital output(I2C bus).

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2.3 GSM Module TC35 is an industrial grade GSM module integrated RF modules and baseband processor.It Support data, voice, SMS and fax[8].The IGT pin maintain low(at least 100ms) at first when the TC35 start.Then it keep high to ensure TC35 work. Input/output interface of TC35 uses a serial asynchronous receiver, meet with ITUTRS232 interface standard. The data interface disposition is 8 bit data positions, one stop position, does not have the verification position.The hardware handshake signal use RTSO/CTSO.The software flow is controled by using Xon/Xoff. TC35 support AT command set.

3 Software Design The software architecture of the data analysis unit is shown in Figure 3. The embedded operating system µCLinux is located at the lowest layer. It manages the software and hardware resources of the system. The user programs are located at the most top layer. It receives and analyzes the environment parameters, and makes assess of the greenhouse’ state. The database management system based on SQLite is located at core intermediate level. It is composed of the numerous program modules and the function is to organize, manage and deposit the shared data effectively. 3.1 Design of Embedded Database System The µCLinux is an open project funded and maintained by Lineo. It was made popular by its ability to run on processors without memory management hardware and with a typical kernel footprint of only 512 Kbytes or less.

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Fig. 3. Structural diagram of the data analysis unit

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It inherits stability and robustness from Linux; It supports a large number of devices. file systems, and networking protocols; No license fee leads to very low cost; It gives developers complete visibility of the source code based on GPL license; Kernel can be customized on demand.

The software platform was built on uClinux. The system establishes cross compiling environment by using arm-elf-tools20030314.sh. This program is copy to the root directory first and run, then µCLinux can be compiled and transplanted. A directory called “images” will be generated in the µClinux-dist/ directory after cross-compilation successfully. It includes 3 binary file: image.ram, image.rom and romfs.img. The Bootloader is designed to make it sure that the execution jumps to the location of the kernel when resets. The MCU core must be initialized and extended in order to achieve the main functions. The whole procedure consists of the following steps: (1) Establishing interrupt vector table; (2) Initialization of various processor models; (3) Introduction of special variables; (4) Initialization of memory; (5) Copy codes. The right to use the CPU is returned to the operating system after hardware initialization process. Then the ultimate goal of the Bootloader is completed. Structfile_operations structure is used to define the various operation set of equipment in the µCLinux kernel. The main purpose of writing character device drivers is to achieve the various functions of structfile_operations structure. Structfile operations structure is defined in /include/linux/fs.h file. The drivers include the RF module, store module and GSM module. They were turned on and off by following functions: (1) RF12_open()/RF12_close(); (2) FLASH_open()/FLASH_close(); (3) TC35_open()/TC35_close().

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3.2 Design of Embedded Database System Large amounts of environmental information are collected and processed in the system. The Database is needed to store and manage a large amount of data. Traditional enterprise databases such as Oracle, Sybase and so on have the difficult to work in the embedded system because of the limited resources. The SQLite is small, fast, and reliable, so it is a good choice for embedded systems. It has the following advantages compared with the traditional databases: (1) (2) (3) (4)

It can run in the embedded devices whice have less memory and computing power; It can reset easily when the power or memory capacity runs out; The cost of establishment and maintenance is very low; It has strong portability to suit the complexity of embedded systems.

The SQLite is a universal embedded database. It must be transplanted and compiled before apply to the”S3C2410+uCLinux” environment. The main steps of transplanting are described below: (1) (2) (3)

Download sqlite-2.8.15.tar.gz; Unzip to directory uClinux-dist/user; User application settings.

The SQLite is added as a user application and converted into a shell. The following files needed to be compiled: (1) (2) (3)

uClinux-dist/user/Makefile uClinux-dist/config/Configure.help uClinux-dist/config/config.in

Run the program”make menuconfig” of uCLinux after above amendments. Selecte”CustomizeVendor/ User Settings” and ”Miscellaneous Applications”, There will be a new”sqlite (NEW)” which added by us before. The SQLite will be compiled to parts of romfs at the stage of”make romfs” later.The following documents needed to be added and modified finally, and thus to complete the compilation of the SQlite under uCLinux. • • • •

Modify sqlite/main.mk Add sqlite/Makefile Modify sqlite/src/os.c Modify sqlite/src/shell.c

The SQLite uses a standardized design structure based on relational database mode. It can be divided into 9 major auto units, as shown in Figure 4. These sub-functional units form the top-down tool chain: compile in the upper layer, query in the middle layer, storage data and operate system interface in the underlying. The SQLite supports most of the standard SQL92 statement, especially to support the view, trigger and services. The API interface of the C language was provided by

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the SQLite insteading of the database engine. The access operations of data can be achieved by the way of calling the appropriate API functions directly. The Header file ”sqlite3.h” must be referenced by the application program. There are 3 core API functions that should be emphasized:

Fig. 4. Structural diagram of SQLite

(1) Open database int sqlite_open (const char *filename, /* name of database */ int mode, /* Read-write mode */ sqlite3**ppDb); /* Output database handle */ (2) Execute database int sqlite_exec (sqlite3, /* database handle already be opend */ const char *sql, /* database statement should be executed */ sqlite_callback, /* Callback function */ void, /* The parameters passed to the callback function */ char **errmsg); /* Save error message */ /* Parameters is the database handle */ (3) Close database Int sqlite_close(sqlite3*); 3.3 Design of the Main Procedure The GSM module runes first when the main program begins. Receiver functions (read_sms()) are called to decoding the short message content when the SMS reaches (InterruptInt_Uart(void) interrupt 4 using 3()). The needed value is read from database

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and sent to the users(send_sms()). Then environmental information is received from the monitoring units(Receive_data()). The values are saved into database at the setting time. The alarm messages are sent to the users when the environmental values are exceeding the normal value. The flow chart of main procedure is shown in Figure 5.

Fig. 5. Flow chart of main procedure

3.4 Design of RF module The RFM12 is initialized (Open TX) at first in the sending mode, then calls subfunction “Send_data()” to send environmental parameters. The first two hexadecimal numbers of data are ID(Identify) codes which are unique for each unit. The next part of the data is the representatives of the air temperature and humidity, soil temperature and humidity, light intensity and CO2 concentration. The nIRQ pin is pull down at the end of sending to notice MCU sending the next data. The program diagram is shown in Figure 6(a).

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The RFM12 is initialized(Open RX) at first in the receiving mode, The nIRQ pin is pull down when the MCU reads the data. The FIFO must be reset after receiving the packet. The received data can be processed only through the validation procedures. The program diagram is shown in Figure 6(b).

Fig. 6. Flow chart of RFM12

4 Test Results The system was tested in the 3rd greenhouse of Southwest University agricultural demonstration center from October 2009 to May 2010. There are 16 monitoring units spreading evenly in the greenhouse. The monitored environmental parameters include the air temperature and humidity, soil temperature and humidity, light intensity and CO2 concentration. Data are automatically stored every 2 hours. The SMS will be divided into two parts if the length is longer than 67 characters (including letters, numbers, or punctuation). The SMS query mode is designed as follows:

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(1) Send”201005040901”,system will return” E24.53 60.23%S21.54 72.36% Li31111lx C04237ppm” automatically, mean the parameters of 1st monitoring point are:air temperature 24.53 , air humidity60.23%,soil temperature21.54, soil humidity 72.36%,light intensity31111lx, CO2 concentration 4237ppm; (2) Send”date+te”(air temperature),” date+he”(air humidity),” date+ts”(soil temperature),” date+hs” (soil humidity),” date+li”(light intensity),” date+c02” (CO2 concentration),system will return the data of the date.

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Among the total of 521 text messages, there were 2 errors, 5 missing, and 514 corrects. The data could be store six months until the new covered them automatically. Experiments show that the greenhouse remote monitoring system based on”ARM uCLinux SQLite”performs reliably and efficiently.

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5 Conclusion The system that combined the pervasive computing with the agricultural information was suitable for the vast rural areas. The most significant features of the pervasive agricultural syetem are low cost, simple to use, and people-oriented design. It can be expected that control center room, wired communication network, and complex database system based on PC would disappear in the future. The farmers can monitor and control the environmental factors of the field or greenhouse anytime and anywhere by handheld devices.

References 1. Han, H., Du, K., Sun, Z., Zhao, W., Chen, R., Liang, J.: Design and application of ZigBee based telemonitoring system for greenhouse environment data acquisition. Transactions of the CASE, 158–163 (2009) 2. Yost, M.: Data warehousing and decision support at the national agriculture statistics service. Social Science Computer Renew, 434–441 (2000) 3. Gao, F., Yu, L., Zhang, W., Xu, Q., Yu, L.: Research and design of crop water status monitoring system based on wireless sensor networks. Transactions of the CASE, 107–112 (2009) 4. Li, Z., Wang, N., Hong, T., Wen, T., Liu, Z.: Design of wireless sensor network system based on in-field soil water content monitoring. Transactions of the CASE, 212–217 (2010) 5. Jackson, T., Mansfield, K., Saafi, M., et al.: Measuring soil temperature and moisture using wireless MEMS sensors. Journal of Measurement, 381–390 (2007) 6. Huisman, J.A., Sperl, C., Bouten, W., et al.: Soil water content measurements at different scales: accuracy of time domain reflectometory and ground-penetrating radar. Journal of Hydrology, 48–58 (2001) 7. Shen, L.: Pervasive Computing. Computer Engineering & Science, 77–82 (2005) 8. Liao, D.: A Survey of the Pervasive Service. ZTE Communications, 32–35 (2008)

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9. Zeng, X., Liu, G., Zheng, D., Zhang, R.: Study development of a field information acquisition system based on wireless technique. Chinese Society of Agricultural Engineering Collected Papers, 151–154 (2005) 10. Li, X., Wang, W., Lei, T., Shen, Y.: Prospects of the application of multisensor information fusion techniques in agricultural engineering. Transactions of the CASE, 10–13 (2003) 11. Zhou, G., Zhou, J., Miao, Y., Liu, C.: Development and application on GSM-based monitoring system for digital agriculture. Transactions of the CASE, 158–163 (2009) 12. Hu, W.: Study on SQLite Implementation on the Embedded System. Computer & Digital Engineering, 158–165 (2009)

Precipitation Resource Potential in Mountainous Areas in Hebei Province Analysis Zheng Liu1, Yanxia Zheng1, and Zhiyong Zhao2 1

Department of Resources and Environment, Shijiazhuang University, Shijiazhuang, Hebei 2 Research institute of Resources and Environment, HeBei Normal University, Shijiazhuang, Hebei [email protected]

Abstract. The climatic conditions of Hebei Province cause this area shortage of water resources, man-made factors lead it more serious. In March 2007 in Hebei, 37 mountain counties got samples of soil and corn, conducted indoor tests, through analyzing Hebei rainfall and soil moisture and corn yield in mountains, using potential resource calculated model to precipitation, they analyzed and evaluated precipitation resource potential in mountainous areas in Hebei , concluded that precipitation is the important factor affecting the soil moisture content and maize yield. The development of storage-volume of precipitation resource potential is less than half, the theoretical storage-volume set of the potential development is lower, so that it is expected to improve the potential of development through technological improvements; Hebei runoff harvesting project should take rural residential areas as its core region; the precipitation of mountainous areas in Hebei Province is dominant factor affecting the amount of runoff harvesting. Keywords: Hebei mountain; precipitation; precipitation resources; potential analysis. Program Source: Science and Technology Bureau of HeBei Province.(04230702), The National Natural Science Funds of HeBei Province(D2005000537), Science research and development plan of Shijiazhuang,program of Shijiazhuang University(09YB006).

Precipitation resource potential is such a process that through people’s controlling of precipitation in the spatial and temporal on underlying surfaces to promote more parts to form the available resources [1]. Hebei province takes rainwater as one of the main sources of water resources, with climate rainy in hot summer, less snowy in dry winter, windy in dry spring , less rainy in autumn, causing the shortage of water supply. This article analyzes the relationship between the corn yield and precipitation, estimates and evaluates rainfall resource potential, to analyze the precipitation resource potential and the using measures in Hebei mountains. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 177–184, 2011. © IFIP International Federation for Information Processing 2011

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1 Data Sources 1.1 Field Sampling and Determination of Characteristic Parameters Group conducted soil sampling of 37 mountainous counties in Hebei Province in March 2007, sampling points chosen had 74 various typical mountain soil, at the same time collected corn seed of samples, recorded samples terrain, altitude, slope, vegetation, irrigation, fertilization, and collect relevant information from relevant departments. Experimented the soil parameters in laboratory: soil moisture, soil bulk density, organic matter, pH, total nitrogen, total phosphorus, available phosphorus, total kalium, available kalium; corn seed parameters are: total nitrogen, total phosphorus and total kalium. 1.2 Other Statistics Totally, counted the soil nutrient content, nutrient content of maize seed and maize yield in Handan, Xingtai, Shijiazhuang, Baoding, Zhangjiakou, Chengde, Qinhuangdao, Tangshan 8 counties, 66 cities (county-level cities, districts), which dam highland grassland owns 6 areas, Hebei Northwest Rocky Mountain 10 areas, Taihang Rocky mountainous 26 areas, Yanshan Rocky Mountainous 24 areas . Statistical characteristics of rainfall and the soil moisture in Hebei Province mountainous area is shown in Table 1. Statistical characteristics of rainfall and the soil moisture in Hebei Province each mountainous area in Table 2. Table 1. Rainfall and soil moisture data describing characteristics statistics table Index Rainfall Soil moisture

Mean 549.019mm 7.047%

Standard deviation 111.420 mm 4.680%

Variation Coefficient 20.294 66.411

（%）

Table 2. Rainfall and soil moisture of sampling sites regional tables

Index Rainfall (mm) Soil moisture(%)

Dam Highland Grassland Area 395.7

Hebei Northwest Rocky Mountainous area 397.69

Yanshan Rocky Mountainous area 646.17

Taihang Rocky mountainous area 566.69

5.354

5.178

9.326

7.088

Table 2 shows that in the mountains of Hebei Province, Yanshan Rocky mountainous area is the most abundant in rainfall, followed by Northwest Hebei and Taihang mountainous Rocky Mountain area, Dam Highland Grassland Area has least precipitation of all.

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2 Mountain Precipitation Resource Potential Calculation and Its Evaluation Methods [2] 2.1 Precipitation Resource Potential Calculation Formula Resource potential in mountain precipitation calculation can be divided into three levels: first, The theory potential of precipitation resource; second precipitation of the set of storage capacity that can be realized; three the actual set of precipitation storage capacity.

，

，

(1) The theory potential of precipitation resource Calculated as: Rt = P × A × 103

(1)

Type in: Rt——Theory of precipitation potential (m3); P—— precipitation (mm); A——area (km2). (2) Precipitation set of storage capacity that can be realized Based on the precipitation of storage capacity, it can be set to achieve understanding, and build the following expression: Ra = λR × P × A × 103

(2)

Or Ra = P-(1-λ1R) R-(1-λ2R) E0

(3)

The formula: Ra——precipitation set of storage capacity can be achieved (m3); P——precipitation (mm); A - area (km2); R——surface runoff; E0——evaporation; λR——possible controlling coefficient achieving the maximum precipitation λR × P——the largest amount of precipitation can be regulated; λ1R—— maximum controlling surface runoff coefficient; (1-λ1R) R—— the minimum controlling of surface runoff; λ2R——maximum controlling evaporation coefficient; (1- λ2R) E0—— minimum difficult controlling evaporation loss. (3) The actual set of precipitation storage capacity It refers to the precipitation on the current use patterns and techniques, amount of resources, set realistic storage capacity Rr is consistent with achievable storage capacity Ra set formula, r is on behalf of mountain precipitation realistic level. Rr = λr × P × A × 103

(4)

Or Rr = P-(1-λ1r) R-(1-λ2r) E0

(5)

The formula: Rr —— the actual set of precipitation storage capacity (m3); p —— precipitation (mm); A - drainage area (km2); R—— surface runoff; λr —— reality level of drainage precipitation controlling ability; λ1r—— runoff controlling coefficient; (1-λ1r) R—— difficult controlling surface runoff; λ2r —— evaporation controlling coefficient; (1-λ2r) E0 ——the difficult controlling evaporation loss; λr, λ1r, λ2r concern with technology and the economic level.

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In theory, λ1R, λ2R → 1, Ra → Rt, with runoff control, and evaporation suppression technology improved, λ1r → λ2r, λ2r → λ2R, Rr → Ra. With the drainage technological progress and economic development, precipitation set of storage capacity can be realized and the actual set of storage capacity will rise. 2.2 Precipitation Potential Assessment Methods [3-5] For a specific mountain, if the precipitation, infiltration, topography, soil information of the mountain and existing use and area of precipitation can be mastered, only through some tests on precipitation runoff, the precipitation potential can be calculated. Combined with the needs of mountainous water conditions, it can evaluate precipitation development and utilization of resources. WDmax precipitation resource potential theory development degree: WDmax = (Ra / Rt) × 100%

(6)

WDreal1 precipitation resource realistic storage development degree: WD real1 = (Rr / Rt) × 100%

(7)

WDrea12 precipitation enables capacity development degree: WDrea12 = (Rr / Ra) × 100%

(8)

Wd, Rd actual water demand degree, the actual water demand: Wd = (Rd / Rt) × 100%

(9)

Precipitation resources development must adhere to principles of the sustainable use and sustainable development. On one hand, precipitation should meet the needs of certain areas of water demand; on the other hand, precipitation can not be unlimited to develop, the scale of development should correspond with the existing conditions of precipitation, it must not exceed the carrying capacity of precipitation. In general, development of precipitation in mountainous areas will appear the following situations: (1) WDmax> Wd> WDreal1, shows that mountain precipitation resources development scale is more realistic, although precipitation resources in the development and utilization can not meet the requirements of the regional water demand, but you can set store by increasing flow works or other measures to enhance the potential for the development level. (2) Wd> WDmax> WDreal1, shows that developed mountain precipitation use scale is beyond the maximum level of precipitation development, it needs to reduce the development of scale and optimize water usage of the business, otherwise it will lead to environmental degradation, or drought intensified.

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(3) WDmax> WDreal1> Wd, shows that developed mountain precipitation use scale is not very good , for the play to precipitation potential, water consumption should adjust the ratio or the appropriate use of expanding the scale of precipitation. (4) WDmax = Wd = WDreal1, shows that mountain precipitation set development scale and resources utilization had reached the maximum precipitation development potential, and the practical development ability can also meet the requirements of the development scale, to realize the efficient use of precipitation , in reality, such opportunities rarely arise. (5) WDrea12 always greater than Wdrea11, the higher WDreal2 shows that precipitation potential can be achieved higher, all taken by the water runoff and storage projects and technology should be more advanced.

3 Estimation and Evaluation of Runoff Collection By precipitation resource potential calculation and evaluation means, results of precipitation resource potential calculation and store to the arable land in Hebei Province mountainous is in Table 3-6. Results of potential development degree to the arable land in Hebei Province mountains is in Table 7. The results show that: (1) In the current runoff collection and storage, rural settlements as courtyard, roof, and scene construction are the key runoff construction areas. In Hebei Province mountains, the percentage of rural settlements area in total runoff area is 74.59%, rural settlements runoff realized potential percentage of store runoff realized potential account is 78.73%, Highways and rural roads in runoff area is 25.41%, realized potential store percentage of runoff to arable land realized potential is 21.27%. Therefore, runoff harvesting project in Hebei Province should take the rural settlements as the core region [6-8]. Table 3. The theory potential precipitation resource in Hebei Province mountainous area (107m3) Area Hebei Province mountainous area Dam Highland Grassland area Hebei Northwest Rocky Mountain area Yanshan Rocky Mountainous area Taihang Rocky mountainous area

Rural Settlements 126.2 40.0 20.9 40.9 24.4

Highway 11.4 3.7 2.2 3.5 2.0

Rural road 22.7 1.6 8.3 8.2 4.6

Sum 160.3 45.3 31.4 52.6 31

Table 4. The theory potential store Rt to the arable land in Hebei Province mountainous areas(m3/mu) Area Hebei Province mountainous area Dam Highland Grassland area Hebei Northwest Rocky Mountain area Yanshan Rocky Mountainous area Taihang Rocky mountainous area

Rural Settlements 514.3 56.2 61.5 228.6 168.0

Highway 229.9 25.7 35.3 100.3 68.6

Rural road 598.5 102.6 118.4 221.1 156.4

Sum 1342.7 184.5 215.2 550 393

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Table 5. The realized storage capacity Ra to the arable land in Hebei Province mountainous areas(m3/mu) Area

Rural Settlements

Highway

Hebei Province mountainous area Dam Highland Grassland area Hebei Northwest Rocky Mountain area Yanshan Rocky Mountainous area Taihang Rocky mountainous area

226.8 24.8 27.1 100.8 74.1

106.8 12.3 13.5 48.1 32.9

Rural road 89.9 15.4 17.8 33.2 23.5

Sum 423.5 52.5 58.4 182.1 130.5

Table 6. The actual storage capacity Rr to the arable land in Hebei Province mountainous areas(m3/mu) Area Hebei Province mountainous area Dam Highland Grassland area Hebei Northwest Rocky Mountain area Yanshan Rocky Mountainous area Taihang Rocky mountainous area

Rural Settlements 128.6 14.1 15.4 57.1 42.0

Highway 27.5 3.1 4.2 12.0 8.2

Rural road 18 3.1 3.6 6.6 4.7

Sum 174.1 20.3 23.2 75.7 54.9

Table 7. The potential development degree to the arable land in Hebei Province mountainous areas(%)

Area Hebei Province mountainous area Dam Highland Grassland area Hebei Northwest Rocky Mountain area Yanshan Rocky Mountainous area Taihang Rocky mountainous area

The theory development degree (Ra /Rt) 31.54

The actual development degree (Rr /Rt) 12.97

The realized development degree (Rr /Ra) 41.11

28.46

11.00

38.67

27.14

10.78

39.73

33.11

13.76

41.57

33.21

13.97

42.07

(2) The theory potential, achieved potential, reality collection and storage of runoff to land is the quite different. Storage capacity to land achieved potential in Hebei Province mountain runoff on average is 423.5m3/mu, accounting for 31.54% in the theory potential storage capacity, the reality storage capacity is 174.1m3/mu, accounting for 41.11% in the achieved potential storage capacity. However, two matters must be focused on, first is in rural settlements, only land near highway and rural roads, runoff collected area are facilitated by supplementary irrigation, which is part of the farmland less than 10% of the total cultivated area; the second is above 85% of construction cellar located in rural settlements, the main problem is to solve drinking water of human beings and animals, cellar in highways and near rural roads runoff is few, and quite a few road away from the field, most of the rural road with narrow surface, not yet

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hardened, low efficiency of runoff , runoff containing large sediment. Therefore, runoff surface construction should be strengthened, rational planning and layout of cellars, to make the planting area and runoff collection area and storage containers is matching [9-10]. (3)It can be analyzed and calculated through precipitation data collected by district, runoff area ratio (the ratio of runoff area and the arable land), and runoff to land collection and storage In Hebei Mountain 4 regions, annual rainfall, wet season rainfall, runoff area ratio, runoff collection and storage to land are significantly different, the double impact of runoff collection and storage are rainfall and runoff area ratio, precipitation is the dominant factor of runoff store. The order of regional reality storage capacity of runoff to the land (m3/mu) is shown in Table 6: Yanshan Rocky Mountainous area (75.7)> Taihang Rocky mountainous area (54.9)> Hebei Northwest Rocky Mountain area (23.2)> Dam Highland Grassland area (20.3). In comparison, the average rainfall in Yanshan Rocky Mountainous area about 646m, runoff collection and storage to land significantly higher than other regions, is relatively easy to collect and store runoff; Dam Highland Grassland area annual precipitation around 396m, runoff to the land saving is very low, runoff development to irrigation farmland need to build up a larger runoff. According to the precipitation resource potential assessment indicators and calculation methods, different regions of Hebei Province mountain runoff to collect and store to land of theory potential development degree is 28-32% (table 7), achieved storage capacity potential development degree is 38-43%, there are about 60% untapped potential, and realistic storage capacity is only a 10-13% development. So the less precipitation, the lower the development; in the existing topography, land use and agricultural technology conditions, the realistic precipitation storage capacity development potential is less than half, the theory development potential is lower, the potential to be developed by improving the degree of technological progress [11].

①

②

4 Conclusion (1) In Hebei Province, the rainfall amount is an important factor affecting soil moisture content and corn yield. In Hebei Province mountains, Yanshan Rocky Mountainous area is most abundant in precipitation, followed by Taihang Rocky mountainous area and Hebei Northwest Rocky Mountain area, precipitation in Dam Highland Grassland area is least. Mountain soil moisture content in Hebei Province, Yanshan Rocky Mountainous area is the most abundant in precipitation, followed by Taihang Rocky mountainous area, Dam Highland Grassland area and Hebei Northwest Rocky Mountain area. (2) In Hebei Province mountains, precipitation resource realized potential development storage capacity is less than half, the theory storage capacity potential development is lower, the potential to be developed by improving technological. Runoff harvesting project in Hebei Province take rural settlements as the core region. Runoff surface construction should be strengthened, rational planning and layout of cellars, make planting area and runoff collection area and storage containers is matching. In Hebei Mountains, precipitation is the dominant factor of runoff storage. Annual rainfall, wet season rainfall, runoff area ratio, runoff collection and storage to land are significantly

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different. In comparison, Yanshan Rocky Mountainous area runoff collection and storage to land significantly higher than other regions, is relatively easy to collect and store runoff. Runoff of Dam Highland Grassland to the land saving is very low, runoff development to irrigation farmland need to build up a larger runoff.

References 1. Niu, W., Wu, P., Feng, H., Gao, J.: The Regional Water Resource Potential Evaluation Method and Utilization Planning Estimation. Chinese Conservation of Water and Soil Science 09 (2005) 2. Hu, B.: Loess Plateau Precipitation Resource Study. Northwest Agriculture and Forestry University. Shannxi (2006) 3. Gao, P., Liu, G., Liu, J., Liu, Z.: Middle and South Mountain Watershed Rainwater Harvesting Potential of the Quantitative Evaluation Study. Water Conservation Journal 20(6) (2006) 4. Kang, X., Ma, H.: Hebei Province Climate Production Potential Estimates and Division. Chinese Agricultural Meteorology 29(1), 37–41 (2008) 5. Gao, X., You, F., Xu, Y.: Hebei Province the Precipitation Conditions of Water Resources Analysis. Arid Meteorology 26(1) (2008) 6. Gao, X., Li, Q.: Hebei Province, 1961-2005 Climate Characteristics and Precipitation and Water Resources Development and Utilization. Ecology and Environment Journal 24(4) (2008) 7. Bi, Y., Wang, H.: Hebei Province Precipitation Analysis. Hebei Water Conservancy Science and Technology 22(3) (2001) 8. Ruan, X., Liu, X., Li, Y.: Hebei Province, Nearly 40 years of Drought Change Analysis. Arid Land Resources and Environment. 22(1) (2008) 9. Guo, W., Ma, Z., Yao, Y.: The Last 50 years in North China Regional Evolution of Soil Moisture Characteristics. Geographical Journal 58(9), 84–90 (2003) 10. Li, Q., Liu, X., Li, X.: Nearly half a Century of Drought in North China Trend Study. Natural Disasters Journal 11(3), 50–56 (2002) 11. Zhai, P., Ren, F., Zhang, Q.: China Extreme Precipitation Trend Detection. Meteorology 57(2), 208–216 (1999)

Precision Drip Irrigation on Hot Pepper in Arid Northwest China Area1 Huiying Yang1, Haijun Liu1, 2,*, Yan Li1, Guanhua Huang3, and Fengxin Wang3 1

College of Water science, Beijing Normal University, Beijing, China, 100875 Tel.: & Fax: +86 10 58802739 [email protected] 2 China National Laboratory of Soil Erosion and Dryland Agriculture on the Loess Plateau, Yangling, China 712100 3 College of Water Conservancy & Civil Engineering, China Agricultural University, Beijing, China 100083

Abstract. An experiment was conducted to study the effects of different soil water potential (SWP) on soil water content, hot pepper’s yield, water consumption and water use efficiency (WUE) under plastic-mulched drip irrigation in the North-West China in order to find a suitable SWP to guide the pepper irrigation. Five treatments were set based on SWP, they are -10kPa (N1), -20kPa (N2), -30kPa (N3), -40kPa (N4) and -50kPa (N5). A control treatment (N6) was set based on local irrigation practice, i.e. border irrigation. SWP was measured using tensiometers at 0.2 m depth immediately under drip emitters. Pepper leaf area, plant height, soil water content, yield and total soluble solid (TSS) were measured, soil water content and water use efficiency were calculated. Results shows that the differences in leaf area index and plant height are not significant (P>0.05) among treatments of N1, N2, N3 and N4. While the pepper yields, WUE and TSS are higher for treatments N3 and N4. Controlling SWP at -50kPa greatly decreases crop yield and WUE. Therefore, we recommend -30 ~ -40 kPa as the irrigation threshold for pepper cultivation under mulched drip irrigation in arid areas of the North-West China. Keywords: Soil water potential; yield, water use efficiency; mulched drip irrigation, Northwest China.

1 Introduction Hot pepper is an important economic crop in China. Areas for pepper production is 35 000 ha[1], mainly distributed in the North-West China and South-West China. The annual production is about 215,000 t. In the North-West China, especially in Gansu Province, the hot pepper is widely cultivated with a high capsaicin and haematochrome content, which are mainly due to its special climatic condition[2]. During the long history of hot pepper cultivation in Gansu Province, the crop is generally irrigated with surface irrigation, including border irrigation and furrow irrigation. Under these *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 185–197, 2011. © IFIP International Federation for Information Processing 2011

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irrigation practices, crop water use ranges from 330 mm to 540 mm. Annual precipitation in this region is from 150 mm to 300 mm , and the arid index is from 4 to 15[3]. Precipitation cannot satisfy the water requirement of the pepper. Agricultural water use generally accounts for 92.57 % of the total water use in this region[4]. With the development of economy, water allocation for agricultural will be decreased. Therefore, water-saving irrigation technology and irrigation scheduling are the ways to saving water and maintaining high crop yield and quality, especially for cash crops, like hot pepper. Water-saving irrigation method is one of the key factors to save irrigation water and increase crop yield in the arid region. Drip irrigation is one of the Water-saving irrigation methods. Drip irrigation system in general frequently irrigates crops, which can minimize water stress, increase crop yield and improve crops’ quality. Hanson and May (2004)indicated that drip irrigation applies water both precisely and uniformly, in comparison with furrow and sprinkler irrigation, resulting in the potential to reduce subsurface drainage, control soil salinity, and increase yield[5]. Liu et al (2005) pointed out that plant growth and yield under drip irrigation were higher than those under flood irrigation, the soluble solid contents under drip irrigation also increased[6]. Li et al (2007) pointed out that drip irrigation can save irrigation water by 49.83% as compared to surface irrigation, and the crop yield increased by 9.99%[7]. Under most experiments, drip irrigation can save irrigation water by 30% to 40%[8]. Hot pepper is sensitive to water stress. Soil water deficit seriously affects the growth and yield of hot pepper. Therefore, many experiments were carried out to seek suitable soil water content for pepper. Wang et al.[9] pointed out that the yield rates of hot pepper in greenhouse are 53.4%, 72.3% and 79.1% when the irrigation were 45, 67.5 and 90 mm respectively, and the highest hot pepper yield was achieved when moisture of the root-zone soil maintains between 60% and 80% of field capacity. Yang et al[10] found that pepper yield and dry matter accumulation increased with the increase of reasonable amount of water and fertilizer and that the highest yield was achieved when soil moisture maintain between 70% and 75% of field capacity. Huo et al[11] also found that the highest pepper yield was achieved when soil moisture maintains 70% of field capacity, while too high or too low soil moisture greatly affects pepper production. In practice, the soil water content measurements generally require special equipments, and the measuring process is complicated. Soil water potential is related to soil water content[12], and also used as an indicator to directly guide irrigation[13-20]. Some experiments showed that, the proper soil water potential is -25kPa for potato, -35kPa for radish, -50kPa for tomato. The objectives of this paper are to investigate the effects of different soil water potentials to soil water content, pepper’s growth, yield, water consumption and water use efficiency under plastic mulched drip irrigation condition, and finally find suitable soil water potential to guide pepper irrigation practice in the arid condition of the North-West China.

2 Materials and Methods 2.1 Experimental Sites The experiment was conducted at Shiyanghe Experimental Station for Water-saving in Agriculture and Ecology of China Agricultural University, located in Wuwei City,

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Gansu Province of northwest China (N 37°52′, E102°50′50″, altitude 1581m) during April to October of 2009. The experimental site is in a typical continental temperate climate zone, mean annual temperature is 8 , annual accumulated temperature (>0 ) is 3550 , annual precipitation is 164.4 mm, mean annual pan evaporation is about 2000 mm, arid index (the ratio of yearly water evaporation to year precipitation) is 15 -25, average annual sunshine duration is 3000 h and free frost days in a year is 150 days. The groundwater table is 40 – 50 m below the ground surface. Soil texture is sandy loam, with a mean dry bulk density of 1.5 g/cm3, and mean volumetric soil water content at field capacity is 0.243 cm3/cm3.

℃

℃

℃

2.2 Experimental Design The experiment consisted of five treatments based on soil water potential (SWP), -10kPa, -20kPa, -30kPa, -40kPa and -50kPa, they are referring to N1, N2, N3, N4 and N5, respectively. A control treatment (N6) was set according to the local irrigation. SWP for each treatment was measured using two tensiometers, which is installed at 20 cm depth immediately under the emitter. Each treatment includes three replications, with a dimension of 4.6 m in width and 5 m in length for each replicated subplot. Each subplot is divided into three raised beds. The spacing between raised beds is 0.4 m; the width and height of the bed are 1.0 m and 0.2 m, respectively. All experi mental subplots were following a complete randomized design, seeing Figure 1. The two tensionmeters were installed in the middle raised bed of each experimental treatment. Before the experiment, soil water content and SWP were measured simultaneously to determine the soil water characteristic curve. This curve was used to calculate soil water content and then to determine irrigation amount for each irrigation event using the measured SWP. The relationship between soil water content and SWP is expressed using Eq. (1). 2

ψ = −94.66 ln (θθ − 130.83(n = 21,R = 0.9584)

(1)

Where θ is soil volumetric water content and ψ is SWP in kPa. When the SWP was below the target SWP, irrigation begins. Each irrigation amount was determined using soil water content and field capacity with the Eq. (2).

M = AH(FC − θ)p / η

(2)

Where M is irrigation amount in m3; A is the plot area in m2; H is irrigation depth in m and is set 0.25 m based on the root distribution; θ is the volumetric water content before irrigation, which is calculated from measured SWP; FC is the field capacity; p is the percentage of wetted area and is 0.652; η is irrigation efficiency and set 1.0 under drip irrigation.

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(a)

(b) Fig. 1. Schematic diagram of the treatment layout (a) and detail drawing of the treatment plot (b), length unit in figure (b) is meter

Fig. 2. Soil water potential (SWP) variations of different treatments in the experimental period

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2.3 Agronomic Practices

Peppers were planted in a raised bed. In each bed, there were four pepper rows, with row spacing of 0.3 m - 0.4 m - 0.3 m and plant spacing of 0.3 m. Two pepper rows was irrigated using one drip tape with emitters spacing 0.3 m and emitter discharge of 2.7 l/h at the operating pressure of 0.1 MPa. (Lvyuan Inc, Beijing). The drip tape is deployed between the two pepper rows. Therefore, there are two drip tapes in each raised bed. The raised beds and drip tapes were covered with a 1.4 - m - width black plastic sheet to reduce surface evaporation and control weed growth. Organic fertilizer (cow manure) was applied uniformly to each plot with a rate of 12000 kg/ha when the soil was plowed. Nitrogen fertilizer (urea) was applied to the peppers using the drip irrigation system by three times with a rate of 180 kg/ha. The hot pepper (American Red) was seeded on 4 May and harvested on 25 September in 2009. 2.4 Measurements

(1)Meteorological factors Meteorological factors included temperature, humidity; solar radiation, wind speed, and precipitation were recorded by a automatic weather stations in the experimental station. (2) Soil water potential Soil water potential (SWP) was measured using tensiometers, which were installed at a depth of 0.20 m immediately below the emitter in one replicate subplot of each treatment. The tensiometers were investigated three times a day, at 8:00, 14:00 and 18:00. When the measured SWP is approaching the set values, irrigation begin. (3) Soil moisture Soil moisture was measured every 10 cm from 0 to 60 cm soil layer by gravimetric measurement using augers every 15 d during the experimental period. (4) Leaf area index Leaf area index (LAI) was measured by leaf area meter AM300 (AM300 Portable Leaf Area Meter, ADC Bioscientific Ltd., Hoddesdon, UK) at 15-day intervals during the pepper-growing season in 2009. For each event, LAI at three to five sites in each treatment were measured and the average value was used for data analysis. (5)Total soluble solids Total soluble solid (TSS) was measured by handheld digital refractometer (PR-32α, Tokyo, Japan) during the harvest time in 2009. Three pepper fruits were random selected for each treatment and the average value was calculated. (6) Yields Pepper yields were determined at harvest. Twenty plants in the middle subplot of each treatment were randomly selected, and the numbers per pepper plant and weight of each pepper were investigated.

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2.5 Calculations

Water consumption (ET) of hot pepper was estimated using the water balance method as:

ET = I + Pe + K − ( Wt − W0 )

(3)

Where I is the irrigation amount in mm; Pe is the effective precipitation in the pepper growth period in mm; Wt is the soil water storage at time of t; W0 is the initial soil water storage; K is the groundwater recharge; ET is the evaportranspiration, mm. Water use efficiency (WUE) of hot pepper is expressed as the production of pepper consuming per units of water, and the calculation formula is:

WUE =

Y ET

(4)

Where WUE is the Water use efficiency, Y is the yield, ET is the evaportranspiration. In this paper, SPSS11.5 statistical software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis.

3 Results and Discussion 3.1 Soil Water Content under Different SWP Treatments

Zhang (2001) reported that most of the pepper roots developed in the soil at a depth of 40 cm[21]. Our measurement also shows that more than 90% of root mass distributed within the 0 - 40-cm soil layers. Therefore, we analyzed the soil water content in the upper 0 - 40-cm soil layer (Fig. 3). It can be found that the average soil water content of N1 treatment was the highest and with the smaller variation during the whole experiment period among the five treatments. The reason for treatment N1 maybe mainly due

～

Fig. 3. Average soil moisture content over 0 40 cm soil layer in the experimental period

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to the higher irrigation frequency and greater irrigation amount, i.e. everyday or once two days in vigorous growth stage, as compared to the other five treatments. The soil water content of treatment N5 was the lowest before August 20, which is mainly because of the least irrigation amount. The soil water content of treatment N6 is also very low before August 20 because the irrigation intervals were too long, i.e. once every 15 to 20 days. In the later period of the experiment, there is a minor difference in soil water content among treatments except N1, which maybe mainly due to more precipitation. The precipitation from June 20 to August 20 was 53.4 mm, and was 38.6mm from August 20 to harvest. 3.2 Pepper Growth 3.2.1 Leaf Area Index Fig. 4 illustrates the changes of leaf area index (LAI) for the 6 treatments during experimental period. The figure shows a sigmoid shape for the LAI in the experimental period. The maximum LAI reached generally around 7 to 27 August. During most experimental period, LAI was the least for treatment N5, which may be due to the less irrigation amount. The differences in LAI among the other five treatments were not significantly during the most experimental period.

Fig. 4. Changes of leaf area index within the pepper growth period

3.2.2 Plant Height Fig.5 shows the changes of plant height for the six treatments during the experimental period. The figure shows that, plant height increased fast before August (generally in vegetative growth stage), afterward, it increased little or stop increasing in fruit growth stage. The plant height of N5 was the lowest and it was the highest for N6 treatment during the whole growing period. The height differences among N1, N2, N3 and N4 were insignificant (P>0.05). It can be concluded that controlling soil water potential of -50 kPa will seriously affect the growth of pepper.

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Fig. 5. Changes of plant height within the pepper growth period

3.3 Fruits Quality 3.3.1 Total Soluble Solids (TSS) TSS is an index of soluble solids concentration in fruit and always used to evaluate fruit quality. Results on TSS for the six treatments are showed in Fig 6. The TSS content generally increased as the SWP decreased from -10 kPa to -40 kPa. The highest TSS was 9.2%, found for treatment N4. The lowest was found for treatment N5 with a value of 7.5%. The TSS for flood irrigation treatment was similar to the treatments N1 and N2, but greatly less than those for treatment N3 and N4. It can be seen that a SWP of -50 kPa will greatly influence fruit quality, while controlling SWP within a relative deficit range (for example -30 to -40 kPa) maybe benefit crop quality. Findings of Ma et al (2006) [22] is similar to our conclusion, who found that the deficit irrigation treatments enhance soluble solid matter in jujube fruits and improve the fruit quality.

Fig. 6. Content of TSS of pepper for different water deficit treatments (%)

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3.3.2 Fruits Classification Sixty pepper plants were selected to determine the fruit yield for each treatment, the weight of each pepper fruit was measured individually. The pepper fruit was classified into four levels according to the pepper fruit weight, they were > 30 g (level 1), 20 – 30 g (level 2), 10 – 20 g (level 3), and < 10 g (level 4). Fig 7 shows the proportion of peppers in each level to the total yield. Except for treatment N5, the proportion was the highest in level 2 (20 – 30 g), following by level 3 (10 – 20 g), level 1 (> 30 g) and level 4 (< 10 g).There was little difference in pepper proportion of each yield level among treatments N1, N2, N3, N4 and N6. For treatment N5, about 50% of peppers were found in level 3, which means there were more small peppers as compared to other five treatments. This is the main reason for the lowest yield of treatment N5 among all treatments (Table 1). 60% 50% >30g

n 40% o tir o 30% p o r P 20%

20g-30g 10g-20g <10g

10% 0% N1

N2

N3

N4

N5

N6

Treatment

Fig. 7. The proportion of pepper yield to the total yield for different levels

3.4 ET, Yield and WUE

Table 1 lists the irrigation amount, crop water consumption (ET), yield and WUE for the six treatments. Pepper ET was calculated using water balance equation, and WUE was calculated by Eq. (3). Table 1. Irrigation amount, ET, yield and WUE of pepper for different treatments

（ton/ha）

3

treatments

Irrigation amount(mm)

ET(mm)

-10kPa(N1)

314

412

34.1(±4.2) abc

8.3

-20kPa(N2)

204

321

31.5(±4.2) bc

9.8

-30kPa(N3)

179

311

35.6(±5.0) a

11.5

-40kPa(N4)

163

295

33.7(±5.7)abc

11.4

-50kPa(N5)

180

312

30.7(±4.8) c

9.9

Border irrigation(CK)

246

317

34.3(±4.2)ab

10.8

Yield

WUE(kg/m )

NS: values in the yield column with the same letter are not significantly different by one-way ANOVA test at the 0.05 probability level.

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Pepper irrigation amount and ET decreased greatly from treatments N1 to N2, while differences of irrigation amount and ET were small for treatments N3, N4 and N5. These results are similar to the finding of Zhang et al. (2006)[23], who carried out a drip irrigation trial with SWP controlling on tomato, and found that irrigation amount decreases with the SWP decreasing first and then increases when SWP decreases to a certain level. We analyzed the relationship between the controlled SWP levels and irrigation water amount and crop ET, showing in the Fig. 8. The relationships between the irrigation amounts, total ET and SWPs can be expressed using a quadratic polynomial, as follows.

ET = 15.072ψ 2 − 113.24ψ + 503.97(n = 5, R 2 = 0.9491)

(5)

M = 18.648ψ 2 − 142.79ψ + 431.36(n = 5, R 2 = 0.9727)

(6)

Where P is the control SWP (kPa); M is the irrigation amount (mm).

Fig. 8. Relationship between pepper irrigation amount, ET and control SWP

Pepper yields for the six treatments were following the order of N3>N6>N1>N4> N2>N5. Statistical analysis shows that the pepper yield of treatment N3 was significantly (P<0.05) higher than Treatment N2 and N5, but the yield differences among treatments N3, N6, N1 and N4 were insignificant (P>0.05). The minimum pepper yield was obtained in treatment N5, which was lower by 16% than the maximum yield in treatment N3. This result means that the controlling SWP of -50 kPa will cause a great potential for yield reduction. This result is similar to the conclusions of the effect of different SWP to leaf area and plant height. The pepper yield of treatment N1 was less than those for treatment N3, though irrigation water amount was greater in treatment N1 as compared to N3, The lower yield for N1 may be due to excessive irrigation and high soil water content, which restrained the growth of root and thereby affected the yield. Ensico et al. (2008) studied the irrigation regimes of onion and found that higher

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total yields were obtained when the soil moisture was kept above -30 kPa, and total yields dropped significantly when soil water stress increased below -50 kPa[24]. Du et al. (2007) studied the irrigation regime of pepper in greenhouse in North-West China, and obtained a yield of 36.5 t / ha when the irrigation amount was 217.5 mm[25]. Pepper yield in our experiment is similar to those of Du et al. (2007), while irrigation water in this study decreased by 38 mm. Besides, the yield of treatment N6 was the second highest among the six treatments, which show that the conventional irrigation method can also obtain a high yield. Water use efficiency for the six treatments is listed in Table 1. WUEs of treatment N3 and N4 were 11.5 kg/m3 and 11.4 kg/m3, respectively, which were greatly higher than those of N2 (9.8 kg/m3) and N5 (9.9 kg/m3). The WUE for treatment N1 was 8.3 kg/m3, the least among the five treatments, which may be due to the greatest ET. Wan et al. (2007) found that high WUE of cucumber can’t be obtained if the SWP threshold was too high[26]. Kang et al (2004) found in a drip-irrigated potato fields that the highest yield and WUE were found when SWP is -25kPa and the least for -55kPa. S. Metin Sezen et al. (2006) studied the effect of drip irrigation regimes on field grown pepper and concluded that WUE and IWUE values decreased with increasing irrigation intervals[27]. These results are similar to our conclusion. Our results also show that the WUE of treatment N6 was higher than N1, N5 and N2, but a little lower than N3 and N4. It can be concluded that drip irrigation method with a suitable SWP can make a higher WUE than traditional irrigation.

4 Conclusion From the mulched and drip irrigated pepper experiment, conducted in northwest China, we found that SWP levels affect the growth, fruits quality, yield and water use efficiency of hot pepper greatly. The main results are summarized as follows: Soil water content is higher with scarce variation in the whole growth period when the SWP level is -10 kPa. Irrigation amount and water consumption decrease as the SWP threshold decrease. The relation between them can be fit as a quadratic polynomial. Controlling SWP levels between -10kPa and -40kPa does a minor effect on leaf area index and plant height. The higher TSS, yield and water use efficiency are found when SWP levels are between -30kPa and -40kPa. SWP of -50kPa will greatly reduce crop leaf area index, height, TSS, yield and water use efficiency. Comprehensively considering the pepper fruits quality, yield and WUE, controlling SWP levels of -30 kPa to -40 kPa at 20 cm depth immediately under drip emitter can be recommended as an indicator for pepper drip irrigation scheduling in northwest China.

Acknowledgement This research was partly supported by the Non-profit Industry Financial Program of MWR (grant number: 200801104) and the State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau (grant number: 10501 260). The authors

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greatly acknowledged the staff in Shiyanghe Experimental Station for Water-saving in Agriculture and Ecology of China Agricultural University for their supports in our field experiment.

References 1. Yuan, Y., Chen, L.Z., Li, X.L.: Pepper production and demand situation of the world. J. Yunnan Agricultural Science and Technology 5, 42–43 (2004) 2. Guo, Y.L., Guo, X.Z., Song, K.Q.: Standardization of pepper cultivation techniques for high yield and quality in Wuwei. J. Gansu Agricultural Science and Technology 6, 18–19 (1995) 3. Peng, S.Z.: The Precipitation Features of Shiyang River basin. J. Gansu Water Conservancy and Hydropower Technology 45(9), 7–8 (2009) 4. Kang, S.Z., Li, X.L.: Study on evolution and its driving forces of water utilization structure of Shiyang River Basin in Northwest arid areas. J. Agricultural Research in the Arid Areas 26(1), 125–130 (2008) 5. Liu, M.C., Liu, X.L.: Effect of different irrigation methods on plant growth and yield of tomato. J. Acta Agriculturae Boreali—Sinica 20(1), 93–95 (2005) 6. Hanson, B., May, D.: Effect of subsurface drip irrigation on processing tomato yield water table depth, soil salinity, and profitability. J. Agricultural Water Management 68(1), 1–17 (2004) 7. Li, Q.Z., Hao, W.P., Gong, D.Z.: Effects of different irrigation patterns on soil water dynamics, water use and yields in an Apple Orchard. J. Agricultural Research in the Arid Areas 25(2), 128–132 (2007) 8. Yang, P.L., Ren, S.M.: Study on The Countermeasure to Develop WSI of Installed Agriculture. J. Water Saving Irrigation 1(2), 7–9 (2001) 9. Wang, Y.X.: Pepper Plant Fertilizer and Water in the Role and Impact Studies. J. Modern Agricultural Sciences. 16(5), 107–108 (2009) 10. Yang, H., Jiang, H., Zhan, Y.F.: Effects of Different Conditions of Irrigation and Fertilization on Dry Matter Accumulation. Nutrient Absorption and Yield of Hot Pepper. J. Crops 6, 26–29 (2008) 11. Huo, H.X., Niu, W.Q., Wang, Y.K.: Influence of Irrigation Volume to Hot Pepper Growth. J. Yellow River 30(2), 55–58 (2008) 12. Lei, Z.D., Yang, S.X., Xie, L.C.: Soil Water Dynamics, p. 7. Tsinghua University Press, Beijing (1988) 13. Zhang, M.Z., Shi, X.L.: A Study of the Eeffects of Different Soil Water Potentials and Optimum Water Potential Value of Irrigation on Rape. J. China Rural Water and Hydropower (5), 8–11 (1992) 14. Li, Y.L., Cui, Y.L., Li, Y.H.: Research on Water Saving Irrigation by Using Soil Water Potential as Irrigation Criterion. Journal of Irrigation and Drainage 23(5), 14–16 (2004) 15. Marouelli, W.A., Silva, L.C.: Water tension thresholds for processing tomatoes under drip irrigation in Central Brazil. Irrigation Science 25, 411–418 (2007) 16. Muirhead, W.A., White, R.J.G.: The Influence of Soil Water Potential on the Flowering Pattern, Pod Set and Yield of Snap Beans (Phaseolus vulgaris L.). Irrigation Science (3), 45–56 (1981) 17. Kang, Y.H., Wang, F.X., Liu, H.J.: Potato evapotranspiration and yield under different drip irrigation regimes. Irrigation Science 23, 133–143 (2004) 18. Wang, D., Kang, Y.H., Wan, S.Q.: Effect of soil matric potential on tomato yield and water use under drip irrigation condition. Agriculture Water Management (87), 180–186 (2006)

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19. Jiao, Y.P., Kang, Y.H., Wan, S.Q.: Effect of soil matric potential on waxy corn growth and irrigation WUE under mulch drip irrigation in saline soils of arid areas. Agricultural Research in the Arid Areas 25(6), 144–151 (2007) 20. Wang, F.X., Kang, Y.H.: Scheduling drip irrigation for potato with tensiometers. Agricultural Research in the Arid Areas 23(3), 58–64 (2005) 21. Zhang, C.F.: Techniques for raising Pepper seedlings in Greenhouse. Scientific Experiments in Rural Areas (12),13 (2001) 22. Enciso, J., Wiedenfeld, B., Jifon, J., Nelson, S.: Onion yield and quality response to two irrigation scheduling strategies. Scientia Horticulturae 3135, 1–5 (2008) 23. Ma, F.S., Kang, S.Z., Wang, M.X.: Effect of regulated deficit irrigation on water use efficiency and fruit quality of pear–jujube tree in greenhouse. Transactions of the CSAE 22(1), 37–43 (2006) 24. Zhang, H., Zhang, Y.L., Yu, N.: Relationship Between Low Irrigation Limit and Yield, Water Use Efficiency of Tomato in Under–Mulching–Drip Irrigation in Greenhouse. Scientia Agricultura Sincia 39(2), 425–432 (2006) 25. Du, J., Sheng, Z.R., Tian, J.C.: A comparative study of greenhouse pepper under several irrigation ways. Ningxia Engineering Technology 6(2), 143–146 (2007) 26. Wan, S.Q., Kang, Y.H., Wang, D.: Effects of saline water on cucumber yields and irrigation WUE under drip irrigation. Transactions of the Chinese Society of Agricultural Engineering 23(3), 30–35 (2007) 27. Metin Sezen, S., Yazar, A., Eker, S.: Effect of drip irrigation regimes on yield and quality of field grown bell pepper. Agricultural Water Management 81, 115–131 (2006)

Study on Thermal Conductivities Prediction for Apple Fruit Juice by Using Neural Network Min Zhang1,*, Zhenhua Che1, Jiahua Lu1, Huizhong Zhao2, Jianhua Chen1, Zhiyou Zhong1, and Le Yang1 1

College of Food Sciences, Shanghai Ocean University, Shanghai, 201306, P. R. China [email protected] 2 School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai, 200093, P. R. China

Abstract. Based on experimentally measured values by thermal probe method, the prediction model of thermal conductivities of apple fruit juice as a function of concentration and temperature was studied by neural network method. The optimal neural network was made of two hidden layers and every hidden layer had six neurons. The prediction result shows that the optimal model could predict thermal conductivity with a mean relative error of 0.11%, a mean absolute error of 0.00054W/mK, a mean standard error of 0.00039 W/mK, the linear relationship of 0.9993. The calculated precision was higher for BP neural network model than that for dual regression model. The presented results were proved that this model can be used with satisfactory accuracy for the prediction of thermal conductivity of apple fruit juice. Keywords: Thermal conductivity, Neural network, Apple fruit juice.

1 Introduction Apple fruit juice as a traditional consumption is widely available on the Chinese market. It is a complex mixture of vitamins, pectin, sugar, mineral salts, organic acids andamino acid. Accurate values of thermophysical parameters over a wide temperature range are needed in the fruit juice industry in order to design and optimize the operation of handling and processing units [1]. Thermal conductivity of fruit juice is one of the important thermophysical parameters used to estimate the rate of heat transfer during processes, such as preservation, transportation and freezing. Generally, mathematical models which express the dependence of thermophysical properties on temperature and concentration are a very appealing alternative to experimentation, and a useful tool for the implementation of computer-aided routines for equipment design and process automation [2]. Over the years an extensive investigation on existing methods of measurement of thermal conductivity of fruits juice has been carried out [1][2][3]. Some empirical equations have been proposed for the estimation of thermal conductivity of various *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 198–204, 2011. © IFIP International Federation for Information Processing 2011

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fruits juice products as a function of composition [4][5]. In these equations one can easily verify that thermal conductivity of fruits juice depends strongly on the water content, since water has the highest thermal conductivity of all food components [6]. In recent years, artificial neural networks as a kind of analysis method were obtained extensive attentions by food engineer. It has become increasingly popular during the last decade. The advantages of this method compared are its high speed, simplicity and large capacity which reduce engineering effort [7]. It can be used successfully in various fields of modeling and prediction in many engineering systems, mathematics, medicine, economics, meteorology and many others [8]. The objective of this study is to develop a BP artificial neural network model to predict the thermal conductivity of apple juice as a function of concentration and temperature and to compare the BP artificial neural network model established with dual regression model by the calculated precisions.

2 Methodology The development of a neural network model involves: the generation of data required for training/testing, the training/testing of the BP artificial neural network model, the evaluation of the BP artificial neural network configuration leading to the selection of an optimal configuration, and validation of the optimal BP artificial neural network model with a data set not used in training before [9]. 2.1 Data Generation of Thermal Conductivity of Apple Juice The thermal conductivity of apple juice at different concentration and temperature were measured using a tiny thermal probe method. It has been described in detail in our previous studied [10]. In this study, the thermal conductivity probe system was proposed, which needs short duration of the experiments, simplicity, high accuracy and relatively small sample requirement. The system worked satisfactorily for its application. The effects of concentration in the range of 0-70° Brix and temperature in the range of 3-50 on the thermal conductivity of apple juice were determined. The 192 data sets with different thermal conductivities affected by concentrations and temperatures were obtained through using this measurement system. The thermal conductivity of apple juice decreased with an increase in concentration while it increased with an increase in temperature. In addition, the influence of concentration on the thermal conductivity of apple juice was stronger than that of temperature. A double-factors regressive equation of thermal conductivity of apple juice as a function of concentration and temperature was obtained:

℃

k = 0.582 + 0.0012t − 0.0035s

(1)

Where k is the thermal conductivity of apple juice, W/m K; s is the concentration of the apple juice (° Brix) and t is the temperature of the apple juice, .

℃

2.2 Establishment of BP Neural Network A multi-layer feed forward BP network structure with input, output and hidden layer(s) was used in this study as shown in Figure 1. It consists of the feed-forward

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pass and backward pass process. The input layer includes two neuron parameters of concentration and temperature. The output layer is the thermal conductivity of apple fruit juice, hidden layer suppose n neurons before training. The network is processed through two stages: training stage and validation stage [11]. BP neural network achieves full connectivity between the upper and lower layers, and no connections between neurons in each layer. The neural cells of input and output layers were decided by the practical input and output parameters [12]. In the training stage, the network was trained to predict an output based on input data. In the validation stage, the network was validated to stop or continue training. The neurons of hidden layer were decided on the error decline in the training course. The learning process was composed of positive signal transmission and the error back-propagation. The actual output value was calculated by the process information flow from input layer, hidden layer to output layer by layer. If the actual output of the output layer does not match with the expected output, the error back-propagation stage was started [13]. The error signals of neural cells were based on this error. The each unit weight can be amended by the error signal values until the network output error was reduced to tolerant ranges.

Fig. 1. Model of BP neural network

The indicators of thermal conductivity of apple fruit juice including the mean absolute error MAE, mean relative error MRE, standard deviation SE, coefficient of determination R2 can judge the right type of performance of the network and optimize neural network model. The four indicators used to compare the performance of various BP artificial neural network configurations were calculated by the following expressions [9]:

MAE =

1 N

N

∑k c =1

m

− kp

(2)

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MRE =

SE =

1 N km − k p ∑ N c =1 k m

∑

N c =1

(3)

(k m − k p ) 2

(4)

N −1

R 2 = 1 − ∑c =1 N

201

(k m − k p ) 2

(5)

k m2

Where N is the number of training samples; km and kp are the actual measurement values and the neural network fitting calculated values of thermal conductivity.

3 Results and Discussion The thermal conductivity data sets of the 144 cases in our study were divided into two groups; training and testing sets. The BP neural network model was trained using 116 randomly selected data (accounting for 80% of the total data) while the remaining 28 data (accounting for 20%) was utilized for testing of the network performance. In order to improve training characteristics, the thermal conductivity data set experimentally obtained were normalized using a simple normalization method. The Maximum and minimum values of the inputs and outputs and normalization values ere given in Table 1. Table 1. Input and output parameters ranges and normalization values Parameters Concentration,% Temperature, Thermal conductivity, W/mK

℃

Minimum value 0 3 0.341

Maximum value 70 50 0.647

Normalization value 70 40 0.646

The modeling program was implemented under MATLAB. The BP learning algorithm has been used in feed-forward, which included an input layer, a hidden layer and an output layer network. There are two input parameters in the input layer, namely the temperature difference and concentration of the apple juice. The output is the thermal conductivity of apple juice. The number of neurons in the input layer is equal to the number of input parameters and the number of neurons in the output layer is the number of output parameters. Optimal number of the neurons in the hidden layer was determined by trying different networks[8]. The number of neurons was increased from 1 to 8 based on the trial and error method in the hidden layer.

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Input layer and hidden layer use the sigmoid transfer function for the transfer function, the output layer use the purelin function, in order to improve the prediction accuracy, the test results would be normalized, so that concentration Ci [0,1], temperature ti [0,1], thermal conductivity kmi [0,1]. In the process of model, the network is initialized by means of Newff function in Matlab neural network toolbox, the trained model parameters set as follows: the maximum epoch of 10000, network performance goal error of 10-6, learning rate of 0.02, momentum constant of 0.9. The errors associated with desired output response are adjusted in the way that reduces these errors in each neuron from the output to the input layer. When the network output error was reduced to the required tolerance, the training automatically stopped. According to equation (2) - (5) the neural network established was training and the error of the fitting calculation results of each network was analyzed. It is found that best structure of the model has six neurons in the first and second hidden layer presented in Table 2. The MRE, MAE, SE and R2 for this optimal configuration were 0.92%, 0.0047 W/mK, 0.000525 W/mK, and 0.9957, respectively. So the optimized BP neural network model architecture has a configuration of 2–4–4–1 neurons.

∈

∈

∈

Table 2. Error parameters with different BP neural network structure Hidden layers 1 1 1 2 2 2

Neurons 1 2 4 2 4 6

MRE (%) 1.11 1.04 1.09 1.07 0.92 0.97

MAE (W/m·K) 0.0056 0.0052 0.0055 0.0054 0.0047 0.0049

SE (W/m·K) 6.66E-04 6.13E-04 6.49E-04 6.25E-04 5.25E-04 5.65E-04

R2 0.9930 0.9941 0.9933 0.9938 0.9957 0.9950

The prediction thermal conductivity of apple juice for the optimized BP neural network model with a configuration of 2–4–4–1 neurons were compared with the experimental ones as shown in Figure 2 for the 116 training data set. The results demonstrated good agreement between the predicted and the experimental values of thermal conductivity (R2=0.9957). The performance of the optimal neural network (two hidden layers and six neurons in each hidden layer) was validated using a smaller data set consisting 28 cases. Correlation between predicted and measured values of thermal conductivity was shown in Figure 3.This network predicted thermal conductivity values with an MRE of 0.11%, MAE of 0.00054 W/mK and SE of 0.00039W/mK, respectively. It still kept a good agreement between the predicted and the measured values of thermal conductivity (R2=0.9993). The results presented that this model can be used with satisfactory accuracy for the prediction of thermal conductivity of apple fruit juice. The mean relative error of 28 prediction values of thermal conductivity cases in this model is smaller than that in double-factors regressive equation whose mean relative error is 1.73% [10].

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Fig. 2. Comparison of measured and predicted values of thermal conductivity for the training sets

Fig. 3. Comparison of measured and predicted values of thermal conductivity for the validation sets

4 Conclusion In this work, the prediction model of thermal conductivities of apple fruit juice as a function of concentration and temperature was studied by neural network method. The optimal neural network was made of two hidden layers and every hidden layer had six neurons. The optimal neural model could predict thermal conductivity with MRE of 0.11%, MAE of 0.00054W/mK, SE of 0.00039 W/mK, R2 of 0.9993 while the mean relative error in double-factors regressive equation was 1.73%. The prediction

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precision of BP neural network model was higher than that of dual regression model. The presented results were proved that this neural network model can be used with satisfactory accuracy for the prediction of thermal conductivity of apple fruits juice and further applications. Acknowledgments. Funding for this research was provided by the Natural Science Foundation of China, Grant No 30771245 and Leading Academic Discipline Project of Shanghai Municipal Education Commission, Project No J50704.

References 1. Frandas, A., Bicanic, D.: Thermal properties of fruit juices as a function of concentration and temperature determined using the photopyroelectric (PPE) method. Journal of the Science of Food and Agriculture 79, 1361–1366 (1999) 2. Telis-Romero, J., Telis, V.R.N., Gabas, A.L., Yamashitah, F.: Thermophysical Properties of Brazilian Orange Juice as Affected by Temperature and Water Content. Journal of Food Engineering 38, 27–40 (1998) 3. Shamsudin, R., Mohamed, I.O., Yaman, N.K.M.: Thermophysical properties of Thai seedless guava juice as affected by temperature and concentration. Journal of Food Engineering 66, 395–399 (2005) 4. Zainal, B.S., Abdul Rahman, R., Ariff, A.B., Saari, B.N., Asbi, B.A.: Effects of temperature on the physical properties of pink guava juice at two different concentrations. Journal of Food Engineering 43, 55–59 (2000) 5. Azoube, P.M., Cipriani, D.C., El-Aouar, A.A., Antonio, G.C., Murr, F.E.X.: Effect of concentration on the physical properties of cashew juice. Journal of Food Engineering 66, 413–417, 79, 1361–1366 (1999) 6. Saravacos, G.D., Kostaropoulos, A.E.: Transport properties in processing of fruits and vegetables. Food Technol 49, 99–109 (1995) 7. Kalogirou, S.A.: Artificial neural networks in the renewable energy systems applications: a review. Renew Sustain Energy Rev. 5, 373–401 (2001) 8. Kurt, H., Kayfeci, M.: Prediction of thermal conductivity of ethylene glycol–water solutions by using artificial neural networks. Applied Energy 86, 2244–2248 (2009) 9. Sablani, S.S., Baik, O.-D., Marcotte, M.: Neural networks for predicting thermal conductivity of bakery products. Journal of Food Engineering 52, 299–304 (2002) 10. Zhang, M., Zhao, H.Z., Xie, J., Sun, Z.Q., Zhang, B.L.: Experimental study on influence factors of thermal conductivities of apple juice. Transactions of the CSAE 22, 162–165 (2006) 11. Yuan, Q.X.: Prediction for the Effect of Temperature and Water Content on the Soil Specific Heat by BP Neural Network. Transactions of the CSAE 39, 108–111 (2008) 12. Zhang, M., Zhong, Z.Y., Yang, L., Zhao, H.Z., Chen, J.H., Che, Z.H.: Prediction model of thermal conductivities of fruits and vegetables based on BP neural networks. Transactions of the CSAE 41, 117–121 (2010) 13. Xu, X., Yu, Z.T., Hu, Y.C., Fan, L.W., Tian, T., Cen, K.F.: Nonlinear fitting calculation of wood thermal conductivity using neural networks. Journal of Zhejiang University 41, 1201–1204 (2007)

Prediction of Agricultural Machinery Total Power Based on PSO-GM(2,1, , ) Model

λ ρ

Di-yi Chen1,2,*, Yu-xiao Liu1, Xiao-yi Ma1, and Yan Long2 1

Electrical Engineering Department of College of Water Resources and Architectural Engineering, NorthWest A&F University, 712100 Yangling, Shaanxi, China 2 College of Mechanical and Electric Engineering, NorthWest A&F University, 712100 Yangling, Shannxi, China [email protected], [email protected]

Abstract. In order to improve the prediction accuracy of agricultural machinery total power then to provide the basis for the agricultural mechanization development goals, the paper used gray GM(2,1) model in the prediction. Through the introduction of parameter λ to correct the background value and parameter ρ for multiple transformation on the initial data, the model was expanded to GM (2,1, λ , ρ ) model and prediction accuracy was improved. Because of the nonlinear traits between parameter λ , ρ and the prediction errors, they are difficult to be solved. The paper used Particle Swarm Optimization (PSO) to search the best parameter λ , ρ , then combination forecast model of PSOGM(2,1, λ , ρ ) was constructed. In order to avoid incorrect selection of inertia weight w causing the global search and local search imbalance, the paper used Decreasing Inertia Weight Particle Swarm Optimization, in which parameter w gradually decreases from 1.4 to 0.35. And agricultural machinery total power was predicted based on Zhejiang province’s statistics. Predicted results show that the combination forecast model prediction accuracy is higher than the gray GM(1,1) model and the model better fits the data. The forecast of the agricultural machinery total power of this combination forecast model is feasible and effective, and should be feasible in other areas of agriculture prediction. Keywords: agricultural machinery total power; gray prediction; particle swarm optimization; background values; multiple transformation.

1 Introduction Agricultural machinery total power is the total power number that is used for agriculture, forestry, animal husbandry and fishery production and transportation of all machinery (including tractors, diesel engines, gasoline engines, turbines, motors, wind turbine, etc.), which reflects the general level of agricultural mechanization in an area. It is an important part of agricultural planning and is often listed as key indicators of planning [1]. There are many specific methods in the prediction of agricultural machinery total power, the gray system has been widely used because of the advantages of less data needed and small amount of computation. However, the current *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 205–210, 2011. © IFIP International Federation for Information Processing 2011

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model applied to the agricultural forecast is just gray GM(1,1) model, which is actually a deviation of the index model, so it subjects to certain restrictions in application. Because of GM(2,1) model has two feature values, it can reflect the system monotonous or oscillations and should be widely used in theory, but its goodness of fit and prediction accuracy in practical application is not very satisfactory [2]. This paper uses GM(2,1) model, references the method of Literature[3], through introducing parameter λ to correct the background value and parameter ρ for multiple transformation on the initial data, expands the model to GM (2,1, λ , ρ ) model, increasing the prediction accuracy. Particle swarm optimization (PSO) [4-5] is an evolutionary computation technique developed by J. Kennedy and R. C. Eberhart in 1995. In this algorithm, the objective function is initialized to a random solution, and each solution is regarded as a particle. Each particle has a initial position and a speed which decides the search direction and distance. The merits of particles are determined by the size of the fitness according to the objective function. Particles update themselves according to their fitness and speed, inertia weight w is used to maintain the balance between the global search and local search, larger inertia weight help search out of local minimum points, while the smaller are conducive to make algorithm converge and to improve search accuracy. Original PSO, inertia weight w is a constant, so is it in the literature[3], literature[6] proposed an adaptive strategy, that is just at the beginning inertia weight w is large, as the iteration progressing, it decreases linearly. Literature[7] thinks that inertia weight is started at 1.4 and then gradually decreases to 0.35 is appropriate, so this paper documents the approach of literature[7]. Examples show that the algorithm improves the data fitting accuracy and its relative error is smaller. So it can reveal the development law of agricultural machinery total power within the next few years, and is able to provide an important basis for agricultural machinery development plans.

2 GM (2,1, λ , ρ ) Modeling 2.1 GM (2,1, λ , ρ ) Modeling Principle Original data sequence is located as: X 0 = ( x0 (1), x0 (2)" x0 (n)) . Do multiple transformation on the data columns. Multiplication parameter ρ has the effect of zoom in or out of the amendment on the initial data, can adjust condition number of the model coefficients, turns original ill-conditioned matrix into a good state, and does not change development coefficient of the model[8], the transformed data sequence is: (0)

{

(0)

( 0)

X (0) = x (0) ( k ) x (0) ( k ) = ρ x0 ( k ), k ∈ N ( 0)

(0)

}

When this sequence is subjected to the Accumulating Generation Operation (AGO), the following sequence X (1) is obtained: k ⎧ ⎫ X (1) = ⎨ x (1) ( k ) x (1) ( k ) = ∑ x (0) (i ) , k ∈ N ⎬ i =1 ⎩ ⎭

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When this sequence is subjected to inverse AGO (IAGO), the following sequence α (1) X (1) is obtained: α (1) X (1) = {α (1) x (1) (k ) α (1) x (1) (k ) = x (0) (k ) − x (0) (k − 1), k ∈ N } The whitening equation of GM (2,1) model is therefore, as follows: d 2 x (1) dx (1) + a1 + a2 x (1) = u 2 dt dt aˆ = [ a1

a2

(1)

u ] is a sequence of parameters that can be found as follows: T

aˆ = [a1 a2 u ]T = B −1Y ⎡ − x (0) (2) − z (1) (2) 1 ⎤ ⎢ (0) ⎥ − x (3) − z (1) (3) 1 ⎥ B=⎢ ⎢ # # # ⎥ ⎢ (0) ⎥ (1) ⎢⎣ − x (n) − z (n) 1 ⎥⎦ The original background value is defined as: z (1) (k ) = 0.5 x (1) (k ) + 0.5 x (1) (k − 1) .

The revised background value by introducting of the parameter [1] is as below: z (1) (k ) = (1 − λ ) x (1) (k ) + λ x (1) (k − 1)

Y = ⎡⎣α (1) x (1) (2) α (1) x (1) (3) " α (1) x (1) (n) ⎤⎦

T

For equation (1), when the discriminant of its characteristic equation Δ = a12 − 4a2 > 0 , it can get two real roots v1 =

− a1 + Δ −a − Δ , v2 = 1 , then we can get the general 2 2

solution: xˆ (1) (k ) = C1ev1 k + C2ev2 k +

u a2

The parameters C1 , C2 in formula are determined by the initial value condition.. By applying IAGO, the predictions for the X (0) can be obtained: xˆ (0) (k ) = xˆ (1) ( k ) − xˆ (1) (k − 1)

The prediction model of the original data sequence is: xˆ0 (k ) = xˆ (0) (k ) / ρ (0)

2.2 Improved PSO Algorithm to Search the Best λ , ρ Generate randomly M groups λ , ρ according to population to form a two-dimensional initial swarm population ( λρ )0 and the fitness value is evaluated by a predefined fitness function of the absolute relative error summation. Then search the optimal value λ , ρ for its minimum, the particles update themselves according to fitness and speed. Velocity and position update formula are as follows: vk +1 = wvk + c1 * rand1 ( pbestk − xk ) + c2 * rand2 ( gbestk − xk )

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x k +1 = x k + v k +1 v k and x k are the particle's current velocity and position, v k +1 and x k +1 are updated particle velocity and position, pbestk is the optimal solution which the particle itself searches, ie the individual extreme, gbestk is the optimal solution that the entire particle population searches, ie the global extreme. c1 and are accelerating factors within [0,2], in this paper c1 = c2 = 2 . rand1 and rand 2 are two random numbers within [0,1]. And w decreases linearly from 1.4 to 0.35. Fitness function is: n

f = min ∑ abs ( i =1

100( xˆ0(0) (i) − x0(0) (i )) ) x0(0) (i)

3 Applications of GM (2,1, λ , ρ ) Model Based on Zhejiang Province’s statistics in literature[1] agricultural machinery total power was predicted. Fitness situation is shown in Figure 1, and fitness results are shown in Table 1. From Table 1 we can see that the maximum fitting relative error 12.1271% is smaller than the largest fitting relative error of -24.12% in literature [1], the average fitting relative error -1.5432% is less than -2.6105% in literature[1]. Fitting the initial data more accurate, the model is more in line with the trend of the initial data then can predict with a higher precision. Predicted results from 2004 to 2008 as shown in Table 2:

Fig. 1. Total power fitting image of Zhejiang Province

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Table 1. Total power fitting results of Zhejiang Province

Years

1. 1982 2. 1983 3. 1984 4. 1985 5. 1986 6. 1987 7. 1988 8. 1989 9. 1990 10.1991 11.1992 12.1993 13.1994 14.1995 15.1996 16.1997 17.1998 18.1999 19.2000 20.2001 21.2002 22.2003 Average

Fitting Agricultural The fitted values relative machinery total power inliteratu e 3 errors in /×104kw literature /×104kw 3 /%

Fitting relative The fitted values in this errors in this paper/% paper /×104kw λ = - 3.8990

1. 628.10 2. 681.86 3. 754.49 4. 810.02 5. 889.31 6. 974.62 7. 1075.91 8. 1133.48 9. 1215.73 10.1265.46 11.1352.03 12.1417.88 13.1497.34 14.1639.80 15.1707.59 16.1733.33 17.1798.84 18.1912.53 19.1990.01 20.2017.24 21.2053.21 22.2039.68

1. 628.1 2. 764.5502 3. 819.5266 4. 876.9505 5. 936.8025 6. 999.0459 7. 1063.6241 8. 1130.4588 9. 1199.4471 10.1270.459 11.1343.3344 12.1417.88 13.1493.8662 14.1571.0232 15.1649.0374 16.1727.5475 17.1806.14 18.1884.3447 19.1961.63 20.2037.3977 21.2110.9775 22.2181.6218

1. 779.58 2. 820.42 3. 863.41 4. 908.64 5. 956.25 6. 1006.35 7. 1059.07 8. 1114.55 9. 1172.95 10.1234.40 11.1299.07 12.1367.13 13.1438.75 14.1514.13 15.1593.46 16.1676.94 17.1764.80 18.1857.25 19.1954.56 20.2056.96 21.2164.72 22.2278.14

1. -24.12 2. -20.32 3. -14.44 4. -12.18 5. -7.53 6. -3.26 7. 1.57 8. 1.67 9. 3.52 10.2.46 11.3.92 12.3.58 13.2.74 14.7.66 15.6.58 16.3.25 17.1.89 18.2.89 19.1.78 20.-1.97 21.-5.43 22.-11.69 -2.6105

ρ = 0.8658

1. -0.0000 2. -12.1271 3. -8.6199 4. -8.2628 5. -5.3404 6. -2.5062 7. 1.1419 8. 0.2665 9. 1.3394 10.-0.3950 11.0.6432 12.-0.0000 13.0.2320 14.4.1942 15.3.4290 16.0.3336 17.-0.4058 18.1.4737 19.1.4261 20.-0.9993 21.-2.8135 22.-6.9590 -1.5432

Table 2. Predicted results from 2004 to 2008

1. Years 2004 2. Total power/×104kw 2248.4991

2005 2310.6882

2006 2367.1715

2007 2416.8281

2008 2458.4267

4 Conclusion This paper constructed a combination forecast model of PSO-GM(2,1, λ , ρ ), and we can draw the following conclusion: (1) The introduction of parameters λ , ρ to adjust the original solution can better improve the precision, that increases applications of gray GM (2,1) model.

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(2) Using the advantages of PSO algorithm for global optimization and local optimization to search the best parameters λ , ρ , making the parameter values flexible, so the model prediction accuracy is high. (3) This prediction method on data is of high precision and prediction accuracy meets the practical requirements in the forecast of the agricultural machinery total power, so this study introduces a practical method to agricultural mechanization forecast.

References 1. Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981) 2. Hong, Y.Q., Song, Z.M., Jin, D.: Predict and Analysis of Agricultural Machinery Total Power in Zhejiang Province Based on GM (1, 1). Journal of Agricultural Mechanization Research 1, 42–44 (2008) 3. Xiao, X.P., Song, Z.M., Li, F.: Gray Technology Fundation and Application. Science Press, Beijing (2005) 4. Liu, H., Zhang, Q.S.: GM(2,1,λ,ρ) based on particle swarm optimization. J. Systems Engineering –Theory & Practice 10, 96–101 (2008) 5. Kennedy, J., Eberhart, R.C.: Particle swarmoptimization. In: Proceeding of 1995 IEEE International Conference on Neural Networks, New York, pp. 1942–1948 (1995) 6. Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, pp. 39–43 (1995) 7. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of 1998 IEEE International Conference on Evolutionary Computation, New York, USA, pp. 69–73 (1998) 8. Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments,applications and resources. In: Proceedings of the 2001 Congress on Evolutionary Computation, NJ, USA, pp. 81–86 (2001) 9. Zeng, X.Y., Xiao, X.P.: Research on morbidity problem “Research on morbidity problem of accumulating method GM(2,1) model. J. Systems Engineering and Electronics 28, 542– 572 (2006) 10. Sun, T.-H.: Applying particle swarm optimization algorithm to roundness measurement. J. Expert Systems with Applications 36, 3428–3438 (2009) 11. Kayacan, E., Ulutas, B., Kaynak, O.: Grey system theory-based models in time series prediction. J. Expert Systems with Applications 37, 1784–1789 (2010)

Prediction of Irrigation Security of Reclaimed Water Storage in Winter Based on ANN Jinfeng Deng Environmental Science and Engineering College, Huangshi Institute of Technology, Huangshi, P.R China

Abstract. The difference of reclaimed water qualities between in summer and in winter were proved considerable. Reclaimed water derived in spring or summer could be applied irrigating in some arid region, which have been considered a rational renewable resource of water. Yet in winter, reclaimed water would run off into river or stream in vain. The idea of storing it in winter and being ready for the next year's irrigation is proposed, however, the effects on crops are maintained sealed. Experiments processed using raw sewage, primary treated water, and secondary treated water in Gaobeidian Sewage Plant of Beijing to irrigate maize and soybean in spring and summer from 2006 to 2007, based on which the irrigation effect derived from reclaimed water of winter was predicted by ANN. The results validated the strong relations between irrigation effect and qualities of water, which indicated the reclaimed water stored in winter was grossly similar to secondary treated water in summer for irrigation. Keywords: Reclaimed water, storage, irrigation security, ANN.

1 Introduction Reclaimed water derived from industrial and domestic effluents make the role of water resource in some area where the climate is rather dry and irrigation agriculture is the main trend. Quality of reclaimed water may be various based on the effluent quality and treat method, which result in the different usage mode such as industrial recovered water, scenic environmental water, agricultural irrigation water, domestic water, until drinking water. Standardization Administration of the People's Republic of China (SAC) has established serial standards for reclaimed water using and the Standard of Reuse of Urban Recycling Water Quality of Farmland Irrigation Water have been establishing. As the condition of drinking water we concern to, we could not detect all of the parameters involved in water quality but only some parameters. Domestic sewage derived from house holding, commerce, school, hospital, and so on, could include washing water, toilet-flushing water, and the others. A serial standards and rules with regard to medical waste have been established since 2003 in China, so the waste volume from hospital descent to little. However, medical waste should be monitored strictly in the collecting and treating process. Industrial sewage composes of production sewage and production wastewater, the former is the water polluted by D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 211–217, 2011. © IFIP International Federation for Information Processing 2011

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production raw material and intermediate products and the latter is the one which may not be polluted directly in production process but only be changed the temperature or some physical characteristics, for example cooling water. Generally speaking, the production sewage is the major source of pollution, which may contain some heavy metals, toxicity, acid or alkali materials. On the other hand, the production wastewater and domestic waster could be the main reclaimed water source for recycling use. In Beijing, the treatment plant received little by little industrial sewage along with the sequent moving of polluting enterprises, and the ratio of effluent volume of domestic sewage to the sum is rather large in China. In 2005 the volume of domestic sewage reaches 8.82 108t, simultaneously the total effluent is 10.10×108t. Irrigation with reclaimed water is a simple, low-cost, user-friendly way to help conserve our fresh drinking water supplies. In China, the volume of agricultural irrigation water dominates the consumption of water of total quantity, i.e. over 65%. But the water resource of China is not sufficient Per capita quantity of water resource is only 2300m3, about one forth of ones in the world, while China is the biggest nation of irrigation. Numerous studies indicate that long-term poor quality water irrigation could cause the farm soil and ground water damaged. According to a recent survey to twenty poor quality water irrigation districts, most districts' ground water has been polluted apart from 4 ones. Previous studies indicated that the water quality is the major factor that causes the soil and ground water pollution. Reclaimed water effects the environment by polluting air, soil, ground water and damaging crop. Reclaimed water effecting the environment and crop subject to the season in arid area for water qualities in different season could be various at some levels. Generally speaking, during winter season the concentration of salt, nutrients, heavy metal, and so on, could be higher than during summer season, which cause the different affection on the environment or irrigation security in different seasons. On the other hand, in winter season the reclaimed water could not be used in irrigation but disposal to the environment, while in summer the quantity of reclaimed water could be insufficient for irrigation in arid area. Reclaimed water of winter season often has been discharging into rivers in vain. Could it be use for irrigation in summer via proper storage way of winter season with safety? To solve the problem, we might irrigate crop with reclaimed water stored in winter season but this is a rigescent way which is not able to dispose some different quality reclaimed water else. Artificial Neural Net (ANN) could be a feasible way to predict the affection and security of irrigating crop with stored reclaimed water of winter season.

×

2 Materials and Methods How would irrigation water of specific quality effect the environment or crop's growth? The experiments by using the specific quality water to irrigate the crop in growth season, which may solve the previous problem but it is noneffective to the various qualities water else. Prediction with ANN may be a feasible utility to definite the irrigation effect using each kind water including reclaimed water. Assuming we meet the same cultural condition such as climate, rainfall quantity, soil, irrigation mode, and so on, then we can decide the different quality irrigation water result in the different

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irrigation effect. This is a black box problem for the reason of mechanism is black to us. To get to know the irrigation effect and security of specific quality water such as reclaimed water from treatment plant in winter, some water which quality is well known would be using as the irrigation water. This study designed potted planting and field experiment and trained the ANN system using several kinds of reclaimed water of raw sewage, secondary reclaimed water, third level reclaimed water and fresh water. ANN demand large amounts of data to train the net, otherwise it would be inadequately trained and result into inaccurate conclusion. Cubic spline interpolation was used to extend the amount of data. The irrigation water was raw sewage, secondary reclaimed water, third level reclaimed water and fresh water, which respectively derived from Gaobeidian Treatment Plant of Beijing and tap water. Potted plant soil was mixed soil with ground surface 0-80cm from Tongzhou district of Beijing. Planted seeds of maize and soybean were bought from Crop Research Institute of Chinese Agriculture Academy of Science (CAAS). The experiment of potted planting and field experiment has processed respectively in Gaobeidian Treatment Plant and in Institute of Agricultural Environment and Sustainable Development of CAAS. Here are the qualities indices of irrigation water and ones for prediction of specific quality water in these experiments (Table 1). As shown in Table 1, extension of sample data, normalization of data, training of neural network, and prediction should be completed in a program. In this study back propagation (BP) network has been used to archive these processes. Training function adopted variable learning rate back propagation VLBP, illustrated with parameter traingdx in Matlab R2006b. A complete process illustrated the previous function as follows (Table2).

，

Table 1. Quality indices of irrigation water /mg·L-1

Raw sewage

Secondary reclaimed water

5

187

9.25

Third level reclaimed water 5.85

Cr

360

38.10

312

Component

BOD COD SS TN TP Cl Pb Cd Zn EC /μS·cm -

-1

Fresh water

Irrigation Water for prediction

0.94

10.10

23.8

0.94

42.60

12.20

5.55

4.00

12.80

43.40

30.10

20.05

7.81

33.60

4.97

1.95

2.02

0.02

3.60

167

133

143

45.9

167

0.28

0.31

0

0

0.31

0.02

0.01

0

0

0.01

0.24

0.13

0.05

0.02

0.13

1497

1199

1184

491

1199

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Table 2. Matlab (R2006b) procedure of artificial neural network

function prev=spnn(trainp,traint,prep)

：spnn.m

% file name

% call format:

spnn(trainp, traint, prep)

，m by n traint- output matrix of training sample，r by n

%

trainp- input matrix of training sample

% %

prep- input matrix of prediction•m by c

% output matrix is r by c if size(trainp,2)~=size(traint,2) return; end spn=500; spp=.8; interpolation

% sample size after extension % smoothing coefficient of cubic spline

if size(trainp,2)<200 for i=1:size(trainp,1) trainpSp(i,:)=csaps(1:size(trainp,2),trainp(i,:),spp,lins pace(1,size(trainp,2),spn)); end for i=1:size(traint,1) traintSp(i,:)=csaps(1:size(traint,2),traint(i,:),spp,lins pace(1,size(traint,2),spn)); end else trainpSp=trainp; traintSp=traint; end

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Table 2. (continued)

[trainpSpN,trainpSpS]=mapminmax(trainpSp); [traintSpN,traintSpS]=mapminmax(traintSp); net=newff(repmat([-1 1],size(trainp,1),1),[15,size(traint,1)], 'tansig','purelin' , 'traingdx','learngdm');

｛

｝

net.trainparam.epochs=500000; net.trainparam.goal=1e-008; net.trainparam.show=200; net0=train(net,trainpSpN,traintSpN); prepN=mapminmax('apply',prep,trainpSpS); pretN=sim(net0,prepN); pret=mapminmax('reverse',pretN,traintSpS); prev=prêt;

3 Results and Discussions Serial results have been obtained about potted planting and field experiment from experiences in 2005. Before prediction on specific quality irrigation water, the network has been validated with indices of dry weight, plant height, mean grain weight, water using efficiency and it showed the network is efficient. Then the results could be made as follows (only partial prediction result was provided). Table 3. The content of total C&N in potted maize’s root, stalk and leaves

treatment

Raw sewage S T F prediction

root Total C(%) 34.49ab 40.34ab 31.53b 43.40a 36.49

total N (mg/g) 10.90 8.55 8.53 7.90 9.42

stalk total total N C(%) (mg/g) 51.78c 10.03b 41.20b 6.68a 41.69b 9.00b 30.81a 6.63a 39.70 7.35

leaves total total N C(%) (mg/g) 37.54a 17.87b 44.77b 15.40ab 40.12c 15.47ab 36.31a 11.95a 40.55 18.54

The different letters represent significant difference in same column.

(1) distribution of component of C, N in potted maize C in plant is the result of photosynthesis, which indicate the plant growth condition and could be the quality standard as animal feed. N in plant performs an important

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function during the plant growth and indicates the absorption of plant nutrition. The prediction of effect irrigated by specific water (Table 3) shows C and N content in the plant root and stack would be close to the one by secondary treated water but the leaves not. It shows the specific water would accelerate the plant growth because the nutrient level is rather high and prior to the secondary treated water. Table 4. Content of lead and cadmium in pot cultured maize’s root, stalk and leaves /mg·kg-1

roots treatments Raw sewage S T F prediction

Pb 3.11c 2.66ab 3.13bc 2.29a 2.85

stalks

Leaves

Cd

Pb

Cd

Pb

Cd

0.22 0.19 0.19 0.16 0.20

1.29 1.67 1.41 1.53 1.54

0.04a 0.07b 0.04a 0.04a 0.06b

1.87 1.83 1.90 1.78 1.83

0.03 0.05 0.04 0.03 0.04

The different letters represent significant difference in same column.

(2) content of some heavy metals in potted maize’s root, stalk and leaves Table 4 shows the content of lead and cadmium in different parts in the potted maize irrigated by four kinds of water and the ones of prediction with the specific water. Content of prediction is rather high and close to the secondary treated water and the content in root is similar to the one of raw sewage. The content of heavy metal in plant is not the simply linear to the one in irrigation water but correlates to the nutrient level.

4 Conclusions The content of C and N in root and stack approximates to the secondary treated water of experiment, but it is not the same condition in plant leaves, the content of C is close to that in third reclaimed water and total N is largest in all levels, which indicates the special water is in favor of absorbing nutrient and photosynthesis and with more nutritive value for animal feed. The content of Pb in plant approximate to secondary reclaimed water in experiment and is between secondary reclaimed water and sewage, which is quite large. Content of Pb in stack (1.54mg kg-1) is close to the level using flesh water in experiment. Cd is largest irrigated by sewage and third, secondary and flesh water in turn, prediction value is close to third reclaimed water in root and largest in leaves and stack using secondary one. So it is essential to attention the content of Cd and Pb in stacks and leaves.

·

Acknowledgement This research was financially supported by Educational Commission of Hubei Province of China (D20104403).

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References [1] Chin, K.K.: Pretreatment to produce ultrapure water from reclaimed sewage. Desalination 106, 269–272 (1996) [2] Al-Shammiri, M., Al-Saffar, A., Bohamad, S.: Waste water quality and reuse in irrigation in Kuwait using microfiltration technology in treatment. Desalination 185, 213–225 (2005) [3] Verdonck, F.A.M., Boeije, G.: A rule-based screening environmental risk assessment tool derived from EUSES. Chemosphere 58, 1169–1176 (2005) [4] Ma, H.W., Hung, M.L., Chen, P.C.: A systemic health risk assessment for the chromium cycle in Taiwan. In: Environment International, pp. 1-13 (2006) [5] Kochera, D.C., Greim, H.: An approach to comparative assessments of potential health risks from exposure to radionuclides and hazardous chemicals. Environment International 27, 663–671 (2002) [6] Miyamoto, K.-i., Naito, W., Nakanishi, J.: Application of an ecosystem model for aquatic ecological risk assessment of chemicals for a Japanese lake. Water Research 36, 1–14 (2002) [7] Chapman, P.M.: Ecological risk assessment (ERA) and hormesis. The Science of the Total Environment 288, 131–140 (2002) [8] Fernández, E.C.M.D., Vega, M.M.: Ecological risk assessment of contaminated soils through direct toxicity assessment. Ecotoxicology and Environmental Safety 62, 174–184 (2005)

Progress of China Agricultural Information Technology Research and Applications Based on Registered Agricultural Software Packages Kaimeng Sun Laboratory of Digital Agricultural Early-warning Technology of Ministry of Agriculture of China, Institute of Agricultural Information of Chinese Academy of Agricultural Sciences, 100081 Beijing, P. R. China

Abstract. By using classified agricultural software products registered during last 15 years from 1994 to 2009, the author analyzed data and statistics in order to look at growth in number of the agro-software packages, their application areas, and research and development organizations, as well as their contribution to the agricultural IT industry in China. Based on the analysis, the article also assessed current agricultural IT sector development and provided some suggestions on further progress. Keywords: Agricultural Information Technology, Applications, Agricultural Modernization.

1 Introduction It is well accepted that development software for agricultural application marks the progress in IT applications in rural development, as well as an important indicator of a country’s modern agriculture. Along with fast development of computer technologies in hardware and software, information technology products such as computers, TV, and cell phones have become popular in rural areas in China at a dramatic speed. China’s central government, local administrations, and agricultural departments have invested much more in IT in the agricultural sectors. Over the recent past years, computers are more popular and network infrastructure has been development at unprecedented speed in China’s rural areas. These provide conditions for IT applications in the agricultural sector, as well as require more on the R&D on agricultural software products. In other words, due to more computers and Internet are used in some rural counties, agricultural software run on the computers becomes an urgent need for IT developers. China’s agricultural IT (AIT) has been developed since 1980s. Early focuses were on agricultural data processing, databases, agricultural expert systems, and crop modeling systems, etc. These applications were generally based on collecting lab data, few directly applied in practical agricultural production and rural lifestyle change. Since the approaching in to IT era, information services for agricultural production, agro product processing, product mobilization, and agricultural supply chains become critical and need software products in providing the services. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 218–226, 2011. © IFIP International Federation for Information Processing 2011

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Compared with agricultural IT R&D team in 20 years ago, it also changed fast. Most of agricultural research institutes and agricultural universities in China have their own developer capacities. Computer technologies are widely applied in their agricultural science research activities becoming popular means. In the mean time, more and more scientific academies, universities, and IT companies are involved in agricultural IT studies and development. Agricultural IT is becoming an area with great potential. As China’s vast territory, rich agricultural resources, big agricultural population, and huge contribution of agriculture in the economy, IT applications in agricultural sector will be developing faster in the future. In order to look at the current status of agricultural software development in China, the author collected data on officially registered 867 agricultural software packages through searching website of the Copyright Protection Center of China (CPCC) by agricultural related key words. The statistics data are further analyzed to reflect current status of AIT applications and development and further indicate the AIT future. The registered 867 software packages were developed during the past 15 years from 1994 to 2009. During the fifteen years, the number of annual software developed their application areas changed significantly. Partly because of general factors such as increased awareness of copyright protection for software products and computer technology development, the 15 years witnessed progress in agricultural IT application development. From the early in house development to providing AIT services, to today’s practical applications in agricultural production and life quality improvement, AIT enters a new era of practical applications.

2 Quantity of Agricultural Software Packages Since the first such software for agricultural applications registered in 1994, the quantity of AIT systems registered grew each year. Especially by 2003, the quantity grew

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several times. The following figure 1 gives a picture of growth in quantity of the 867 AIT systems registered at China Copyright Protection Center. Some conclusions can be drawn from the quantity growth: 2.1 Number of Developed AIT Systems Grow over the Years Since 1990s, agricultural information technology has developed fast in China. Particularly since the beginning of 21st century, as agriculture development obtained strong national policy support, information technology in agricultural sectors has been greatly invested by central and local government which incentivized research and development. Therefore, it is a natural that the number of agricultural software of the agricultural research results increased greatly. Applications of the agricultural information technology has entered to a new development era in terms of increased software packages, improved software quality, and extended application areas. 2.2 Improved Capacities in Agricultural Software Research and Development The increased number of software packages is partly due to more and more developers and research capacities. As a large agricultural sector, Chinese central government paid great attention to the sector investment so that research and development capacities have significantly improved and therefore promoted more agricultural software packages. 2.3 Improved Awareness of Software Copyright Protection With more and more agricultural software products in the market, the awareness of intellectual copyright is improved among researchers and software developers. It is realized gradually that software registration would be one of the best approach to protect developers’ efforts and their copyright in the software market. More registered software packages also mark the capacity of developing organizations and software scientists and become an important indicator of scientific research results. Acceptance of intellectual copyrights by the public also encouraged agricultural science research and software development in return.

3 AIT Applications According to the statistics of the registered agricultural software packages, author identified 9 classes of the packages in terms of their application areas: information service for macro agricultural related decision making, agricultural economy management, village management, crop production management, animal husbandry production management, rural business management, agricultural ecological monitoring and control, agricultural information service, and agricultural software tools, mainly including software modules and developing tools.

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Crop production

Animal husbandry production

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Fig. 2. The distribution of the 867 agricultural software packages in application areas

From the above figure 2, we can draw the following findings: 3.1 Crop Production Management Software Is Most Widely Applied Among the registered agricultural software packages, 187 packages are agricultural crop production management software, or 21.2%, concerning wheat, corn, cotton, rice, beans, and vegetable crops. Functions of the software packages include numerical simulation of crop growing process, planting management, pest diagnose, crop management decision support, and expert systems. As grain and economic crops are main agricultural products in China, computerized information systems are mostly utilized to help solve problems during crop production and become focal point of agricultural research. Since crop production is critical in China’s agricultural sector, production decision support software packages take the major market of software products, including decision support on breed selection, fertilizing, irrigation method, optimal yields, pest prevention, field management measures, etc. Crop models, expert systems, and DDS have been developed to practical applications to provide services for agricultural production services. This is very positive trend. Many software packages focus on both agricultural science laws and practical problem solving objectives. 3.2 More Attention to Agricultural Information Service Functions Information service software packages can include agro-product supply, information service, market information, network, and technical training information, etc. Among the survey, this type of software packages take 19.1%, totaled 166. Because vast Chinese territory are rural areas, many remote countryside communities has poor information access and with very traditional production ways. Through agricultural information service such as Internet and cell phone information dissemination, it helps local farmers to communicate with market and provide useful technical information and exchanges. Their crop product information will also be known through the network by demand side. These systems provide stored data, service functions, and rich market information. It can promote market oriented agricultural production, encourage product

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chain, and help increase farms income so that the agricultural sector will be more effective, convenient, and developed. 3.3 Environmental Protection Gained More Focus in the AIT Research This class of software packages has functions on data processing, analysis, and information service of water resources, soil, and climate data. Natural resources are basis for agricultural production and rural life because all aspect in agricultural production and rural life will depend on local natural conditions. Therefore, the agricultural environment software type focus on functions of climate disaster forecast, field condition monitoring, soil water content analysis, irrigation planning, land uses, etc. The increasing availability of this type of software packages indicate that agricultural conditions get more attention and research on the agricultural environment is going deeper. To make sure favorable agricultural environment will be necessary for prosperous agricultural production and for local farmer income. 3.4 Software Applied for Animal Husbandry Production Maintains Continuing Progress Early in 1980s, computer programs were used for feed formula in China. Software applied for animal husbandry production therefore became one of the earliest applications. Along with the improved computer technologies, animal husbandry software packages develop accordingly. Applications also expanded from the early feed formula to animal husbandry production quality, poultry disease diagnoses, computerized production management, and so on. Especially because of recent fast development, the animal husbandry software packages are targeting large farms and household farmers. Since animal husbandry’s high profit than crop farming, they buy these packages. According the survey, there are 82 registered software packages for animal husbandry applications, accounting for 9.5% of the total in the part 15 years. 3.5 New Applications for Rural Economy and Village Management As China central government focus on agriculture and rural development, daily management in rural areas becomes one of the fast growth in AIT applications. Office automation in rural areas will include financial management, administrative affairs, and material management, especially in financial planning, debt management, clinic operation, farmer management, etc. Application of these software packages make the management in rural areas improved greatly. It helps efficient management. There are a total of 113 rural economy and management software packages surveyed. In particular, rural management software developed very fast since 2005.

4 AIT Research and Development Capacities 4.1 Agricultural Research Institutes and Universities Are Major Developers of AIT Packages According to the statistics over copyright holders of the 867 software packages, 337 or 38.9% are developed by agricultural academies, 89 packages are developed and owned

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by agricultural universities, accounting for 10.3%. Sum of the two types are about 50% of the surveyed agricultural software packages. This demonstrates that agricultural research institutes and universities are major developers and take the lead in software research and development.

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Among the agricultural research organizations, national and provincial agricultural academies take the lead in terms of rich agricultural data sets, sufficient developer team, excellent computer infrastructures, and large number of various agricultural experts. Meanwhile, local and national financial support also makes them the mainstream of agricultural information technology research and development. 4.2 The Important Role of Computer Software Companies There are 241 software packages were developed by computer software companies, accounting 27.8% of the total. The author finds out that software companies play important role when end users are not professional in developing the software. Some software companies obtain their developing contract from bidding national and local government projects. Over the recent years, professional agricultural software development companies grow fast, partly owned by agricultural research organizations and market oriented independent companies specialized in software development. Involvement of the software companies help introduce advanced technologies into the agricultural software market and help improved abundance of the products. At the same time, they also promoted progress of AIT software and benefit both company business and AIT advancement.

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4.3 More Institutes and Universities Take Part in Agricultural Software Development More and more non-agricultural research institutes, colleges, and universities are involved in the AIT R&D activities, organizations such as Chinese Academy of Sciences, Wuhan University, etc. Some of them develop the software packages through winning bid for national and local projects; some take part in the joint research program. The universities and research institute take their advantages of technical strength and make their contribution in the agricultural studies. This joint development helped focus on agricultural issues, and contributed to competition with agricultural sector capabilities. Of the surveyed software packages, 101 registered software systems were developed and owned by academies and universities, accounting for 11.6% of the total. 4.4 Most of the Property Rights Owned by Organizations Of the agricultural software package registrations, 808 packages are owned by organizations (93.2%). In China, these organizations are major developers, including research institutes, universities, software companies, or government departments. Even some privately registered packages may have been funded by some organization. This percentage tells us that most of the AIT products are funded publicly and their objectives are public service oriented. We can affirm that under the current rural economy, it is not practical for AIT project profitable enough. Social benefit is still a major objective.

5 Conclusions and Recommendations From the above research and development status and analysis on the registered agricultural software packages, we can conclude the following in terms of progress and challenges: 5.1 AIT Develops Successfully in China The registered 867 agricultural software packages in the past 15 years demonstrate a fast development. Significant progress has been made in copyright protection, capacity building, and expanded application areas. Due to the increasingly high attention to agricultural sector in China, information technology roles in supporting agricultural production and technical service have become common understanding by researchers and developers. This has changed significantly compared with the situation in 1980s when only few devoting organizations and lack of technical capacities. 5.2 R&D on AIT Products Is Developing towards Effectiveness and Efficiency In the past, software development focused more on technical advancement but practical applications. Many projects ended with submitted research results, with very weak application support. Now because computers are popular in agricultural sectors, insufficient software systems become a challenge. Current study projects attach great

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importance to demonstration and applications. Therefore, many software packages are developed for such demonstration and application purposes, such that features and functions of the software systems are more local condition oriented, and achieved satisfactory results. 5.3 R&D of Agricultural Software Meets New Demands of Today’s Agriculture Development Like other sectors, agricultural software must follow updated demand on functions. Along with improved agriculture management and farmer education, some critical problems must be addressed in AIT system development. For instance, early focus on crop models and expert systems in dealing with planting and management must be further improved to solving problems of agricultural product quality. Some recently developed packages focus more on quality and safety. More and more software are using to trace quality in production chain. Again, environmental protection has drown more attentions such that increasingly more software packages are used in analyzing water and land resources, human resource optimization and monitoring. 5.4 Popularization of AIT Systems Must Be Strengthened There are anyway only hundreds of registered agricultural software packages. This is far insufficient in terms of China’s large rural area and agricultural population. Even of the current software packages, very limited number of packages is suitable for agricultural production and rural population demands. Many such packages need to be improved for better agricultural applications. Therefore, central and local governments need to apply and popularize the existing products. If the heavily invested current systems were not fully applied, it will be a huge waste. Though some specially planned demonstration and application projects, it is not enough. Further investment shall include both capital and human resources to improve the effectiveness and efficiency of agricultural software applications. Otherwise AIT progress will be not as expected. Promotion of agricultural software needs joint efforts by government bodies, research institutes and rural users by working together closely. Under the current situation, sales of the AIT software systems can hardly profitable. Therefore, capital support from government will be needed for software development, technical training, and hardware investment. Only by these supports, agricultural software can be applied effectively and efficiently. Currently, improving applications of existing agricultural software packages is an important task for promoting AIT applications. Only by increasingly more applications, can the research and development of AIT systems progress at a sound basis. In return, the national and local investment will be paid off and AIT development progressed. This will facilitate AIT to provide greater contribution to agriculture production and to farmers’ technical service. And finally it will contribute to modern agriculture in China.

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Acknowledgement This research was supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science and Technology of China(2006BAD10A06, 2006BAD10A12), Special Fund of Basic Scientific Research and Operation Foundation for Commonweal Scientific Research Institutes (2008J-1-06).

Reference Copyright Protection Center of China (CPCC), http://www.ccopyright.com.cn/cpcc/

Quantification Research on Different Load Weight-Bearing Running Biochemical Indexes of Rats Huaping Shang* School of P.E. East China Jiaotong University, Nanchang, Jiangxi, P. R.China Tel.: +86-13970081211 [email protected]

Abstract. Research Objective: To discuss exercise performance and biochemical reactions of different load weight-bearing running of rats. Research Methods: 24 male 3-month-old SD rats were randomly divided into 3 groups (n = 8), namely, a none weight-bearing running group, a 35% maximum load weight-bearing running group, a 75% maximum load weight-bearing running group. After weight-bearing running training for 6 weeks, the maximum weight-bearing capacity, the blood lactic acid value and serum CK value of rats were tested and compared. Research Results: 30m/min speed running and 45m/min speed running respectively enabled the lactic acid value to generate first and second inflection points; weight-bearing running increased the serum CK-M value with the increase of bearing weight and load, and the blood lactic acid value and the serum CK value were significantly increased. Conclusion: 30m/min is the anaerobic threshold intensity of rat running; maximum load capacity of rats is about 65% of sole weight; weight-bearing running with appropriate load can not damage muscle fibers and can effectively mobilize type muscle fiber, 75% maximum load weight-bearing running can mobilize type muscle fiber to be fully raised, but can cause muscle fiber damage.

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Keywords: weight-bearing running; exercise performance; quantification.

1 Research Objective Rat resistance training is internationally and widely adopted in biological adaptation mechanism of researching on strength training, the load running training still has many problems in experiments as an effective method of resistance training. Since weight-bearing running training comprises two exercise load modes of weight-bearing and running, the load effect of running is mainly to improve the functions of respiratory and circulatory system, it is also still effective in improving muscle endurance simultaneously, and weight-bearing training has the main value of developing muscle maximum strength and power endurance. In weight-bearing training, the size of load strength may have different influences on muscle functions and even different types of muscle fibers. Therefore, When training plans are formulated in rat weight-bearing run *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 227–233, 2011. © IFIP International Federation for Information Processing 2011

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experiments, the quality and quantification features of running and gravity load must be made clear, simultaneously, the boundary among maximum weight-bearing ability, aerobic exercise and anaerobic exercise of rats should be comprehended. The experiment aims at discussing the exercise performance and biochemical reactions of running with different loads and bearing weights of 3-month-old rats, thereby providing quantitative basis of exercise load control for rat weight-bearing running experiments, and also providing reference for rat related exercise experiment in the future.

2 Materials and Methods 2.1 Experiment Object 24 clean male 3-month-old SD rats were provided by Animal Center of Jiangxi Medical College and were fed in Animal Room of Animal Center of Jiangxi Medical College. Breeding conditions comprised: free water and food feeding, natural lighting, room temperature of 18DEG C to 22 DEG C, humidity of 40% to 45%; normal feedstuff feeding, and the feedstuff was provided by Animal Center of Jiangxi Medical College. 2.2 Experimental Grouping 24 rats were randomly divided into 3 groups: a none weight-bearing running group, a 35% maximum load weight-bearing running group, a 75% maximum load weight-bearing running group with eight rats in each group. 2.3 Instruments and Reagent (1) ZH-PT-type Animal Experiment Treadmill produced by Anhui Huaibei Zhenghua Bioinstrumentation Equipment Co, Ltd; (2) YSIl500 SPORT Blood Lactic Acid Analyzer produced by United States Gimcheon Instruments Inc; (3) TU-1810 UV-Vis Spectrophotometer produced by Beijing Persee Instrument Co., Ltd; (4) MP200A Type Electronic Balance produced by Shanghai Second Balance Instrument Factory; (5) Creatine Kinase Kit produced by Beijing Biosino Bio-technology and Science Inc; (6) Blood Lactic Acid Test Kit produced by Nanjing Jiancheng Biological Technology Co, Ltd. 2.4 Experimental Methods (1) Rat maximum load capacity test: heavy objects were applied on waists and backs of rats (iron half-cylinder bodies with the diameters of 2cm and 0.6cm with the length of 3cm, the weights were 80 grams and 10 grams respectively), the weight was progressively increased, and the increase grades were 80 grams, 40 grams and 10 grams respectively. The weight which enabled rats to be incapable of walking was defined as the maximum load intensity in rats. (2) Determination of anaerobic threshold in rats: the treadmill was inclined for 0 degree. 8 rats were made to run for 3min at the speed of 25m/min, tail blood was sampled at the moment’s notice, 1min, 1.5min, 2min, 2.5min and 3min before and after running for blood lactic acid analysis, thereby measuring the time of rat muscle lactic

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acid and blood lactic acid to reach equilibrium. 8 rats were made to warm up for 2min at the speed of 10m/min, then, the rats were made to run without stop for 2min under gradual increasing load intensity of 25m/min, 30m/min, 35m/min, 40m/min, 45m/min, 50m/min, 55m/min and 60m/min, and the tail blood was immediately sampled at the end of each grade load respectively. (3) Comparison of blood lactic acid values after weight-bearing and non weightbearing running of rats: rats were respectively made to bear no weight, and to bear 75% maximum load bearing weight and 35% maximum load bearing weigh (n = 8) and were made to run for 2min at the speed of 25m/min, and blood was sampled in 1.5min after running for testing blood lactic acid values. Blood lactic acid tests adopted YSIl500 SPORT lactic acid analyzer, and testing process was operated according to kit instructions. (4) Comparison of serum CK and CK-MB values of rats after exercise under different loads and bearing weights: rats were respectively made to bear no weight, and to bear 75% maximum load bearing weight and 35% maximum load bearing weigh (n = 8) and were made to run for 2min at the speed of 15m/min, the tail blood was sampled immediately after running for CK and CK-MB test. Test methods adopted electro-optic colorimetry and could be finished on TU-1810 UV-Vis spectrophotometer. Testing process was operated according to kit instructions. 2.5 Statistical Analysis Measurement information and data were represented with mean ± standard deviation, data statistics was processed through adopting SPSS15.0 statistical software, and statistical methods adopted LSD multiple comparison method for analysis.

3 Results and Discussion 3.1 Experimental Results (1) Maximum load capacity of rats. In the incremental load test, it could be obtained from table 1 that the even maximum load capacity of rats was 65% of sole weight through computing according to rat weight percentage. Table 1. Rat Weight and Average Maximum Load Capacity (n = 8) Group None weight-bearing group 35% maximum load weight-bearing running group 75% maximum load weight-bearing running group

Average BodyWeight (g) 209±15.12

Maximum Load Capacity (g) 136.18±47.2

235±9.13

154.18±10.3

198±13.25

128.18±30.4

(2) Testing Result of Rat Anaerobic Threshold. The time for muscle lactic acid and blood lactic acid to achieve balance in the experimental test was 1.5 minutes. In order to carry out rat weight-bearing running training, changes of blood lactic acid of rats in the process of running with different intensities should be firstly comprehended, thereby

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effectively controlling aerobic and anaerobic components in rat weight-bearing running training. In the continuous run process, with the increase in the intensity of running, anaerobic components of energy metabolism was gradually increased, showing an increase in muscle lactic acid production. Gravity load also changed energy metabolism forms in the muscle work process. In order to distinguish causes of generating lactic acid in rat weight-bearing running training, the effect direction of load intensity of running should be comprehended, thereby determining the influence of loading on muscle energy metabolism in rats. The experiment results showed that the blood lactic acid of rats was gradually increased with the increase of load intensity, the inflection point appeared in the speed point of 30m/min. When the speed was increased to 45m/min, the second inflection point of blood lactic acid increase appeared. The first inflection point appeared between running speed of 30m/min and 35m/min, blood lactic acid was increased by 1.46mmol / L and increased very significantly. The second inflection point appeared between the running speed of 45m/min and 50m/min, blood lactic acid was increased by 1.80 mmol / L, and the increase extend was further enlarged. In Table 2, Treadmill the first inflection point 30m/min speed of blood lactic acid changes in rat increasing load running could be defined as rat anaerobic threshold speed. Table 2. Blood Lactic Acid Values during Rat Increasing Load Running (n = 8) Speed (m/s) Blood Lactic Aci (mmol/L) Speed (m/s) Blood Lactic Acid (mmol/L)

25 3.15±0.37 45 5.79±0.64

30 3.63±0.49 50 7.59±0.33

35 5.09±0.38 55 8.17±0.69

40 5.32±0.41 60 8.58±0.47

(3) Blood Lactic Acid Value of Rats after Running with Different Loads and Bearing Weights and after Non-weight Bearing Running. In Table 3, the after-running blood lactic acid highest value was correspondingly increased with the increase of bearing weight and load of rats after 75% maximum load weight-bearing group (big load group), 35% maximum load weight-bearing group (small load group) and none weight-bearing group running. The small load weight-bearing group was averagely increased by 3.63mmol / L compared with none weight-bearing group. The big load weight-bearing group was averagely increased by 4.12mmol/L compared with small load weight-bearing group. It was indicated that running with different loads and bearing weights had extremely prominent influence on blood lactic acid values. Table 3. Blood Lactic Acid Value of Rats after Running with Different Loads and Bearing Weights (n = 8) Group Big load group Small load group None weight-bearing group

Blood Lactic AcidValue (mmol/L) 11.09±2.09 6.97±1.12 3.34±0.43

P Value * P< 0.05 ** P< 0.01 *** P< 0.05

* Big load group was compared with the small load group; ** big load group was compared with the none weight-bearing group; *** small load group was compared with none weight-bearing group.

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(4) Rat serum CK and CK-MB Value Testing Results of Running with Different Loads and Bearing Weights. From Table 4, it could be seen that with the increase in weight-bearing load, CK values were increased significantly, while the prominence level of differences among different groups was also increased. CK-MB was also increased, but the increase was not obvious, especially the changes between small weight-bearing group and the big weight-bearing group were not obvious, and there was no significant difference. Table 4. Rat Serum CK and CK-MB Values (n = 8) of Running with Different Loads and Bearing Weights Group None weightbearing group Small load group Big load group

CK (U/L)

CK-MB (U/L)

150.65±18.39

27.89±35.78

301.03±28.56

66.01±18.52

690.05±87.05

63.29±31.77

P Value (CK)

P Value (CK-MB)

*P< 0.05

*P< 0.05

**P<0.01

**P> 0.05

* Different prominent levels between none weight-bearing group and the small load group; ** Different prominent levels between small load group and big load group.

3.2 Analysis and Discussion (l) The average maximum load capacity of rats was about 65% of their sole weights, the determination standard treated the load that rats could essentially stand up for support and could simply walk as limit load, this movement mode did not always reflect the overall features of rat muscle function. In addition, the rat loading mode used in this study was to apply heavy objects on waists and backs of rats, load was dispersedly acted on rat's limbs and trunk, this load capacity embodied rat systemic comprehensive endurance. The results of this experiment only provided reference basis for load control of weight-bearing run training of 3-month-old rats, its change rules of comprehensive weight-bearing capacity with age needed further study. (2) When rat lactic acid threshold speed was 30m/min and the speed achieved 45m/min, the increase in blood lactic acid generated the second inflection point. In this study, twice blood lactic acid inflection points in rats were results of twice changes of anaerobic metabolism process, the first inflection point marked that the anaerobic glycolysis functional systems began to be greatly employed, temporary equilibrium between energy consumption and supply occurred immediately after employ, with further increase of exercise intensity, the second inflection point occurred, which marked that the potential of body anaerobic glycolysis power supply system functions was further developed. Scientists often treated the time between the appearance of the second inflection point to the end of exercise as important indexes for measuring tolerance fatigue ability of athletes, and the second inflection point was used to control intensity of anaerobic capacity training of athletes. Two inflection points obtained in this study were only inflection points of blood lactic acid curves, and had certain correspondence with two inflection points of exercise physiology states in human physiology, the appearance mechanisms of two

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inflection points may have certain same mechanisms in twice changes of physiological function states obtained in studies in human physiology, and it was concrete performance of body function capacity change on blood lactic acid value in rat exercise. The blood lactic acid change form of the experiment can provide reference data of exercise intensity control for future rat exercise experiments. (3) The blood lactic acid highest value was increased with the increase of bearing weight and load after running at the speed of 25m/min under no bearing weight, small load bearing weight and big load bearing weight, and as the increase of load intensity, the increase extend of blood lactic acid had increasing trend. This study showed that when rats run under the speed of 25m/min, the rat body energy metabolism was basically in aerobic metabolism, after running, the blood lactic acid was little increased, thereby, the increase of blood lactic acid after bearing weight could be considered to be caused by changes in muscle working status. The blood lactic acid in the exercise process was mainly generated by anaerobic glycolysis in muscles, the contents and activities of amber mitochondrial malate dehydrogenase in different types of muscle fiber were different, the contents and activities in II-type fiber were higher than that of I type fiber, thereby it can be inferred that when the II type fiber was more used, the blood lactic acid contents after exercise was increased considerably. It could be further inferred that 75% load running can effectively mobilize II type muscle fiber recruitment. In weight-bearing training, the effect directions of different load intensities were always important tissues of sports training study, according to features of different sports games, athletes need to develop different muscle strength qualities and develop different types of muscle fibers; athletes of speed and strength type need to develop II type muscle fibers, the neurons for dominating II type muscle fiber is large, excitation threshold is high, only high-intensity resistance load can effectively enable II type muscle fiber to be fully collected for participating in contraction. In this study, the blood lactic acid results of different load weight-bearing running trainings support this view. Namely, large weight-bearing load running more fully uses II type muscle fiber, the main form of II-type muscle fiber energy metabolism is glycolysis, and thus increase extent of blood lactic acid after running is higher than that of big load weight-bearing group and none weight-bearing group. (4)75% maximum load weight-bearing running could result in a certain degree of damage in muscle fibers, while 35% maximum load weight-bearing running could not cause injury. Two kinds of maximum load weight-bearing running could not cause obvious damage of myocardial fibers. In experimental studies in mammals, CK-M and CK-B in serum CK were frequently applied, CK-M and CK-B 22 were combined to form CK-MM, CK-MB and CK-BB, CK-MM was usually a skeleton muscle source, CK-MB was a cardiac source, whereas CK-BB was a brain source. In the studies, the activity of three isozymes was often used to compare with total CK activity to determine the skeletal muscle, heart muscles, or brain injury and its extent. CK was commonly found in muscle cells, muscle fiber micro-injury may lead to increased permeability of fibrous membrane, and thereby CK could be very vulnerable to enter into the blood through the muscle cell membrane, so that CK was significantly increased. Field of sports medicine treated increased activity of CK and its isoenzymes as a sensitive indicator of muscle damage judgments, but in animal experiments,

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quantitative criteria to judge these indicators were still deficient. Su Quansheng [1] studied and found that when rat CK and CK-MM were above 409 U / L, the injury symptoms of muscle fibers were more obvious. Cui Yupeng [2] studied and found that when rat CK was up to 600U / L, the skeletal muscle damage and CK were significantly correlated. The results of this study were compared, it could be determined that the rat CK value reached 690U / L after weight-bearing running of 75% the maximum load, it was shown that the weight-bearing running under load intensity may cause certain damage to muscle fibers. However, CK value was 301U / L after weight-bearing running of 35% maximum load, it was shown that the load and weight-bearing running could not cause significant damage in skeletal muscle.

4 Summary Through different load weight-bearing running experiments of 3-month-old SD rats on 0 degree slope treadmill, it is showed that: 30m/min speed running is the rat anaerobic threshold intensity; the rat maximum load capacity is about 65% of their sole weight. Load can prominently increase blood lactic acid of rats under aerobic running intensity, the weight-bearing running with appropriate load can not damage muscle fibers and can effectively mobilize type muscle fiber; 75% maximum load weight-bearing running makes II muscle fiber to be more fully raised, but may cause minor damage to the muscle fibers.

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References 1. Su, Q., Tian, Y., Sun, J., et al.: Phasic Changes of Blood Interleukin -6, Creatine Kinase and Isoenzyme Thereof after Rat Exercise Skeletal Muscle Damage. Chinese Sports Medicine Magazine 25(2), 176–180 (2006) 2. Cui, Y., Yang, Z., Zhou, L., Gao, H., Xu, B.: Skeletal Muscle Damage, Plasma CK and Isoenzyme Activity Level Changes of Rat after Different Load Swimming Sports. Transaction of Capital Institute of Physical Education (01), 83–85 (2004) 3. Wei, A., Wei, X., Shi, R., Zhao, C.: Influence of Vibration Training on Greatest Strength and Muscle Fiber Morphology of Rat Skeletal Muscle. Journal of Chinese Sports Medicine (01), 83–84 (2009) 4. Issurin, V.B., Tenenbaum, G.: Acute and residual effects of vibratory stimulation on explosive strength in elite and amateur athletes. Journal of Sports Sciences 17, 177–182 (1999) 5. Luo, J., Mcnamara, B., Moran, K.: The use of vibration trainingto enhance muscle strength and power. Sports Med. 35(1), 23–41 (2005) 6. Torvinen, S., Kannus, P., Sievanen, H., et al.: Effect of four-month vertical whole body vibration on performance and balance. Med. Sci. Sports Exerc. 34, 1523–1528 (2002)

Rapid Determination of Ascorbic Acid in Fresh Vegetables and Fruits with Electrochemically Treated Screen-Printed Carbon Electrodes Ling Xiang1,2, Hua Ping1,2, Liu Zhao1,2, Zhihong Ma1,2, and Ligang Pan1,2 1

Beijing Research Center for Agrifood Testing and Farmland Monitoring, Beijing 100097, P. R. China 2 Beijing Research Center for Information Technology in Agriculture, Beijing 100097, P. R. China [email protected]

Abstract. This study demonstrates a new electrochemical method for rapid determination of ascorbic acid in real samples with electrochemically pretreated screen-printed carbon electrodes. Cyclic voltammetry results indicated that the electrochemically activated electrodes possessed an excellent electrocatalytic activity toward ascorbic acid oxidation and can be successfully used for its selective and sensitive determination in fresh vegetables and fruits. The proposed method was further validated by means of specificity assay with ascorbate oxidase and a comparison with an accurate method such as high-performance liquid chromatography using ultraviolet detection. The electrodes presented the advantages over other existing methods such as mass production and without artificial modification. This study essentially offers a facile and reliable method for rapid determination of ascorbic acid in food analysis with high selectivity and sensitivity as well as low cost. Keywords: Ascorbic Acid, Electrochemical Pretreatment, Screen-Printed Electrodes, Vegetables, Fruits.

1 Introduction Increasing evidences have revealed that ascorbic acid (AA) possesses lots of different functions in the physiological and pathological processes, such as an important antioxidant and free radical scavenger in neuroprotection, an essential cofactor for biosynthesis of neuropeptides, and an effective neuromodulator of dopamine- and glutamate-mediated neurotransmission [1]. As fresh vegetables and fruits are the most important suppliers of AA in normal diets, the determination of AA can offer a marker for food analysis and quality evaluation. There are several reported methods for the determination of AA in foodstuffs, including chromatography [2], spectrophotometry [3], and titration [4], etc.. However, these methods usually require complex steps, which may increase the chance of AA oxidation during the pretreatment of fresh samples. On the other hand, some methods may lack good specificity and require high-cost equipments. Thus, the development D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 234–242, 2011. © IFIP International Federation for Information Processing 2011

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of rapid, facile, and reliable methods for AA determination is particularly important in food industry. Recently, electrochemical methods have shown unique advantages in rapid determination of AA [5]. AA can be electrochemically oxidized through a two electron and one-proton pathway followed by an irreversible hydrolysis process to produce an electroinactive product at neutral pH [6]. Such an electrochemical property of AA can be used to realize direct electrochemical determination of AA with simple steps. But the high over-potential of AA oxidation at the conventional electrodes leads to poor specificity in real samples which also contain other electroactive substances, such as thiols and phenols [7]. Focused on the solution of this problem, most research used chemically modified electrodes [8] or the additional use of ascorbate oxidase (AAox) to differentiate the current response for AA from the total response obtained [9]. O’Connell et al. proposed a sensor modified with electropolymerized aniline (PANI) to selectively catalyze the oxidation of AA at low potential (+100 mV) so as to minimize the effects of most common interfering agents in juices and pharmaceuticals [10]. A carbon paste electrode modified with SiO2/SnO2/phosphate/Meldola’s blue was used to study the electrocatalytic oxidation of AA in commercial fruit juices [11]. Civit et al. successfully demonstrated amperometric determination of AA in several kinds of fruits and vegetables using a disposable screen-printed electrode modified with electrografted o-aminophenol film [12]. With the appearance of carbon nanotubes (CNTs), recent electrochemical studies have revealed that CNTs can facilitate the oxidation of AA at a low potential, and demonstrated possible application of CNTs for selective determination of AA [13]. Crevillén et al. developed carbon nanotube-modofied disposable detectors in microchip capillary electrophoresis for AA determination in pharmaceutical quality control [14]. However, Most of the above mentioned mehods showed a certain degree of complexity in the phase of electrode modification. This study exploits a new electrochemical sensor for determination of AA in fresh vegetables and fruits with an electrochemical pretreated disposable screen printed carbon electrode. The integrated screen printed carbon electrode, which consists of a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode, is amenable to rapid fabrication and mass production. In addition, this method demonstrated here just requires simple activation of electrodes to enhance the selectivity of AA determination without artificial modification, and could find some interesting applications in food and pharmaceutical industry.

2 Experimental Methods 2.1 Reagents and Solutions All chemicals were of analytical grade or higher and used as received. Aqueous solutions were prepared with MilliQ water (Millipore, Inc., 18 MΩ cm-1). AA and AAox (Cucurbita species, EC 1.10.3.3) were all purchased from Sigma and used as supplied. A stock solution of AA (1.0 mM) was prepared in 0.1 M phosphate buffer at pH 7.0 just before use. Oxalic acid and other reagents were all purchased from Beijing Chemical Co., Ltd. (Beijing, China). AAox was used to prove the absence of collateral reactions by means of specificity assays. The enzyme (250 U) was dissolved in

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1.0 mL 0.1 M phosphate buffer (pH 7.0), and divided into portions and stored at -40 °C until use. One portion was taken out and diluted with 0.1 M phosphate buffer (pH 7.0) to get a fresh AAox solution with the concentration of 25.0 U/mL for use. 2.2 Electrode Preparation The planar screen printed carbon electrodes (SPCEs) are designed by our lab and fabricated by Beijing Meiborui Co., Ltd. (Beijing, China) as previously described.15 The electrode 4.0cm×1.2cm×0.05cm (length×width×height) consists of three main parts which are a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode. The carbon working surface is 4 mm in diameter. The Ag/AgCl reference electrode was formed by applying 0.3 M FeCl3 solution on the silver electrode for 10 min [15]. The fabricated SPCEs were first sequentially sonicated in ethanol, 3.0 M HNO3, 1.0 M KOH, and distilled water each for 3 min., and then typical electrochemical pretreatment of SPCEs was carried out by applying +2.5 V for 60 s in 1.0 M NaOH (vs. Ag/AgCl reference electrode on the same SPCE). 2.3 Sample Preparation Fresh orange and cabbage samples were obtained from a local supermarket. The operative processes were the usual ones: green cabbage leaves were washed in running water, drained, and finally weighed (about 50 g/sample). Oranges were peeled and cut into suitable, representative portions of the proper weights (about 50 g/sample). Each sample was prepared by weighing a sufficient amount of vegetable or fruit to provide ca. 10 mg of AA/sample. 125 mL 0.1% (w/w) oxalic acid was immediately added to each sample, and the sample was homogenized with a Braun knife homogenizer for a few seconds. The homogenate was immediately submitted to 5 000 rpm in a refrigerated centrifuge for 5 min. An aliquot of the clear supernatant solution was filtered through a 0.45 μm pore-size filter (Millipore, Bedford, MA) before being injected into the system of high-performance liquid chromatography (HPLC). 2.4 Electrochemical Measurements Electrochemical measurements were carried out on a computer-controlled electrochemical analyzer (CHI660C, CHI, TX), and were performed by placing a drop of the corresponding solution on the working area. A 0.10 M phosphate buffer solution (pH 7.0) was used as supporting electrolyte for the electrochemical studies. For real sample analysis, 1.0 mL of the clear supernatant solution containing AA after centrifugation was first mixed with 1.0 mL of the working buffer solution (0.1 M phosphate buffer at pH 7.0), and then a 50 μL drop of the corresponding solution was placed on the working area before measurement. 2.5 HPLC Analysis Determination of AA by HPLC was performed in a Waters 2690 HPLC system (Milford, Massachusetts), with a column (5 μm; C18; 150 × 3.9 mm). Detection was

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done by ultraviolet (UV) absorption at 254 nm (Waters 2487 Tunable Absorbance Detector) [16]. At a flow rate of 0.6 mL/min (Waters 2690 Pump), the retention time for AA was 2.75 min. Identification of the AA peak was carried out using a highly purified standard solution prepared as 20 ppm in 0.1% (w/w) oxalic acid. The HPLC mobile phase was a 97%:3% V/V mixture of oxalic acid (0.1% (w/w), adjusted to pH 2.5 before mixing) and methanol, which was filtered through a 0.45 μm pore-size filter (Millipore, Bedford, MA). 2.6 Specificity Assays Aliquots of 5.0 μL of the enzyme solution (25.0 U/mL) were added into the 1.0 mL mixture, which was composed of clear supernatant solution obtained in the sample preparation and 0.1 M phosphate buffer at pH 7.0 (v/v 1:1), in order to achieve utter oxidation of the AA present in a period not longer than 15 min.

3 Results and Discussion 3.1 AA Oxidation at the Fresh and Pretreated SPCEs As shown in Fig. 1, at a fresh SPCE without activation, the oxidation of AA began irreversibly at 0 V, showing poor electrochemical kinetics with high over-potential and low electron transfer rate. McCreery et al. have systematically studied electrochemical reactions at carbon electrodes and demonstrated that the oxidation of AA is an inner-sphere reaction with electron-transfer kinetics sensitive to the electrode surface [17]. Thus the electrode activity shows a dramatic effect on electrochemical oxidation property of AA, which is generally determined by one or more of the following factors: (1) surface microstructure, (2) surface cleanliness, (3) hydrophilicity/hydrophobicity, (4) electronic structure, and (5) surface functional groups. It is well known that carbon electrodes are often necessarily preconditioned by electrochemical pretreatment to enhance the electron transfer properties at the electrode surface [18]. No matter electrochemical pretreatment are carried out in an acid, base or neutral solution, the main purpose of pretreatment on SPCEs is to remove organic ink constitutes or contaminates and to increase surface roughness or the density of oxygenated groups [19]. To find appropriate electrochemical pretreatment for optimum electrochemical performances of SPCEs designed by our group, different electrolytes and conditions were studied using potassium ferricyanide as an electrochemical probe. By gradually changing the concentration of NaOH, applied potential as well as pretreatment time, an electrochemical activation procedure, applying +2.5 V for 60 s in 1.0 M NaOH aqueous solution, was finally selected as the most reproducible and advantageous. As shown in Fig. 2(A), the response of a fresh SPCE without electrochemical activation was poor with a large peak separation of oxidation and reduction ( Ep 1000 mV), indicating a slow electron transfer rate and a irreversible electrochemical process. As to the electrode after pretreated in 1.0 M NaOH at +2.5 V for 60 s, Ep was reduced to ca. 160 mV, showing a rapid electron transfer rate and a quasi-reversible electrochemical response. The result also showed that the current response of Fe(CN)63−/4− at pretreated SPCE

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I / μA

obviously increased, compared with the fresh SPCE, due to the improvement of electron transfer rate and electrode surface roughness caused by the electrochemical activation procedure. Unlike the fresh SPCE just after rinse in different solutions, AA oxidation started at -0.4 V after electrode pretreatment shown in Fig. 2(B), which was obviously more negative compared with that at the fresh electrode. This result clearly indicated that the effective electrochemical activation leaded to a faster electron-transfer kinetics for the AA oxidation, which benefited for selective determination of AA at lower potential on the pretreated electrode. The comparison of the voltammograms obtained for AA oxidation at the SPCEs before and after pretreatment also showed the enhancement of current response of AA and background charging current, due to the increase of electrode surface caused by electrochemical pretreatment.

E / V vs. Ag/AgCl Fig. 1. CVs obtained at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed line) and presence (solid line) of 0.50 mM AA before electrochemical pretreatment. Scan rate, 100 mV s-1.

In addition, the electrochemical activated SPCEs showed a good linearity for AA measurement, as depicted in Fig. 3. The currents recorded clearly increased with increasing AA concentration in solution (Fig. 3(A)), and were linear with AA concentration within a range from 0.01 to 1.00 mM (I/μA= 28.02C/mM - 0.39, γ=0.9990) (Fig. 3(B)). The detection limit, based on a signal-to-noise ratio of 3, was calculated to be 5.0 μM. The activated electrode was also stable for repetitive use, and the sensitivity remained essentially identical after the daily measurements of AA standard (0.10 mM) for 4-6 h on at least consecutive 10 days. On the other hand, the electrode showed good reproducibility for the determination of AA, which was illustrated by the small relative standard deviation (RSD) obtained with the same electrode used for the measurements of AA for several times. For example, the RSD recorded for the AA standard (0.10 mM) was calculated to be 2.3% (n=10). The good performance of activated SPCEs mentioned above is indeed advantageous to be applied in determination of AA in real samples.

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80

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Fig. 2. (A) Typical cyclic voltammograms (CVs) at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) containing 5 mM K3Fe(CN)6 before (dashed line) and after (solid line) electrochemical pretreatment. Scan rate, 100 mV s-1. (B) CVs obtained at a fresh SPCE in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed line) and presence (solid line) of 0.50 mM AA after electrochemical pretreatment. Scan rate, 100 mV s-1.

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Fig. 3. (A) Typical CVs obtained at activated SPCE in 0.1 M phosphate buffer (pH 7.0) containing AA with different concentrations of 0.00, 0.01, 0.02, 0.05, 0.10, 0.20, 0.40, 0.60, 0.80 and 1.00 mM. Scan rate, 100 mV s-1. (B) Plot of current response vs the concentration of AA.

3.2 Analysis of AA in Cabbage and Orange As detailedly described in experimental section, the AA content in cabbage and orange samples was analyzed by use of electrochemically pretreated SPCEs. As displayed in Fig. 4, the current response obtained for the addition of cabbage (Fig. 4(A)) and orange sample (Fig. 4(B)) showed well-defined shape of voltammograms, which

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were remarkably the same as the one obtained in AA standard solution before the real sample analysis. According to the oxidation current of real sample, AA content can be calculated in terms of the plot of sensor calibration in Fig. 3(B).

A I / μA

I / μA

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E / V vs. Ag/AgCl

E / V vs. Ag/AgCl

Fig. 4. CVs obtained at activated SPCEs in 0.1 M phosphate buffer (pH 7.0) in the absence (dashed lines) and presence (solid lines) of diluted cabbage sample (A) and orange sample (B). The dotted lines in (A) and (B) refer to the responses of the mixture of AAox and each diluted sample after 15min, respectively. Scan rate, 100 mV s-1.

Considering the importance of the specificity of the AA determination in the real sample analysis demonstrated in this study, we further confirmed such a critical factor after the determination of AA content in both cabbage and orange by using AAOx to specifically catalyze the oxidation of AA [20]. The dotted lines in Fig. 4 showed the current response of a mixture of cabbage or orange with AAOx. As shown in Fig. 4(A) and Fig. 4(B), the presence of AAOx into the sample resulted in a clear decrease in the current response almost to the baseline level, confirming that it was feasible to selectively determine AA virtually interference-free from other electroactive species coexisting in the cabbage and orange. In this study, the results obtained via an electrochemical voltammetric method have also been compared with those obtained using classical HPLC-UV analysis, in order Table 1. Method comparison sample 1 2 3 4 mean

cabbage (mg/kg) voltammetry HPLC 96.3 97.6 98.9 100 99.7 97.8 101 98.9 99.0±2.0 98.6±1.1

orange (mg/kg) voltammetry HPLC 420 431 435 425 428 435 439 427 431±8 430±4

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to validate its reliability for rapid determination of AA in fresh vegetables and fruits. As displayed in Table 1, the comparison of the results obtained by the proposed electrochemical method to those of the reference method demonstrated an excellent correlation. These demonstrations suggest that the analytical system described in this study could be useful for rapid, facile, and reliable determination of AA in food analysis and other fields with low cost.

4 Conclusions By taking advantage of facile electrochemical pretreatment and disposable screenprinted carbon electrodes, we have successfully developed a new electrochemical method for voltammetric measurement of AA with electrochemically activated electrodes. The integrated disposable screen printed carbon electrode, which consists of a carbon counter electrode, a silver pseudo-reference electrode and a carbon working electrode, is amenable to rapid fabrication and mass production, which simplifies the determination equipment and reduces the cost. In addition, this method demonstrated here just requires simple activation of electrodes to enhance the selectivity of AA determination, which avoids any artificial modification and reduces the determination time. Specificity assay and method comparison have demonstrated the electrodes possess a high selectivity and could thus be used for selective and reliable measurement of AA at lower applied potential. The method has also found to be sensitive, reproducible, and thus essentially finds some interesting applications in rapid food analysis and other fields. Acknowledgments. This research was financially supported by the National High Technology Research and Development Program (2010AA10Z403, 2007AA10Z202), and Beijing Municipal Science and Technology Commission Program (Z09090501040901).

References 1. Rice, M.E.: Ascorbate Regulation and Its Neuroprotective Role in the Brain. Trends Neurosci. 23, 209–216 (2000) 2. Han, J.-S., Kozukue, N., Young, K.-S., Lee, K.-R., Friedman, M.: Distribution of Ascorbic Acid in Potato Tubers and in Homeprocessed and Commercial Potato Foods. J. Agric. Food Chem. 52, 6516–6521 (2004) 3. Dürüst, N., Sümengen, D., Dürüst, Y.: Ascorbic Acid and Element Contents of Food of Trabzon (Turkey). J. Agric. Food Chem. 45, 2085–2087 (1997) 4. Verdini, R.A., Lagier, C.M.: Voltammetric Iodometric Titration of Ascorbic Acid with Dead-Stop End Point Detection in Fresh Vegetables and Fruit Samples. J. Agric. Food Chem. 48, 2812–2817 (2000) 5. Terry, L.A., White, S.F., Tigwell, L.J.: The Application of Biosensors to Fresh Produce and the Wider Food Industry. J. Agric. Food Chem. 53, 1309–1316 (2005) 6. Zhang, M., Liu, K., Gong, K., Su, L., Chen, Y., Mao, L.: Continuous On-Line Monitoring of Extracellular Ascorbate Depletion in the Rat Striatum Induced by Global Ischemia with Carbon Nanotube-Modified Glassy Carbon Electrode Integrated into a Thin-Layer Radial Flow Cell. Anal. Chem. 77, 6234–6242 (2005)

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7. Malinauskas, A., Garjonyte, R., Mažeikien, R., Jureviciūte, I.: Electrochemical Response of Ascorbic Acid at Conducting and Electrogenerated Polymer Modified Electrodes for Electroanalytical Applications: a Review. Talanta 64, 121–129 (2004) 8. Nassef, H.M., Civit, L., Fragoso, A., O’Sullivan, C.K.: Amperometric Sensing of Ascorbic Acid Using a Disposable Sscreen-Printed Electrode Modified with Electrografted o-Aminophenol Film. Analyst 133, 1736–1741 (2008) 9. Mello, L.D., Kubota, L.T.: Biosensors as a Tool for the Antioxidant Status Evaluation. Talanta 72, 335–348 (2007) 10. O’Connell, P.J., Gormally, C., Pravda, M., Guilbault, G.G.: Development of an amperometric ascorbic acid (Vitamin C) sensor based on electropolymerised aniline for pharmaceutical and food analysis. Anal. Chim. Acta 431, 239–247 (2001) 11. Castilho, R.F., Souza, E.B.R., Alfaya, R.V.S., Alfaya, A.A.S.: Meldola’s Blue Immobilized on a SiO2/SnO2/Phosphate Xerogel, a New Sensor for Determination of Ascorbic Acid in Medicine and Commercial Fruit Juice. Electroanalysis 20, 157–162 (2008) 12. Nassef, H.M., Civit, L., Fragoso, A., O’Sullivan, C.K.: Amperometric Determination of Ascorbic Acid in Real Samples Using a Disposable Screen-Printed Electrode Modified with Electrografted o-Aminophenol Film. J. Agric. Food Chem. 56, 10452–10455 (2008) 13. Zhang, M., Liu, K., Xiang, L., Lin, Y., Su, L., Mao, L.: Carbon Nanotube-Modified Carbon Fiber Microelectrodes for In Vivo Voltammetric Measurement of Ascorbic Acid in Rat Brain. Anal. Chem. 79, 6559–6565 (2007) 14. Crevillénl, A.G., Pumera, M., Gonzálezl, M.C., Escarpa, A.: Carbon Nanotube Disposable Detectors in Microchip Capillary Electrophoresis for Water-Soluble Vitamin Determination: Analytical Possibilities in Pharmaceutical Quality Control. Electrophoresis 29, 2997– 3004 (2008) 15. Cui, G., Yoo, J.H., Lee, J.S., Yoo, J., Uhm, J.H., Nam, H.: Effect of Pre-Treatment on the Surface and Electrochemical Properties of Screen-Printed Carbon Paste Electrodes. Analyst 126, 1399–1403 (2001) 16. Robards, K., Antolovich, M.: Methods for Assessing the Authenticity of Orange Juice: a Review. Analyst 120, 1–28 (1995) 17. McCreery, R.L.: Electroananlytical Chemistry. Dekker, New York (1991) 18. Guo, T., McCreery, R.L.: Surface Chemistry and Electron-Transfer Kinetics of HydrogenModified Glassy Carbon Electrodes. Anal. Chem. 71, 1553–1560 (1999) 19. Prasad, K.S., Muthuraman, G., Zen, J.-M.: The Role of Oxygen Functionalities and Edge Plane Sites on Screen-Printed Carbon Electrodes for Simultaneous Determination of Dopamine, Uric Acid and Ascorbic Acid. Electrochem. Commun. 10, 559–563 (2008) 20. Akyilmaz, E., Dinçkaya, E.: A New Enzyme Electrode Based on Ascorbate Oxidase Immobilized in Gelatin for Specific Determination of L-Ascorbic Acid. Talanta 50, 87–93 (1999)

Regional Drought Monitoring and Analyzing Using MODIS Data — A Case Study in Yunnan Province Guoyin Cai, Mingyi Du, and Yang Liu School of Geomatics and Urban Information, Beijing University of Civil Engineering and Architecture, No. 1, Zhanlanguan Road, Xicheng District, Beijing 100044, P.R. China [email protected]

Abstract. Yunnan suffered from a severe drought from Winter, 2009 to Spring, 2010. Compared with traditional methods for drought monitoring, remote sensing (RS) based methods has the merits of almost real-time and large covering area. In this paper, vegetation water supply index (VWSI) was used to detect the drought condition utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) data in Yunnan province on 7 March, 2010. Merits and pitfalls of this method were analyzed according to the comparison between the RS detected drought and the real drought status developed by the National Meteorological Bureau. Our results showed that VWSI was apt to agriculture fields with dense vegetation covered areas. This means that this method can be used to detect the drought in a regional area. Keywords: Drought, VWSI index, MODIS, Yunnan Province.

1 Introduction Drought refers to the abnormal water shortage resulted from the imbalance of water budget. When it changes to threaten human’s daily life and living conditions, drought becomes drought disaster. An extremely drought was occurred in Yunnan Province from Winter 2009 to Spring 2010. This is the biggest drought occurred in Yunnan province in a century, causing an extremely shortage of water for agriculture, forestry, human beings and living stocks, and a huge loss in local and national economy. So drought monitoring is very important for the local government to make some measurements to reduce losses and water supplying. Classic drought monitoring approaches were based on the point-based data predominantly using meteorological observations and regional drought records, such as the Palmer drought severity index (PDSI) (Palmer 1965)[1], the crop moisture index (Palmer 1968)[2], the surface water supply index (Shafer and Dezman 1982)[3] and the standardized precipitation index (SPI) (McKee et al. 1993, 1995)[4,5]. However, because the meteorological observations are scarcely distributed and most of the underlying surfaces are not homogeneous, so the water conditions obtained from meteorological observations are not representative to the regional conditions (Cai et al. 2007)[6]. Because of the characteristics of almost real time and large covering of areas, remote sensing technology has become the most promising tool for large scale monitoring of drought (Liu and D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 243–251, 2011. © IFIP International Federation for Information Processing 2011

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Kogan, 1996)[7], furthermore, remote sensing data are continuous datasets consistently available and can be used to detect the onset of drought, its duration and magnitude (Thiruvengadachari and Gopalkrishna, 1993)[8]. Generally speaking, there are three different ways for drought monitoring using remote sensing. 1), detecting soil moisture using remote sensing, and then the drought condition is derived using relationship between soil moisture and drought; 2) Since vegetation developing status was found to be sensitive to water stress, vegetation index is regarded as the indicator of the drought condition, such as Normalized Difference Vegetation Index (NDVI)[9], Atmospherically Resistant Vegetation Index (ARVI)[10], Soil Adjusted Vegetation Index (SAVI)[11] and Enhanced Vegetation Index (EVI)[12]. If vegetation index is calculated from satellite images, drought condition is acquired based on the empirical relationship between vegetation index and drought; 3) like vegetation index, land surface temperature (LST) is also sensitive to drought phenomenon. LST is a good indicator of the energy balance at the earth’s surface processes on regional and global scales (Wan et al. 2004)[13]. The main drawback using LST to detect drought is that it is quite difficult to normalize the variation of daily meteorological conditions such as net radiation, air temperature, wind speed, humidity, which affect daytime thermal measurements (McVicar and Jupp 1998[14]). So the vegetation index and LST are combined to evaluate drought status in most cases. Because the soil moisture derived from satellite images is not too accuracy to detect the regional drought, plus, the different indexes are easy to obtain with a higher spatial and temporal resolution, so the NDVI has been used extensively for drought monitoring in recent years (Ji and Peters 2003[15], Vicente-Serrano 2006[16]); Chen et al. (1994)[17] developed the anomaly vegetation index (AVI) to study annual vegetation dynamics. Xiao et al. (1995)[18] developed the Water Supplying Vegetation Index (WSVI), which detects drought by combing vegetation information with temperature retrieved from National Oceanic and Atmospheric Administration (NOAA) satellite data. Qin et. Al. (2008)[19] developed a new index called perpendicular drought index (PDI), which is defined as a line segment that is parallel with the soil line and perpendicular to the normal line of line line intersecting the coordinate origin in the two-dimensional scatter plot of red against near infrared (NIR) wavelength reflectance, to evaluate the drought status of northwestern China using TerraModerate Resolution Imaging Spectroradiometer (MODIS) data. Yet the accuracy of drought monitoring by using the NDVI based drought index may be affected by the change of vegetation type and difference in ecosystems. In addition, the NDVI based drought index may be serve as an after-effect indicator of drought since there are time lags between drought occurrence resulting either from soil moisture deficiency or abnormal reduction of railfull for some consecutive time periods and change of the NDVI. The combination of NDVI and LST has proved better understanding of drought events with their close inter-relations with surface drought status. The ratio of NDVI and LST, also called the temperature-vegetation index (TVX) (Prihodko and Goward, 1997[20]), has been proven to be significantly correlated with crop moisture (Nemani et al, 1993[21]) and soil moisture in most climatic and land cover conditions (Carlson et al., 1994[22]). Results from McVicar and Biencirth (2001) [23] indicated that the composite AVHRR LST/NDVI was a rapid and effective indicator for drought assessment at country or province level. Beside the LST/NDVI approach, the LST

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versus NDVI scatter plot reveals more meaningful information of drought. Based on the “Triangle” space, the vegetation temperature condition index (VTCI) (Wang et al. 2001[24]) and temperature-vegetation dryness index (TVDI) (Sandholt et al. 2002[25]) were developed for assessing soil moisture status. Because NDVI was easily saturated when there were dense vegetation, Han et al. (2006)[26] discussed the relationship between LST and NDVI in the “triangle” space and replaced NDVI with LAI to further interpret the drought condition when NDVI was saturated. In this paper, VWSI, which is the combination of LST and NDVI, is used to detect the drought in Yunnan province. The aim of our study was to evaluate the drought in Yunnan province using MODIS data and validate the effectiveness of VWSI for drought monitoring in dense vegetation covered area. In what follows, the study area and data source are given, as are the methodology employed in the research. The results and analysis are then presented. The paper closes with preliminary conclusion and perspectives.

2 Experiment and Methods 2.1 Study Area Yunnan is a southwestern province in China. It is a Plateau in low latitude with complex landscape. It is controlled by the dry continental monsoon in winter, and the moist ocean winds in summer. There are 7 climate types in Yunnan province, they are northern tropical climate zone, northern subtropical climate zone, middle subtropical climate zone, middle temperate climate zone, southern subtropical climate zone, southern temperate climate zone, and plateau climate zone. The annual precipitation in most parts of Yunnan province is 1100mm or so, but distribution of precipitation is uneven in seasons and regions because of influences of different atmospheric circulation in winter and summer. Almost 89-90% of rainfall concentrated in May to October, especially in June to August in which the precipitation almost accounts to 60% of the total annual precipitation. The dry season is from December to next years’ April when the precipitation occupies 10-20% even less of the total annual precipitation. This results in the occurrence of spring drought, causing more frequency occurrence of forest fire and influencing the normal growth of crops. 2.2 Data and Processing MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. MODIS has a viewing swath width of 2,330 km and views the entire surface of the Earth every one to two days. Its detectors measure 36 spectral bands between 0.405 and 14.385 µm, and it acquires data at three spatial resolutions -- 250m, 500m, and 1,000m. These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

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MODIS Terra level 1B data on 4 March 2010 was downloaded from official MODIS website to detect the drought status in Yunnan province. Before using MODIS data to calculate some parameters, several pre-processing steps are needed as: (1) geometric calibration: MODIS 1B datasets provide us with the latitude and longitude dataset, which can be used to correct the MODIS 1B data. First, the latitude and longitude should be resized to the same size as reflective dataset by bilinear algorithm, then the resized latitude and longitude are used to build a geographic lookup table (GLT) file which contains map locations for every pixel of the image it is associated with, finally, the image is geo-referenced using the established GLT; (2) image cropping: because our study area is only part of the georeferenced one swath of MODIS data, so the image cropping should be applied. First, the vector data of Yunnan province is overlaid to the image, then the region of interest (ROI) covering Yunnan province is selected, and finally the selected ROI is cropped; (3) cloud screen :to remove the cloud influences, cloud screening should be performed in the study area. The near infrared reflection method is used in this paper (Leprieur et al., 1996)[27]; (4) water body removing: because we focus on the land surface, so we removed the water body before calculating NDVI and LST to monitor the drought. Because there is a big difference between water body and land in band 5 of MODIS data, so the threshold method is used in our research to remove the water body. 2.3 Methodology VWSI is defined as: VWSI =

NDVI LST

(1)

When crops suffer from drought, their leaf apertures partly close to reduce the loss of water, causing the temperature of the leaf surface to increase. The more severe the drought is, the higher the temperature of the leaf surface. At the same time, the growth of crops is affected by drought, resulting in a reduction of the leaf area index (LAI). Leaves will also wither under a high air temperature. All of these factors may result in a reduction of the NDVI. The result is an index, the WSVI, showing the influence of drought on agriculture, and maps of summer drought over large areas. Using this method, vegetation growth can be closely monitored and the regional effects of summer drought can be closely monitored. NDVI is a simple numerical indicator that can be used to analyze remote sensing measurements, typically but not necessarily from a space platform, and assess whether the target being observed contains live green vegetation or not. The NDVI is calculated as a ratio between measured reflectivity in the red and near infrared portions of the electromagnetic spectrum. These two spectral bands are chosen because they are most affected by the absorption of chlorophyll in leafy green vegetation and by the density of green vegetation on the surface. Also, in red and near-infrared bands, the contrast between vegetation and soil is at a maximum. The NDVI transformation is computed as the ratio of the measured intensities in the red (R) and near infrared (NIR) spectral bands using the following formula: NDVI =

NIR − red NIR + red

(2)

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The resulting index value is sensitive to the presence of vegetation on the Earth's land surface and can be used to address issues of vegetation type, amount, and condition. Many satellites have sensors that measure the red and near-infrared spectral bands, and many variations on the NDVI exist. MODIS is a 36 channel radiometer with channels in the red (channel 1) and near infrared (channel 2) potion of the spectrum. The MODIS NDVI is created using data from these channels in the following manner: NDVI =

channel2 − channel1 channel2 + channel1

(3)

LST is derived using the local split-window algorithm, a common and popular way to calculate the LST. The philosophy of our iterative self-consistent approach for land surface temperature determination is based on the fact that the actual land surface temperatures are the same for all thermal bands (Xue et al., 2005)[28]. Based on the local split-window method (Becker and Li, 1990[29]), for three thermal bands of MODIS data, we have Ts ,1 = A0 + P1

T1 + T2 T − T2 + M1 1 2 2

(4)

T1 + T3 T − T3 + M2 1 2 2

(5)

and Ts , 2 = A0 + P2

where T1,T2 and T3 are the brightness temperature for 1st, 2nd, and 3rd thermal infrared band respectively. A0 is a constant, P1, P2, M1 and M2 can be expressed as functions of emissivities (ε1,ε2,ε3) in these four thermal infrared bands. Theoretically, Ts,1 is equal to Ts,2. Because of the effects of emissivity and atmosphere, the temperatures derived from equations 4 and 5 are different in practice. MODIS has 5 thermal bands, 29,30,31,32, and 33. Because band 30 has too much ozone absorption to use, band 33 located in the edge of thermal bands, so bands 29,31,32 are used to constitute two groups to determine the LST. In order to calculate emissivities and LSTs, the study area was classified as several land cover types using the above mentioned method. Each land cover type was regarded as one window. To enable comparisons to be made for the two sets of satellite data, all pixels (Pm) in the window were chosen from each set of satellite data. The detailed description of this method is in Xue et al. (2005).

3 Results After NDVI and LST are calculated, VWSI is computed using band math in software ENVI. Based on the drought levels, that is, extreme drought, severe drought, moderate drought, slight drought and normal, are obtained by means of density slice. Figure 1 is the NDVI which generally shows the growth of vegetation in Yunnan province. Figure 1 indicates that the vegetation growth developed not well in most parts of our study areas in color yellow and cyan, except small parts in the southern and western parts in color purple. Areas in white are covered by cloud. This means that the vegetation growth in Yunnan Province is not good at this time when it is developed well in normal years.

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Fig. 1. NDVI derived from MODIS data in the Yunnan Province on 7 March, 2010

Fig. 2. LST derived from MODIS data in the Yunnan Province on 7 March, 2010

Fig. 3. LST derived from MODIS data in the Yunnan Province on 7 March, 2010

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Figure 2 is the LST in the study area. The temperatures in our study area are from 290K-310K. Generally speaking, temperatures in the northern and western regions are lower than that in eastern regions. This indicates that, compared with western areas, there are severe drought in the eastern regions. Figure 3 is the VWSI from which we can see that most of the areas are in drought, especially in the eastern part of Yunnan Province. This is in a consistency with the real drought status monitored and issued by the National Meteorological Bureau.

4 Conclusions and Perspectives Our research shows that VWSI index can be used to obtain the drought information in Yuannan province. This indicates that VWSI can be adopted to monitor drought in a regional scale from satellite images. There are some issues should be taken into consideration when using VWSI. First, it is difficult to collect the satellite images in a cloud-free day in a regional scale, even using MODIS data with 1km spatial resolution; Second, the underlying surfaces should be covered mostly by vegetation when using VWSI index, otherwise, it should be replaced by other indexes such as soil moisture or thermal inertia; third, VWSI is a relative value with no real meaning, the level of drought based on VWSI should be determined by empirical, this influences the accuracy of the monitoring results. According to those issues, our further research should focus on using some satellite images in microwave bands without influencing of cloud. Furthermore, a drought monitoring system should be developed to detect the drought status and give some prediction for the future possible drought. This might be helpful for the local government and farmers to make some measurements to decreasing losses. Acknowledgments. The Publication is supported by Beijing Natural Science Foundation under Grant No.4102018, and by Beijing Municipal Organizing Department under Grant No. 070800905, and is also supported partly by Jurisdiction of Beijing Municipality under Grant No. PHR200906138, PHR200907127 and PHR 20070101.

References 1. Palmer, W.C.: Meteorological Drought. Research Paper No.45, US Department of Commerce Weather Bureau, Wshington, DC (1965) 2. Palme, W.C.: Keeping track of crop moisture conditions, nationwide: the Crop Moisture Index. Weatherwise 21, 156–161 (1968) 3. Shafer, B.A., Ezman, L.E.: Development of a surface water supply index (SWSI) to access the severity of drought conditions in snowpack runoff areas. In: Proceedings of the 50th Annual Western Snow Conference, Colorado State University, pp. 164–175 (1982) 4. McKee, T.B., Doeskin, N.J., Kleist, J.: The relationship of drought frequency and duration to time scales. In: Proceedings of 8th Conference on Applied Climatology, Boston, pp. 179–184 (1993) 5. McKee, T.B., Doeskin, N.J., Kleist, J.: Drought monitoring with multiple time scales. In: Proceeding of 9th Conference on Applied Climatology, Boston, pp. 233–236 (1995)

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6. Cai, G.Y., Xue, Y., Hu, Y.C.: Soil Moisture Retrieval from MODIS data in Northern China Plain using thermal inertia Model. International Journal of Remote Sensing 16, 3567–3581 (2007) 7. Liu, W., Kogen, F.N.: Monitoring regional drought using the vegetation condition index. International Journal of Remote Sensing 17, 2761–2782 (1996) 8. Thiruvengadachari, S., Gopalkrishna, H.R.: An integrated PC environment for assessment of drought. International Journal of Remote Sensing 14, 3201–3208 (1993) 9. Rouse, J.W., Haas, R.H., Schell, J.A.: Monitoring the Vernal Advancements and Retrogradation, Texas, Texas A & M University (1974) 10. Kaufman, Y.J., Tanre, D.: Atmospherically Resistant Vegetation Index (ARVI) for EOSMODIS. IEEE Transaction on Geoscience Remote Sensing 30, 261–270 (1992) 11. Huete, A.R.: A Soil Adjusted Vegetation Index (SAVI). Remote Sensing of Environment 25, 295–309 (1988) 12. Liu, H.Q., Huete, A.R.: A feedback based Modification of the NDVI to Minimize Canopy Background and Atmospheric Noise. IEEE Transaction on Geoscience and Remote Sensing 33, 457–465 (1995) 13. Wan, Z., Wang, P., Li, X.: Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern great plains, USA. International Journal of Remote Sensing 25, 61–72 (2004) 14. Mcvicar, T.R., Jupp, D.L.B.: The current and potential operational uses of remote sensing to aid decisions on drought exceptional circumstances in Australia: a review. Agriculture System 3, 399–468 (1998) 15. Song, X., Saito, G., Kodama, M.: Early detection system of drought in East Asia using NDVI from NOAA AVHRR data. International Journal of Remote Sensing 25, 3105–3111 (2004) 16. Vicente-Serrano, S., Cuadrat-prats, J.M., Romo, A.: Early prediction of crop productivity using drought indices at different time scales and remote sensing data: application in the Ebro valley (north east Spain). International Journal of Remote Sensing 27, 511–518 (2006) 17. Chen, W., Xiao, Q., Sheng, Y.: Application of the anomaly vegetation index to monitoring heavy drought in 1992. Remote Sensing of Environment 9, 106–112 (1994) (in Chinese) 18. Kogan, F.N.: Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15, 91–100 (1995) 19. Qin, Q., Chulam, A., Zhu, L.: Evaluation of MODIS derived perpendicular drought index for estimation of surface dryness over northwestern China. International Journal of Remote Sensing 7, 1983–1995 (2008) 20. Prihodko, L., Goward, S.N.: Estimation of air temperature from remotely sensed surface observations. Remote Sensing of Environment 60, 335–346 (1997) 21. Nemani, R., Price, L.L., Running, S.W.: Developing satellite derived estimates of surface moisture status. Journal of Applied Meteorology 32, 548–557 (1993) 22. Carlson, T.N., Gillies, R.R., Perry, E.M.: A method to make use thermal infrared temperature and NDVI management to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews 9, 161–173 (1994) 23. McVicar, T.R., Biencirth, P.N.: Rapidly assessing the 1997 drought in Papua New Guinea using composite AVHRB imagery. International Journal of Remote Sensing 22, 2109– 2128 (2001) 24. Wang, P., Gong, J., Li, X.: Vegetation-Temperature Condition Index and its application for drought monitoring. Geomatics and Information Science of Wuhan University 26, 412– 418 (2001) (in Chinese)

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25. Sandholt, I., Rasmussen, K., Andersen, J.: A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79, 213–224 (2002) 26. Han, L., Wang, P., Yang, H.: Study on NDVI-Ts space by combining LAI and evapotranspiration. Science in China Series D 49, 747–754 (2006) 27. Leprieur, C., Kerr, Y.H., Richon, J.M.: Critical assessment of vegetation indices from AVHRR in a semiarid environment. International Journal of Remote Sensing 13, 2549– 2563 (1996) 28. Xue, Y., Cai, G.Y., Guan, Y.N.: Iterative self-consistent approach for earth surface temperature determination. International Journal of Remote Sensing 26, 185–192 (2005) 29. Becker, E., Li, Z.L.: Towards a local spilt window method over land surface. International Journal of Remote Sensing 11, 369–394 (1990)

Regression Analysis and Indoor Air Temperature Model of Greenhouse in Northern Dry and Cold Regions Ting Zhao and Heru Xue College of computer & information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, P. R. China [email protected]

Abstract. The indoor air temperature of greenhouse is an important parameter in environmental monitoring. Existing research on it has many, and the mathematical models have been made. But the obtained models do not apply to the northern areas features of dry and cold. On the basis of previous studies, the indoor air temperature model of greenhouse will be re-modeling by using the regression analysis method in this paper. Analysis each convection heat transfer coefficient of the model and re-established them. In the case of known the outdoor weather conditions, the solution of the indoor temperature will be implemented by using the computer programming. And the data of a period is selected randomly from the observational data to validate the model is feasible and applied. Keywords: Indoor air temperature model, Regression analysis method, Model parameters, Computer programming.

1 Introduction There are many advantages in China's light greenhouse, but the environmental control level of greenhouse is still lower. The artificial experience is still the main force, and theoretical and practical guidance is lacking. The indoor air temperature of greenhouse is an important parameter in environmental monitoring. Predecessors have done a lot of research on the prediction model of the indoor air temperature of greenhouse. But most studies have been done for the greenhouse in Beijing. [1, 2]And the obtained models do not apply to the northern areas features of dry and cold. The prediction model of the indoor air temperature of greenhouse which can be used in the northern areas features of dry and cold can predict the daily change of temperature with time. According to forecasts, the staff can regulate the temperature actively, so that the crops can be provided the best growth temperatures. The model to be build up on the basis of previous’ work, and the indoor air temperature model of greenhouse will be re-modeling by using the regression analysis method. Analysis each convection heat transfer coefficient of the model and re-established them. The typical areas and typical greenhouse will be chosen for the study. And using the known conditions to validate the model is feasible and applied. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 252–258, 2011. © IFIP International Federation for Information Processing 2011

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2 The Indoor Air Temperature Model of Greenhouse Table 1. Sign Description emblem

description

unit

emblem

description

Q

Heat exchange

W

a

V

Volume

m3

b

Indoor temperature Rear wall

A

Area

m2

c

Crop canopy

T

Temperature

K

h

Heating

Q1

Convection heat transfer energy Heat capacity

W

o

Kg/s

r

Outdoor Temperature Rear slope

ρ c pV

J/K

s

Soil

v

Ventilation

pf

Covers

air

air

2.1 Heat Balance Differential Equation of Indoor Air Temperature The heat balance differential equation used in this paper mainly by heat conduction differential equation. The model conditions have been simplified some. And according to the heat transfer principles and the law of conservation of mass and energy, the physical processes in the greenhouse have been considered comprehensively. The thermal change has much effect on thermal environment of greenhouse caused by heat change and moisture variation which come from solar radiation, heat convection, radiation heat transfer, heat exchange and natural ventilation. [3, 4]

U D F SDYD u

G7D GW

4 K 4E 4U 4V 4SI 4F 4Y

(1)

On the left of the equation is the heat change caused by the air temperature changes with time. And the right contains the energy of the indoor air temperature coming from hot-water heating-- 4 K ; the energy of the indoor air temperature wasted by natural ventilation and infiltration heat loss-- 4Y ; and the energy coming from natural-convection heat transfer-- 4 L . This is the derived formula of the indoor air temperature changes with time.

WD

WD 'WD

WD

4 K 4 E 4 U 4 V 4 SI 4F 4Y

U D F SD Y D

'W

(2)

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Known by the Newton's law of cooling, the convection heat transfer energy from gas A to solid B can be shown as this.

4$ %

$ $ %D $ ˄ % 7$ 7%

(3)

In this equation, $ $ % is the surface area of the heat transfer from A to B; D $ % is the coefficient of the heat transfer between A and B; 7$ and 7% is the temperature of A and B. The indoor air is heated mainly by the hot-water heating pipes. And the heat transfer between heating pipes and the indoor air is carried out by convection. The energy of the indoor air temperature coming from hot-water heating is 4 K . It can be shown as this.

4K

$K KK7S 7D˅

(4)

In this equation, is the surface area of the hot-water heating pipes; is the coefficient of the heat transfer between the heating pipes and the indoor air; is the temperature of the hot-water heating pipes; 7D is the indoor air temperature. The energy of the indoor air temperature wasted by natural ventilation and infiltration heat loss is 4Y . It can be shown as this.

4Y

UD FD )˄ Y 7D 7R˅

(5)

In this equation, )Y is the air quantity of the indoor air; UD F SD is the specific heat volume of the indoor air; 7D is the indoor air temperature; 7R is the outdoor air temperature. Through the equations of (2),(3),(4),and (5), the heat balance differential equation of indoor air temperature can be simplified as this.

7D 7 U D F SD Y D '7 $ K K˄ K 7D 7K˅ $ E D D ˄ E 7D 7E˅ $ U D D ˄ U 7D 7U ˅ $ V D D ˄ V 7D 7V ˅

(6)

$ SI D SI˄7D 7SI ˅ $ F D D ˄ F 7D 7F˅ U D F SD )˄ Y 7D 7R ˅ 2.2 Heat Convection Coefficient--

DL

Heat convection is the heat transfer which happened on the liquid flows through the solid surface. In the greenhouse which has the small natural ventilation, rising or declining the indoor air temperature is mainly through the heat convection with the cover, the ground soil, plants and other heat transfer to achieve. There are many essays about the heat convection in overseas. And the studies in this respect are also very mature. The arithmetic in these studies can be used in our country greenhouse. The heat convection coefficient can be shown as this. [5]

Regression Analysis and Indoor Air Temperature Model of Greenhouse

D

K˄7L 7M E

E

255

(7)

Based on such expression of the heat convection, each heat convection coefficient of the heat balance differential equations can be explained to . Therefore, the problem of optimizing the equation is the problem of calculating the value of h.

3 Known Conditions of This Model 3.1 Structural Parameters To facilitate the calculation, the greenhouse structure can be approximately regarded as the following graphic which does not affect the results on the premise.

The length of the greenhouse L5=50(m) Leaf area index of the indoor crops La=0.3 The area of the covers L1*L5 The area of the ground L4*L5 The area of the rear slope L2*L5 The area of the rear wall L3*L5 The area of the crop canopy L4*L5*La The volume of the indoor air V=506 The specific heat volume of the indoor air ρ ac pa =1185; kJ/(kg· ) The temperature of the heating pipes during the day T=60+273; (K) The area of the heating pipes is 9 m^2 The coefficient of the heat transfer between the heating pipes and the indoor air

℃

˄7オ≄ 7㇑޵≤˅ .

The air quantity of the indoor air Φ=0.001*V/3600

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3.2 Known Conditions (Temperature Indicators) 1)

2)

The 24-hour predicted values come from the weather forecast. They include the outdoor air temperature, the outdoor humidity, the wind velocity, the wind direction and the uitraviolet intensity. Each average of the data calculated by hour is obtained. And the data of the cover temperature, the soil temperature, the rear wall temperature, the rear slope temperature, the crop canopy temperature and indoor air temperature is detected by the greenhouse monitoring system.

4 Model Validation Experimental data is the observed data which can stand for the northern areas features of dry and cold. Use nonlinear regression analysis to optimize the parameters which comes from this heat balance differential equation of indoor air temperature. Nonlinear regression analysis is using least square method to estimate the parameters of non-linear model. Formula six is used to the regression equation of non-linear model. The starting value of the parameter h chose the empirical value 1.25.The sample data chose the data from November and December of 2009 and January and February of 2010. Other required temperature used the observed data. Each heat transfer coefficient of the model was re-established by using SAS software. November 23 to 30, 2009 and January 1 to 6, 2010 is the tested date. And the forecasting module of the indoor air temperature has been verified by using these data. The two pictures below are the comparison diagram of predicted values and measured values using MATLAB subroutine to output.

Fig. 1. The test and prediction data inside air temperature of the solar greenhouse

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Fig. 2. The test and prediction data inside air temperature of the solar greenhouse Table 2. The test and prediction data indoor air temperature of the greenhouse Jan.2,2010 0

1

2

3

4

5

6

7

Predictive value

9.5

9.1

8.8

8.4

8.1

7.7

8.7

9.3

Measurements

9.3

8.7

8.6

8.4

7.9

7.9

9.1

10.3

8

9

10

11

12

13

14

15

Predictive value

9.8

10.2

11.1

13.6

16.9

19.3

18.4

16.4

Measurements

10.8

12.2

13.9

14.4

18.2

19.5

19.1

15.2

16

17

18

19

20

21

22

23

Predictive value

14.3

13.7

13.1

12.6

12.3

12.1

11.9

12.1

Measurements

13.8

13.6

12.9

12.7

12.6

12.6

12.6

12.5

4.1 Interpretation of Result Validation data used the data of the weather including sunny, cloudy and cloudy. Diagram one and diagram two shows that the variation trend with time of predicted values and measured values is consistent. And the temperature contrast between predicted values and measured values is small. The average relative error is less than 10%. Therefore, the simulation results which calculated by the mathematical model of are reliable. The two diagrams also show that the maximum between predicted values and measured values present to 9 o'clock and 10 o'clock after lifting up the quilt used to keep warm. Because the solar radiation light into the indoor, the indoor air temperature shoot up. But this prediction model cannot predict it. So this is the model should be improved.

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5 Conclusion The rebuilding heat transfer coefficients apply to the northern areas features of dry and cold. So the prediction model of indoor air temperature is feasible and applied. But the values predicted are not good after lifting up the quilt. And the problem will be studied next.

References 1. Sun, F.: Simulation and Analysis of Direct Light Environment in Typical Greenhouse in Beijing– Light Simulation and Analysis Facility Agriculture Research 4. Agricultural Engineering 9(2), 45–51 (1993) 2. Liu, H., Gou, W., Li, H.: Light Environment Simulation and Analysis of Greenhouse in Beijing. Journal of Applied Meteorology 19(3), 350–355 (2008) 3. Li, W., Dong, R., Tang, C., Zhang, S.: Theoretical Model of Thermal Environment in SolarPlastic Greenhouses with One-Slope. Transactions of the CSAE 5(3), 160–163 (1997) 4. Chen, Q., Wang, Z.: Dynamic Simulation of Thermal Environment in Solar Greenhouse 1(1), 67–72 (1996) 5. Li, X.: Mathematical Simulation and Structural Optimization of Greenhouse Thermal Environment. China Agricultural University (2005) 6. Meng, L.: Thermal Environment Model in Chinese Solar Greenhouse Based on VB and MATLAB and Structure Optimization. Chinese Academy of Agricultural Sciences (2008) 7. Wu, C., Zhao, X., Guo, L.: Simulation and Analysis of Greenhouse Crop Thermal Environment. Agricultural Engineering 23(4), 190–195 (2007) 8. Xin, B., Qiao, X., Teng, G.: Construction of Greenhouse Environmental Prediction Model. Agricultural Research 4(2), 96–100 (2006) 9. Wang, G., Kang, G.: Regression and Analysis of Greenhouse Environment. Beijing Agricultural College 9(2), 75–84 (1994) 10. van Ooteghem, R.J.C.: Optimal Control Design for a Solar Greenhouse. CIP gegevens Koninklijke Bibliotheek, Den Haag (2007) 11. Korner, O., Challa, H., van Ooteghem, R.J.C.: Crop based climate regimes for energy saving in greenhouse cultivation. Wageningen University, Wageningen (2003)

Remote Control System Based on Compressed Image Weichuan Liao School of Fundamental Science, East China Jiaotong University, Nanchang, Jiangxi, P.R. China

Abstract. Client computer controlling remote server computer or devices is usual for industry and agriculture automation. A monitor software system adopted C/S frame based on new algorithms of compressing image including improved LZ77, LZSS, LZW is designed. The client computer obtains compressed desktop image of remote server then decompresses the image on the client window to fulfill some mouse and keyboard actions of remote servers. Advantage of the control system by the new compress algorithms is that size of compressed image is smaller and system response time is shorter. Keywords: Remote control, Server, Client, Compressed image.

1 Introduction Remote control system software is developed by C/S frame, which adopted new algorithms of compressing image. Client computer can perform some mouse and keyboard actions of multiple remote servers to control remote terminal devices. First, Compressed desktop bitmap of remote server is transformed to a view of client computer, then the client decompresses the bitmap and displays the bitmap in the view, so the client can fulfill all kinds of operations of servers ,such as starting or shutting down computer, opening files of servers etc. The client can monitor multiple remote servers by creating one view for every server. This remote control system takes shorter response time and less network traffic than other popular remote control systems.

2 Frame of Remote Control System The remote control system adopts the popular C/S frame. Design of network communication bases on Winsock API of Microsoft windows system. The control system makes use of the recent improved algorithms of compressing image including LZ77, LZSS, LZW. The server computer compresses own desktop bitmap by some compressing algorithm and sends the compressed image to the client computer. After the client has received compressed bitmap then decompress the image, the client displays the image on a view of client window. In this view, the client executes mouse and keyboard actions which synchronously transmit to the server and server will execute same actions. The remote control system chooses VC++ Multi-document program. The system creates a new view when it controls a new server. Every server has a D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 259–263, 2011. © IFIP International Federation for Information Processing 2011

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corresponding view of the client monitoring own desktop. It looks like a Trojan, but our purpose is beneficial for industry and agriculture automation. TCP/IP is used for communication protocol of servers and client. Win socket classes are selected for developing this software.

Client

Decompresses Selects compressing Server Open view

Algorithm

Displays image

Gets own desktop bitmap

Selects Color mode

Compresses desktop bitmap

Fig. 1. System Flow Chart

3 Design of Server Server program is set up in computer monitored. Server creates a thread received messages from the client, will send local compressed desktop bitmap to client and executes commands from client selected some compressing algorithm. The flowing codes show us the thread how to handle all kind of commands from client. int CSeverThread:: Receive(BYTE *chMsgData, int nLen) {//execute codes after received commands from client. int nRet = 0; switch(MsgData [0])//First char represent type of command { case MSG_GET_DIB://Get desktop bitmap of server //computer

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if(SendDIBThread != NULL)//Terminated it , if //thread has being run. { ::TerminateThread(SendDIBThread->m_hThread, 0); ::WaitForSingleObject(SendDIBThread->m_hThread, INFINITE); //keep synchronizing pSendDIBThread = NULL; } pSendDIBThread

=

AfxBeginThread(SendDIBThread,

(LPVOID)m_sckClient); // Start up new thread that send desktop bitmap of //server computer to client. } break ; case MSG_MOUSE_MOVE://Received mouse action //from client and move mouse focus. { POINT point; point.x = *((int *)(chMsgData+1)); point.y = *((int *)( chMsgData +1+sizeof(int)));//Move mouse focus to //Mouse moves to new position based on commands of //client computer. MouseMove(point);//Set new position of mouse // focus. SendMSGData(m_sckClient, NULL, 0, PL_TEMP);//Send message that //mouse action has been finished. } break; //other commands. } The following chart shows client how to control remote server computer.

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Client sends commands

Server receives commands

Classifies the commands

Executes commands

Notifies the client that mission is finished

Fig. 2. How servers to execute commands from the client

Server has started up two threads, one of them handles all commands of the client and other of them is responsible for sending desktop bitmap of server computer to the client. The last thread sends desktop bitmap of server computer based on one of three new algorithms of compressing image including improved LZ77, LZSS, LZW. Because of adopted new compressing algorithms, Size of compressed image is smaller and system response time is shorter.

4 Design of Client Design of client uses the frame of multiple documents of VC++, the client can control multiple servers. The client creates new view display the bitmap after the client has received the compressed desktop bitmap of server computer and new document manage the data from server. Client managers can switch all of the views and control all servers. Improved algorithm LZSS is introduced by Wang, L.[3], improved algorithm LZW is introduced by Lan,B.[4] Next, we will show the details how client to control mouse actions of remote server computer. The client displays the bitmap in a view of the client after the client has received the desktop bitmap of server. When the client move mouse point in the view, the new position of mouse focus will transmit to server, and the server has received the new position and will do the same things as the client, which moves the mouse focus to the same position as in the view of the client. The following figure 3 shows the procedure of the client moving mouse focus of server. To sure that the same mouse actions have been executed by the client and servers, we use the GetCurPos() function to get position of mouse focus of the view of client, and mouse_event function in the server thread moves the mouse focus of server computer to the same position.

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View of the client

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Desktop of server

Displays the desktop bitmap

Performs the same mouse

of server, executes mouse

moving as the view of client.

moving

Fig. 3. How the client to control mouse actions of servers

Other control of actions, such as keyboard actions have the similar procedures will be not explained any more.

5 Conclusions Our remote control system compares with some popular remote control systems in Chinese market by local network, takes less response time. Response time of several systems Monitor System RV-3000 ZWPIC-GP Our System

Average Response Time 354.5 ms 313.4 ms 254.1 ms

Here, we explained main parts of a remote control system which adopted new image compressing algorithms. This remote control system takes shorter response time and less network traffic than other similar remote control systems. Acknowledgments. This work was partially supported by the Jiangxi Province Science Foundation No.2008GZS0012.

References 1. Ma, L., Fang, K.L.: A Remote Data Collect and Supervise System Based 6n Visual C++. Microcomputer Information 24(14), 1155–1159 (2008) 2. Liao, W.C., Wan, T.: Object-Oriented Design of Drawing Tool. Computer Engineering & Design 26(5), 1373–1376 (2005) 3. Wang, L., Xu, Y.: Application of Improved IZSS Algorithm in E-mail System. Journal of JILIN University (Information Science Edition) 23(3), 112–128 (2005) 4. Lan, B., Lin, X.Z.: Application of an Improved LZW Algorithm in Image Coding. Computer Engineering and Science 28(6), 210–215 (2006)

Analysis of the Poverty-Stricken Rural Areas’ Demand for Rapid Dissemination of Agricultural Information — Taking Wanquan County in Hebei Province as an Example Xiaoxia Shi1,2 and Yongchang Wu1 1

Chinese Academy of Agricultural Sciences Institute of Agricultural Resources and Regional Planning, Beijing 100081, China [email protected] 2 Graduate School of Chinese Academy of Agricultural Sciences Beijing 100081, China [email protected]

Abstract. The majority of rural population in China has limited access to agricultural information at present. Wanquan County was chosen as the studied area, in where the research used questionnaire to survey the current situation of peasants’ demands for agricultural information, as well as the information delivery approaches. A questionnaire was developed and data were collected from 200 peasant households. The major content is focused on the demands for the information service of peasant households, as well as the supply mode of agricultural information acquisition. The survey shows the information dissemination is irregular and the channels of information acquisition are serious in poverty-stricken rural areas. Although there are different types of peasants’ demands for agricultural information, the utilization rate is low. Bridging the digital divide between urban and rural areas will be a major challenge for government in Wanquan County. On the basis of analysis, the research designs a local characteristic industry platform for the characteristic agriculture. Keywords: Questionnaire to survey, Agricultural information resources, Information demand, Information delivery approach, Development and utilization.

1 Introduction During the rapid development of information technology, the information is not just a kind of instrumentality but a kind of resource. At present, information technology (IT) has become the most advanced and the most active element of production [1]. The quick development of IT, especially the wide use of communications media has made agricultural normalization to be the most important field of IT. Information and communication technologies play a major role in improving the quality of life in rural areas, imagining a world where information is the medium of exchange. The significant progress in information construction of China agriculture has been made, but there still exists problems-- the literacy level of rural village is still low, which is the block of the information development in rural areas. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 264–273, 2011. © IFIP International Federation for Information Processing 2011

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In different rural areas of China, peasants in poor areas have more dire need for agricultural information, but the lack of information resources have caused the gap between that of developing areas, and led to the efficiency of information service in developing rural areas much lower than developed rural areas [2]. Wanquan County in Hebei Province as the typical poverty-stricken county in China., in where the majority of the peasants live in the lower stand of living and have little or no access to agricultural information. The author had in-depth studied about the peasant households in Wanquan County through questionnaire, intensive investigation, and site visits [3]. Data were collected both at village and peasant households levels.

2 Basic Research To achieve the objectives of this study, statistical analysis was employed. The questionnaire was pre-tested and revised, including both open-ended and fixed choice questions, and the open-ended questions were used to gather information to encourage peasants to provide feedback. This text was from analyzing the peasant households’ information demands, including the characteristics of demands on information service, affordability and willingness to pay, on the basis of analyzing information demands of peasant households and the level of information construction of Wanquan County, as well as the peasants themselves, such as the social status, education level, family status and so on. This research aimed at putting forward the supply-demand equilibrium mechanism of information service oriented by demand and the construction of multiple service system, such as the way through which the communication of information can be conveyed to them promptly and effectively [4]. 2.1 Basic Condition of Survey Area Situated at northeast of Hebei Province, Wanquan County, is located northeast by North China plain, the east is near Zhangjiakou city, the west and north is next to the Great Wall. By a series of mountain range, the county covers an area of approximately 1161 square kilometers. It governs 172 administrative villages and has a population of 220,000 plus, which is a typical poverty-stricken county in Hebei. The peasants in Wanquan County are still practising an extensive traditional cornbased agricultural system dominated by non-shifting cultivation, which requires large investment of labour but provides little return. Wanquan stands for the general situation of poverty and features in rural areas, which will be exploited as backward area.

3 The Status of Survey Subjects 3.1 Personal Characteristics of Peasants Statistical analysis method was used to analyze the data of the basic status of the peasants themselves. Under investigation, table 1 shows the ages range from 14 years to 79 years, and the average age was 49.56 years, but the rate of females (6.0%) was

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lower than that of males (94.0%). The educational level of the peasants was collected through a household survey, and was calculated by adding total number of years of schooling completed by each survey subject, getting the mainly levels were junior middle school or below. Its scope of business covered fields such as agricultural production (69.5%) and others (30.9%). Table 1. Personal characteristics of peasants Sex Age(years) Education level Field of work

Males (94.0%)

Females (6%) Average age=49.56 junior middle school (73%) agriculture (69.5%) others (30.9%)

3.2 The Status of Peasant Households Results showed that as a family generally, it had an average of five members. To view the situation as the number of communications media, as one of the most popular and effectual propagation medium at present, the television played an important role, as well as the mobile phone. And almost every family had a TV set, mobile phone averaged out at 1 per head. However, this survey found a small proportion owned computer, followed by telephone and radio. Judging from the number of means of transport, bicycles and battery cars took the largest proportion, followed by motorcycles and mopeds, with the smallest proportion of cars, as well as some cattle carts. Used for the transport in agricultural production, mostly cattle carts and motorcycles were popular. Looking at from the family financial circumstances, most farmers, including the poorest, were in the outside business not only to feed and clothe themselves and their families, but also to make money or at least barter their food. Except the essential payment amount over each individual year, each family remained 10,000 Yuan average. Peasant households’ information needs depended largely on their activities in production and livelihood levels, by which there were some differences among subsistence peasants, livestock peasants and part-time peasants. Those part-time peasants concentrated on information of non-farming opportunities. Peasant households in the survey were not engaged in the textile and transportation business.

4 Investigation of Peasants’ Information Demands 4.1 The Way of Acquiring Information The questionnaire defined information channels as all technologies that are electrical appliances, including telephone, video, radio, mobile phone and computer. The major channels to gain information for peasants were the popular media such as television and mobile phone. Broadcasting was no longer the main channel of information communication in people’s lives. Fig. 1 shows that television and mobile phone had become the important channels for users to acquire knowledge and information, which were the most basic household

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electrical appliances. Mobile phone as the most frequently communications satellite, was essential to users. The mobile phone had high percentage in the effective dissemination of information, but was not as good as the television. Since it can be seen clearly, television played the important role in information dissemination. Among other communications media, TV possesses the following features: low price, popular quality and good ratings. According to the data, the rate of peasants who subscribed to the newspapers and magazines was 0%. There was only a small part of the villagers used to borrow the newspapers from the village committee. From our data, the computer’s rate at the city level is an astounding 76.56 percent, compared with only 9 percent at the bottom end of Wanquan County. Most of the peasants did not even come into contacting with the internet, and had little or no knowledge of the internet. The effect of computer in rural area's science and technology information communication is limited. Computer mostly was used for entertainment by the peasants who owned a user-friendly computer model, while acquiring information from computer was not quite universal.

rate

Electrical appliances radio

TV

mobile phone

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computer

Fig. 1. The rate of information media

4.2 The Information for Public Awareness The growth in income and the marketization in the Chinese economy create new demands on information service. According to the survey on information demands, in our questionnaire, four options were put out for the information for public awareness, including (1) Actively seek it, (2) Do not actively seek it, (3) Not interested, and (4) No need. This paper briefly found the existence structures and discernment of potential information demands in the information services, and the strong will of peasants. The descriptive statistics of these variables are presented in Fig. 2. They tend to seek life and production information, however, little information was being made available. Because of the influence on ability in the schooling, knowledge structure, search and receive message, but no matter peasant households were regarded as the purchasers or the suppliers of agricultural products, the information occupied was often insufficient, often suffered losses because of inferior position of the market information. In view of the increasing concern about agricultural information and awareness of peasants, the peasants hoped to accept practical information by any way.

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˄˅Actively seek it

˄˅Do not actively seek it

˄˅Not interested

˄˅No need

Fig. 2. The Information for public awareness

4.3 The Characteristic of the Information Demands of Peasants Demands difference for information service was gradually obvious. Generally, peasants demanded for many kinds of information in their production and live, however, they paid more attention to science, technology information and market information related to improving agricultural production and raising their income. The three top information types most needed in this research were: agricultural production service, the price of agricultural products and weather forecast. The information of social welfare, healthcare and education were also being regularly used by most of people who were in the pursuit of better quality livelihoods, which were the New Peasants’ initial performance. A kind of required information about off-farm income opportunities are scarce and agricultural products, mainly summer maize and potato, are the major sources of income. Each of these components plays a role in agricultural information acquisition, a role that depends on the strengths of the types of information resources and element of the agricultural economy that it serves [5]. Peasants’ diversified demands for information were increasing. As a multiple choice testing in the questionnaire, the question was described as “which kind of information means provided will you choose?”, and the four options were (1)Completely free, (2)Exchange with other people, (3)Cost price,(4)Market-pricing. Compared with the three options, most peasants (94.0%) hoped to get information resources completely free (table2 shows). Ability-to-pay was inconsistent with the willing to pay to a certain degree. However, peasants still could not get a lot of cheap agricultural information, but did not know where and how to have the liberty to free Table 2. Acceptable of information supply (Multiple choice test)

Acceptable categories

Completely free

Exchange with others

Rate (%)

94.0

25.5

Cost price 16.0

Marketpricing 2.50

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information resource access. Peasant households were lack of the effective mechanism to appeal to information service demand. 4.4 The State of Technical Training The number of technological knowledge training to peasants was just 3 last year, which was only aimed at the training of Plastic Green House. While the results were not optimistic, and the rate of training peasants was only 5%, since only a minority of peasant households were growing greenhouse. However, through visiting these households, greenhouse cultivation was performed without the guidance of professional, so the peasants could only use their own production experiences to manage the greenhouses, leaving the disastrous consequences. The peasants were interested in the opportunities of traditional training and lectures on agriculture subjects. In response to such needs, several notable trainings of agricultural information should be emerged in recently. Insufficient efficiency of the agricultural information service is the reason that, up to now, only simple training model can be implemented. 4.5 The Economic Structure Is Single The economic structure of this county is single, and hard to dig out new economic growth points. Summer maize had been the main grain crops for many years, which made crop diversity poor. Corn stalk was burned in the local traditional incineration method, but the maize straw was not employed. With the short of the new production equipment and technological processes under the pressure of low information consciousness and restriction on local people’s access to information age, so improving information technological equipment in many places has become a social and economical problem . Facilitative change in the economic structure asks for strengthening information service consciousness and upgrading service levels.

5 The Causes and Analysis 5.1 The Information Literacy Is Low Because of the lower levels of literacy and information literacy awareness of opportunities and benefits that information service can provide, the effective rate of utilization of information was low. In poverty-stricken area, it is lack of culture living and peasant’s culture diathesis should be improved. 5.2 Financial Resources Are Inadequate Getting the essential knowledge to those who need the agricultural information most remains difficult and expensive, but much optimism has been generated as a result of the increased growth of new electronic information services, such as digital television and mobile phone. At present, computer based is used effectively in developing areas, although peasant households have preliminarily possessed ability-to-pay information service, but

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the whole level is quite weak. Demands for the information service of peasants were influenced by several economy factors, and ability-to-pay was inconsistent with the willing to pay to a certain degree, which led to the deficiency of demands for the information service of peasant households on the whole. Based on the phenomenon of above, although facing the unprecedented changes of knowledge economy and information society, experiencing no benefits, peasants hardly ever got the vast quantities of information. 5.3 Lack of High-Quality IT Talents This survey showed the lack of information technology resources was extreme in this county. So it urgently needs to train and promote large numbers of IT professionals for the country’s information acquisition development. The government should speed up the agricultural extension and technological achievements in agriculture into productive forces and spread the use of agricultural information [6] [7]. It gets that a lack of reliable information at all stages along the supply chain is a severe limitation on the development of agriculture, in spite of the existence of modern information service [8]. Survey results showed that, this county will need further strengthening rural village concept of technical training, which cannot be just accomplished by simple inputoutput all kinds of information that obscure the key consciousness in the public on the ultimate development goal. Convincing arguments are needed that address public academic quality concerns [9]. The ability of organizations and individuals to introduce new methods and processes of agricultural information that are socially or economically relevant, particularly with respect to smallholder peasants who represent the majority of agricultural producers in the county. 5.4 The Development of Agricultural Market and Industrialization Is Backward In the context of small scale farming and without building an agricultural industrialization system, leading the backward development of the agricultural market. On further examination, it found that the production model-the scattered peasant organize production, which hindered the rapid dissemination of information. 5.5 Legislative, Policy and Regulatory Hurdles The policies that provide affordable access to information need was not perfect, which needs to be carefully indentified and examined by government. According to the statistical result of the data provided by the 200 questionnaires, the fraction of referring to agricultural information common principles is as high as 93%. It is now clear that people’s desire for information policy is strong.

6 Conclusion and Suggestion 6.1 Bring Up the High-Quality Peasants Peasants are the direct beneficiaries of the agricultural information, as well as the subjects of advanced new agricultural information. However, the overall qualities and

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information literacy were low in the rural areas. As the backward rural economy and culture of China, the low educational level directly limits the ability to learn information technology and the capacity to use the information analysis [10]. The state of consciousness was enclosing, as a trial or as a means of entering the information age. New things on the network, such as unconfirmed message and effective information, the peasants were unable to recognize the significant role of information and were lack of enthusiasm for using information effectively. At the same time, information literacy should be greatly improved, so that the peasants will be able to collect, use, feedback and identify the information. In order to help peasants in this poor area improve their scientific and cultural level, the effective methods can be used in the process of transmitting information, the methods can play a complementary role and the role of high-level development of information technology. Different levels of peasants left behind different educational approaches, especially with respect to agricultural information education and research. Evidence shows that even small efforts to put rural dissemination of information policy on the national agenda can have significant results. 6.2 Using Effective Measures to Disseminate Agricultural Information Effective measures should be taken to expedite the construction of agricultural information. For one thing, the shortage of managerial and technical specialists was the major constraint. To set up and improve the system of agricultural information specialists, so there is a need to balance channels of supply and demand for the information service of peasants. Many countries as successful cases linked their research programs in agriculture to information research and extension organizations, a structure reflecting the influence of the agricultural information. Based on the above phenomenon, the current investigation discovered that the countryside information extended to the households by network lays was completely not realistic [11]. Compared with internet, using some mature ways, such as the broadcast television, telephone, technology books as well as kinds of training can provide affordable cost information, which can disseminate the agricultural information knowledge for the county. 100% peasant households had the television, and mobile phone’s rate achieved 90%, therefore using these good communications media is reality. 6.3 Calls for the Government’s Reform and New Responses The government is still the most important organization institution. Transfer the role of government and to encourage peasants to actively take part in information service construction. It is thus not surprising that there is a constant call for government reforms in subWanquan County—reforms that are meant to improve government’s contribution to the creation of more innovative, agricultural information across the county. In response to such calls, several methods of reform should be set up by government. The challenge for agricultural information policymakers and planners, particularly in the context of this poverty rural, is how to enable people to adopt more attractive agricultural information that do not have adverse impacts. Furthermore, in line with the basic

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principles of dissemination of information, agricultural information policy measures further promotes the use of agricultural information and help to preserve the development of the rural economy [12]. There is a real need to provide rural people with the best information in a timely fashion so they could develop sustainable communities, and reverse urbanization. The information consciousness of cadres and masses should be strengthened constantly, leaving peasant household’s information demand strengthened constantly. And ultimately, recognition is needed of the fact that information service designed to strengthen the speed of dissemination of agricultural information is a long-term undertaking—only through a long-term change can information service contributes to the development that peasants in a wider system of information service. 6.4 Build Up a Local Characteristic Industry Platform Building up a characteristic of industry platform must be carried on promoting the demonstration and expanding the influence. The agricultural production holds an important position in Wanquan, summer maize is the main grain crop. Agricultural restructure will be further strengthened, with the stable growth of production in this county, in order to accelerate the economic development of the county. Exert great efforts in developing New Waxy Corn Variety, and the mass result will be with the form of “company +base +peasants”, and “market +information +peasants”, developing Fresh-food Maize’s planting base and industrialization processing. County and township governments need to control through planning, guidance and resources of industry consolidation, and make the "baby corn" into a large industry, "small workshop" into Industrial Park. The industrial restructuring and resource integration of this county must be accelerated by government, which needs to exploit its advantages in geography and resources and create points of economic growth. In order to change cereal-based subsistence agriculture to fully market oriented commercial agriculture, with intensive use of putting platform. Eventually, the project can drive a rapid rural economic development in the region. Furthermore, in line with the basic principles of good agricultural information, agricultural policy measures further promote the use of this local characteristic industry platform and help to improve the life of the rural village.

References [1] Hosseini, S.J.F., Niknami, M., Nejad, G.H.H.: Policies Affent the Application of Information and Communication Technologies by Agricultural Extension Service. American Journal of Applied Scinences 6(8), 1–2 (2009) [2] Cui, Y., Qian, P., Sun, S., Zhang, J., Luo, C.: Studies on Agricultural Scientific and Technical Information Core Metadata Register System. Agricultural Scinences in China. J. 7(2), 249 (2008) [3] Jian, X., Feng, H.: Analysis on the Features of Information Needs from Different Farmers in the Poor Chinese Rural Community. Review of China Agricultural Science and Technology 9(2), 113 (2007) [4] Gai, L., Tao, F.: Analysison Agricultural Information Research. Agriculture Network Information 2 (2007)

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[5] Wu, J., Xie, K., Xiao, J.: Information Economics, China (2009) [6] Liu, J.: Development Status, Problems and Countermeasures of Agricultural Information in China. Modern Information 22(1), 62–63 (2009) [7] Hu, S.: Agricultural Information Resources Development and Utilization for Needy Mountainous Area. Agricultural Information 10, 46–48 (2007) [8] Niederhauser, N., Oberthu, T., Kattnig, S., Cock, J.: Information and its Management for Differentiation of Agricultural Products: The example of Specialty Coffee. Computers and Electronics in Agriculture 61, 242–245 (2008) [9] Eiard: Impact Assessment and Evaluation in Agricultural Research for Development. Agricultural Systems 78, 332–333 (2003) [10] Zhang, J., Zhu, Y., Min, X., Xin, H., Zhuang, J.: Thinking on the Integration of Information Resources in Agricultural Information. Hebei Agricultural Science 13(6), 141–142 (2009) [11] Xia, Q.: Thoughts on Informationalized Reporting System of Depressed Areas. Rural Economic 7, 93–94 (2005) [12] Liu, Z.: Research on Internet Communication of Agricultural Information in China. Agriculture Network Information 5, 10–11 (2008)

Research and Analysis about System of Digital Agriculture Based on a Network Platform Duan Yane School of Computer & Information Engineering, Beijing Agricultural College, Beijing, P. R. China

Abstract. Digital Agriculture is an “intelligent” computer management and application system that includes many high technologies of, such as infomationization, digitization, network, automation and more so on. On the basis of analyzing the main content and meaning of “Digital Agriculture”, this paper designs the system construction of “Digital Agriculture” based on Internet, and discusses the function and structure of main core modules of System of Digital Agriculture in detail. Finally, this paper also analyzes the developing trend about system of “Digital Agriculture”. Keywords: Digital Agriculture, Decision Support System of Agriculture, Agriculture Module.

Preface The former American vice-president AI Gore put forward the concept of introduced the term “Digital Earth” in a speech addressed at the California Science Center on January 31, 1998. From that time, the concept of "Digital Earth" has aroused great concern and become one of the newest researching hotspots in global science and technology field. It has remarkably accelerated the development of digitalization in different fields. Digital Agriculture is one of the most important parts of Digital Earth and is the extending of “Information Superhighway”, “Digital Earth” and “Knowledge Economy” within the field of agriculture. Digital Agriculture is a comprehensive technology system of agricultural production and management combined the technology of Digital Earth with the technology of modern agriculture. It is the only way of the future in agriculture modernization, integration, and automation and can advance agriculture technology revolution. It brings about the further developing trend of agriculture in Digital Agriculture bring agriculture into the informational generation.

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Introduction of “Digital Agriculture”

In the new century, agriculture will turn into water-saving agriculture, mechanical and intelligent agriculture, and high-quality, high-yield, pollution-free agriculture. Digital Agriculture is a necessary and effective approach to realize all of these purposes, and is the core of agricultural informatization. Digital Agriculture is also called informational agriculture or intelligent agriculture, which refers to the utilization of Digital Earth D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 274–282, 2011. © IFIP International Federation for Information Processing 2011

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technology, including multi-resolution remote sensing, telemetering, GPS and GIS, computer etc. It also involves all the hi-tech systems which are unified with the agricultural production activity and the production management, such as including rapid collection of farm data on cultivation, land management, agricultural chemicals utilization, contamination control, agricultural engineering equipments and their industrialization technology and so on. It’s in short, it’s a comprehensive agricultural production management technical system which integrates Digital Earth technology and the modern agricultural technology. Beginning in the late 90’s, Digital Agriculture and the research associated with it was systematically put into practice in developed countries. Now, the research of Digital Agriculture has reached a higher level in those countries. The using of these hi-techs has gotten to a practicable level at many developed countries. They have generally realized that users have been able to collect, manage, product and transmit any kinds of information of agriculture supported by GIS, RS, GPS , DSS and computer network etc..For example, the United States has created an information managing system of crop strains throughout the nation, and allowing the information of more than 600 000 sample plants has to be managed by computers. The Plant Protection Bureau of the French Ministry of Agriculture has built nation-wide computer networks to survey and forecast diseases and insects, which can provide the true picture of diseases and insects, the forecast of chemicals and the evaluation of chemical remains. The Japanese Province of Agriculture, Forestry and Aquatic Products has built a data bank system of many crop strains, such as rice, soybeans and wheat, etc. The Research Academy of New Zealand Agriculture and Husbandry provide all sorts of information services, the so-called "farm system"[1]. China is a great agricultural country with a large population, limited soil resources and traditional manual farming methods, with these challenges facing them, the central government has been attaching great importance to the development of agriculture and put forward a new agricultural technology revolution -- the transformation from traditional agriculture to modern agriculture and from extensive farming to intensive farming. From 1998, China officially initiated the concept of “Digital China”, and this bring about the encouraging development of Digital Agriculture which can promote agriculture technology revolution, two transformations of agriculture and agricultural development[2]. To-date many provinces have built different kinds of system about Digital Agriculture including an Expert system of wheat, crop simulation modeling, Pesticide Ranking, web-based seedling production Management and so on.

2 Research Contents of Digital Agriculture Digital agriculture is an advanced combination of agricultural science, modern computer technology, network communication and spatial information technology, and the combination will become agricultural development’s new pattern in the 21st century[3].The essence of Digital agriculture is to utilize the digitization of each kind of process in every aspect of agriculture (including crop production, animal husbandry, aquatic products industry, forestry)by using Information Technology. It includes Digital Informationization of Agricultural factors (biological, environmental, technical and social), Digital Informationization of agricultural process, Digital Informationization of agricultural managements (agricultural administration, agricultural production

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management, agricultural science and technology management and agricultural enterprise management). In other words, each agricultural field and process must be expressed by the binary numeral (0, 1) and digital model. So Digital Agriculture, with the exception of production of crop, also will include precision gardening, precision foster, precision processing, precision running and management even includes forestry, animal husbandry, foster, processing, production, supply and sale. It is an agriculture technology system with the whole procedure of the whole agriculture will be characterized by digital, network, and intelligence, using technology of remote sensing, telemetry, tele-control, and computer. This will result in effective business, with intelligence management, reasonable job labor for agriculture product - make every square meter have optimized utilization. It will constitutes an information agriculture technology system including monitor and estimating of crop, land , and soil, current or dynamics analyses of crop growth, and factors of environment, diagnose forecast, cultivation step, management planning and decision support[4].

3 System Architecture of Digital Agriculture The system of Digital Agriculture involves various aspects. This includes the construction of a database, Metadata standard, monitoring system, forecasting and

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decision-making system, and information-releasing system etc. This roughly falls into four levels: information basis level, functional modules level, integral application level and integral web portal level which integrate with each other (Fig.1).

4 Structural Designing of Digital Agriculture System 4.1 Information Basis Level The environment of farmland is a very complicated ecology system and involves many different kinds of factors, such as soil, fertilizer, moisture, sunshine, temperature, atmosphere, etc. All of these data includes features of enormous, dynamic, regional, and sequential. In addition, the collection and expression of agricultural data includes not only directly related factors, but also indirectly related factors. So consequentially the information basis level which takes charge of data collection, processing, and analysis is an important and necessary level of Digital Agriculture System. This level includes public information basis, attribute information basis and spatial data basis facilities. The public information basis includes laws, rules, regulations and technology standards. The attribute information basis takes charge of the management of non-spatial attribute data. The spatial data basis is a consistent, integral geo-spatial data and service system, including spatial data structure of digital agriculture, coordinated management of spatial data, renewing and distribution system, exchange standard of spatial data and metadata, etc.(Fig.2).

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In this level, the acquisition of data includes how to acquire field data rapidly and effectively and how to transmit the field data correctly and at a low cost. Agricultural production environment and agricultural production process includes dispersed

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collection points, long average collection period, low speed, small amount of data, bad conditions of field etc[5]. All of these factors increase the degree of difficulty of data acquisition. This years, the related method and technology of data acquisition includes manual measurement, statistics and analysis of experiments and modern automatic collection. In these methods, modern automatic collection has the features of higher precision, faster speed, wider range of data and more data. This has gradually become the main method of data collection. It involves RS (Remote Sense), GIS (Geography Information System), GPS (Global Position System) and network technology. 4.2 Functional Modules Level Functional Modules Level consists of management, update, search and analysis of agricultural basis information database (non-spatial data and spatial, static and dynamic data), expert knowledge database and agricultural model database. It includes Agriculture MIS (Management Information System), Agriculture ES (Expert System) and Agriculture MS (Model System) (Fig.3).

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The function of Agriculture MIS involves the management, update, search, statistic and output of agriculture basic attribute information (such as product, biology, Science and Technology, economy data etc.), and spatial geography information (such as environment resource, agricultural condition, agriculture produce data etc.), it also includes the function of comprehensive management, search, analysis and output of attribute data and spatial data. Agriculture ES mainly involves the creation of knowledge database, advisory of expert, search and output of knowledge, etc. Knowledge database mainly stores and manages special agriculture expert knowledge; this knowledge includes basic fact (test specimen) of agriculture, book information, common sense, and knowledge from agriculture experts. The quantity and quality of knowledge is the key factor of Expert System and would affect the reliability of the solution. Agriculture MS is used to store and manage different kinds of agricultural models, such as agricultural spatial analysis models, crop simulation models, comprehensive evaluation models, statistics and analysis models. It is the theory basis for Digital

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Agriculture, process management, intelligent decision of the scientific plan of agriculture production. 4.3 Integral Application Level The integral application level is based on the network platform and takes charge of system integration of different special modules of digital system. It mainly includes agriculture comprehensive MIS, virtual agriculture system, Agriculture DSS (Decision Support System), Agriculture Automatic Monitor System. 4.3.1 Virtual Agriculture System The virtual agriculture is one of the greatest key subsystems of digital agriculture. The rationale of virtual agriculture is the truth that the relationship between crops and environment is computable [6]. The virtual agriculture system takes networks and computers as a platform to simulate and duplicate the studied objects of each link in the agriculture and achieve the aims of interaction and visualization of studied objects and environments. In a broad sense, virtual agriculture includes: virtual crops (Fig.4), virtual animals, virtual agricultural machinery manufacture, virtual farm, etc. [7].

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4.3.2 Agriculture DSS Agriculture DSS (Decision Support System) means the use of DSS in the field of agriculture. DSS can be considered as a computer based system which allows the user to solve semi-structural processes by using comprehensive datasets and analytical models, according to El-Najdawi and Stylianou [8]. Agriculture DSS is built on the basis of agriculture information system, crop simulation system and agriculture expert system, and can be used in any fields of agriculture. Today, the research of agriculture DSS has developed from single field decision (such as DSS for Pest Management, cotton efficient fertilization DSS) to an integrative DSS, moving from a single user system to

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Spatial Database Expert

Public Information

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Attributes Users

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Fig. 5. Agriculture Decision Supporting System

multi-user B/S system. Through Internet, expert knowledge can be obtained at any time, from any place, by any person (Fig. 5). 4.3.3 Remote Auto-Monitoring System of Agriculture Remote Auto-Monitoring System of Agriculture involves the communications between the computer and remote data collectors with the network (such as GSM). The entire system is composed of data management center and remote monitor terminal (Fig.6).

Fig. 6. Remote Monitoring System of Agriculture

The function of the data management center is to control the network module to send and receive messages and to display, store and print the data received from the remote terminal. The function of remote monitor terminal is to send and receive the messages,

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deal with the collected data and send data back to data management center. The system is based on B/S mode; users can browse the data and set the parameters using the browser. 4.4 Comprehensive Integral Web Portal Level The Internet is the important technological platform which ensures remote data collection, remote management, remote monitoring of agricultural production. By using the portal website, Digital Agriculture system can integrate collection of data; analysis of data; simulation of process and support decision together and create an integrative flow of information. Users can have different operations and get different results according to their requirement with the browser (Fig.7).

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Fig. 7. Internet Server Platform of Digital Agriculture

5 Summary Digital agriculture is based on the modern theory of agriculture while using the technology of digital Earth. It will promote production and maintain developing agriculture by realizing intelligence of procedure of agriculture. The research of Digital Agriculture not only is the support technology but also is the strategic target which modern agriculture will choose inevitably. Implementing Digital Agriculture in different areas in our country is at the exploration stage. In the last ten years, the rationale and application of engineering research system related to the technical system of digital agriculture has become an active domain of high & new technology research in the developed country. The application and development of digital agriculture in China will become important for agricultural development in the 21st century. By the

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multi-disciplinary fusions and the coordination, digital agriculture will effectively change the rural industrial structure, increase yield and farmer’s income, improve the countryside ecological environment, and realize the sustainable development of agriculture and rural economy.

Acknowledgement This work is elaborated within the project of “Research on Growth Evaluation and Physiological-Ecological Simulation of Peach”, No. KM201010020011 funded by the Beijing Municipal Commission of Education.

References [1] Ouyang, X., Zhang, J.: Some thoughts on the transformation of agricultural information. Review of China Agricultural Science and Technology 4(3), 76–80 (2001) [2] Liang, Y., Lu, X., Zhang, D.-g., Liang, F., Ren, Z.-b.: Study on the Framework System of Digital Agriculture. Chinese Geographical Science 1(13), 15–19 (2003) [3] Wei, T., Liu, Y.: Study on the Development Strategy of Digital Agriculture in Hebei Province. Journal of Anhui Agricultural Science 36(30), 13458–13460 (2008) [4] Cheng, J., Yi, S.: Digital Agriculture–One of Application Domain of Digital Earth. In: Towards Digital Earth — Proceedings of the International Symposium on Digital Earth. Science Press, Beijing (1999) [5] Sun, G., Zheng, W., Zhao, T., Shen, C.: Study on the Field Soil Moisture Montoring Sytem based on GSM-SMS Technology. In: Proceedings of the 4th International Symposium on Intelligent Information Technology in Agriculture (ISIITA). China Agriculture Science & Technology Press (2007) [6] Wang, Y.: Situation and Development of Digital Agriculture. Transactions of the CSAE (suppl.), 9–10 (2003) (in Chinese) [7] Li, H.: Analysis of Virtual Reality Technology Applications in Agriculture. In: Proceedings of First IFIP TC 12 International Conferences on Computer and Computing Technologies in Agriculture (CCTA 2007), vol. (I), pp. 133–139 (2007) [8] Liang, Y., Lu, X., Zhang, D., Liang, F.: The Main Content, Technical Support and Enforcement Strategy of Digital Agriculture. Geo-spatial Information Science (Quarterly) 5(1), 68–73 (2002)

Research and Development of Preceding-Evaluation System of Rural Drinking Water Safety Project Lian He and Jilin Cheng Yangzhou University, Yangzhou, P. R. China [email protected]

Abstract. The preceding-evaluation plays a crucial role in guiding construction. Preceding-evaluation system of rural drinking water safety project includes four main contents: opportunity evaluation, necessity evaluation, feasibility evaluation and decision evaluation. Using multi-turns specialist consultation method, the paper mainly studies the decision evaluation. Modular system structure is designed according to system function analysis and target analysis. Based on system database as the basic information support, preceding-evaluation system is developed by object-oriented method. The syetem includes the functions of project information storage, project estimation, economic analysis and decision evaluation, it can provide reference for planning, design and construction management of rural drinking water safety project. Keywords: rural drinking water safety; preceding-evaluation system; objectoriented method; decision optimization.

1 Introduction Protection of rural drinking water safety directly relates to the health and safety of farmers. Implementation of rural drinking water safety project is an important foundation work of building a new socialist countryside and a harmonious society. According to the national capital construction program, construction of rural drinking water safety project needs to go through a feasibility study, preliminary design, construction design, construction and other sectors. Any of problems in the process of building may lead to failure and loss of construction projects, especially the early decisionmaking directly relates to success of the project. In order to ensure decision-making being objective, just and scientific, a rational preceding-evaluation system is needed. Now preceding-evaluation works of rural drinking water safety project are not so standardized. There are irregular, incomplete evaluation index system, the lack of uniform evaluation criteria, complicated operation, with the qualititive evaluation as the main approach and evaluation only from the viewpoint of investment in current evaluation works, which lead to some planning and design of rural drinking water safety project extremely unreasonable, and even appear some reconstruction works. Although a lot of research work of evaluation system has been carried out in many domestic and international trades, but the existing evaluation results are often not directly applied[1]~[4]. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 283–289, 2011. © IFIP International Federation for Information Processing 2011

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The preceding-evaluation system of rural drinking water safety project needs to be studied to further optimize engineering decisions and reduce blind or wrong investment. Appropriate preceding-evaluation system software needs to be developed to provide practical evaluation management tools, to facilitate the smooth progress of preceding-evaluation and to ensure the actual effect of project investment.

2 Research of Preceding-Evaluation System of Rural Drinking Water Safety Project 2.1 Main Contents of Preceding-Evaluation of Rural Drinking Water Safety Project Preceding-evaluation is the general term for a variety of evaluation before project establishment. Its main task is to solve the contradiction between limited resources and unlimited demand through analyzing and confirming the necessity and feasibility of the project, and giving decision-makers the evaluation results of different options to choose. According to time, preceding-evaluation can be divided into opportunity evaluation, necessity evaluation, feasibility evaluation and decision evaluation[5]. Opportunity evaluation is to predict and assess potential opportunities. Necessity evaluation is to evaluate necessity of the project. Rural drinking water safety project is designed to solve the problem of drinking water for rural residents and is a major livelihood project. It is very necessary to construct rural drinking water safety project, so opportunity evaluation and necessity evaluation are not specially carried out. Feasibility evaluation is to evaluate project rationality from the aspects of technology, economy and operation condition, social and environmental influence. It includs four parts: technical feasibility, economic feasibility, operation condition, social and environmental influence evaluation. 2.2 Decision Evaluation of Rural Drinking Water Safety Project Decision evaluation is to integrate special evaluation results and to optimize decision. There is no decision comprehensive evaluation index system of rural drinking water safety project at present, to better measure and compare project feasibility, and to provide quantitative standard and scale for scheme decision, decision evaluation index system is studied. Index system is the scientific quantitative and qualitative expression of evaluation purpose, intent and will[6]. Firstly, the indexes should be chosen under the guidance of scientific concept of development and the idea of sustainable development; Secondly, the indexes which are easy to quantify, easy to access and easy to accurately determine should be chosen as far as possible; the content difficult to quantify can use qualitative index; Simultaneously, drinking water safety project involves a wide range and the large regional differences,so the indexes should be chosen on both universality and regional specificity. Common methods of selecting indexes have expert scoring method, principal component analysis method, the method of according to the minimum mean square error

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to delete index, the method of according to the minimax deviation method to reduce the index, correlation coefficient method[7]. According to evaluation objective and rural drinking water safety project content and in accordance with the principles of evaluation index system, decision evaluation index system of rural drinking water safety project adopts the method of combining theoretical analysis and statistical analysis, indexes are selected by multi-turns specialist consultation method, that is, specific index system is determined through consultation with expert advice on evaluation, statistical processing, focused expert advice after several rounds of feedback from consultation. This method is anonymous, feedback and statistical. It fully utilizes the knowledge and experience of expert groups and its results are more in line with the objective situation. Index system is a complex system, so decision evaluation index system of rural drinking water safety project is divided into target layer, criterion layer and index layer by layered frame structure. Target layer is the highest layer, embodies the comprehensive evaluation results and provides decision basis for the rural drinking water safety project. Criterion layer reflects evaluation results of different aspects of rural drinking water safety project, and includes four parts: technical feasibility, economic feasibility, operation condition, social and environmental influence evaluation. Index layer is the basis of the evaluation system and is constituted by the direct indexes of feasibility evaluation. Decision evaluation index system of rural drinking water safety project is showen in Table 1. Index weight is determined by experts consultation method. Table 1. Decision evaluation index system of rural drinking water safety project Target layer

Criterion layer Index name Weight

Technical feasibility

Decision evaluation

0.40

Economic feasibility

0.20

Operation condition

0.20

Social and environmental influence evaluation

0.20

Index layer Index name Planning convergence Water source selection Project scale Overall scheme Collection works Purification works Transmission and distribution works Application of new technology Investment estimation Financing National economic evaluation Financial evaluation Construction management Operation management Water quality detection Social benefit Economic benefit Environmental sustainability

Weight 0.10 0.15 0.15 0.15 0.10 0.15 0.15 0.05 0.40 0.30 0.20 0.10 0.30 0.30 0.40 0.40 0.20 0.40

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3 Design of Preceding-Evaluation System of Rural Drinking Water Safety Project System design includes objective analysis and functional analysis, system development goals identified, system components determined and programme implementation. 3.1 System Objectives and Functions The main objective of preceding-evaluation system of rural drinking water safety is to achieve opportunity evaluation, necessity evaluation, feasibility evaluation and decision evaluation functions, and to provide scientific decision management tools for rural drinking water safety project. The specific objectives of evaluation system as follows: to achieve the basic project data input, query and output functions; to achieve evaluation data query and output functions; to achieve investment estimation and economic analysis functions; to achieve the special evaluation function and to achieve comprehensive evaluation function. 3.2 System Components According to the objective analysis, system design adopts modular architecture. According to system functions, system is divided into engineering information module, project necessity evaluation module, project decision evaluation module, project investment benefit evaluation module, help module and other modules. Click data entry button on the drop-down menu of the project information module, you can input basic information data. Click the information query button, you can query and modify all the basic information and evaluation information of the project evaluated. Project necessity evaluation module can carry out opportunity evaluation and necessity evaluation. Project decision evaluation module can input and modify the indexes and weights, and evaluate project decision. The indexes selection and input sub-menu can choose correspondence index system according to the different project types; The weight selection and input sub-menu can choose or revise the corresponding weight; The project comprehensive evaluation system establishes the fuzzy synthetic evaluation model and expert scoring method to carry out decision evaluation to achieve project technical and economic comparison and optimize project decision. Project investment benefit evaluation module has investment estimation, economic evaluation and benefit analysis sub-menus. Click different menus, you can realize corresponding function. Help module can provide users with instruction to help users operation and solve problems in time. 3.3 System Implementation System is supported by database and supplemented by users-friendly interface and human-machine dialogue process. The whole system is divided into the database subsystem, the data input subsystem, data calculation subsystem, data output subsystem, error control subsystem and program help subsystem. The data input subsystem can achieve the basic project data input and evaluation information input, such as

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engineering information, engineering solutions and evaluation indexes. Data can be inputed by assignment control or selection control. Data calculation subsystem can calculate project investment and input or output data through computational module interface. Data output subsystem can output data in text or table format. Error control subsystem throughout the entire system can touch off debugging code running with direct inputing some predefined event of control itself, or establish an interactive link debug object and data input system by message mechanism, timely report the error to users and protect system normal operation through customizng objects and encapsulating code within the object. Program help subsystem penetrates every corner of the entire program process to provide user help information at any time. 3.4 Data Organization All operations are launched around the database which is the core of the evaluation system. Comprehensive data is the premise of scientific management and good decision, the data of preceding-evaluation system database of rural drinking water safety project includes three categories: evaluation data, engineering information data and economic data. Data design use object-oriented relational data structure, strictly followed relational schema normalization and meet the physical integrity, referential integrity, userdefined integrity principles. Data encoding formats use record format and dynamic list format to ensure data sequential and random management. Data storage and management use two network nodes of combination of library and table.

4 Development Approach of Preceding-Evaluation System of Rural Drinking Water Safety Project 4.1 Development Tools and Development Environment Visual FoxPro has strong object-oriented programming functions, users-friendly interface and easy operation features. Preceding-evaluation system of rural drinking water safety project uses the Visual FoxPro development platform, stores data using its powerful database management features, pulls object-oriented programming in evaluation system, and achieves cross-platform software integration with other software on the windows system. Software help system uses HTML HElP Workshop and FrontPage software. HTML HElP Workshop can be directly embedded applications and also run independently from the application. Document prepared has a similar interface with resource manager, which is easy to transfer files and browse web. FrontPage is easy to use and allows ordinary users to quickly write web. The project document in the software help system is prepared by FrontPage and compiled by the HTML HELP Workshop. 4.2 Development Method System development methods have process-oriented traditional development method and object-oriented development method.

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Process-oriented development method focuses on the system functionality to be achieved, which establish system function structure and the corresponding program module structure with self-directed, the gradual functional decomposition approach. Once the program needs to be modified, this way has heavy workload and is easy to produce a new error to make program degradation. Object-oriented development method is to make up the deficiency of traditional method. First it focuses on objects involving applied problems, establishes object structure and the sequence of events to solve the problem. Accordingly, it establishes class inheritance hierarchy to achieve the required functions through various news connection. Object-oriented development method can use specific software tools to directly complete the conversion from the object to the software architecture, solve the multiple complex converting mapping process from analysis and design to software module structure, shorten the development cycle, make the entire system stable and ensure the system will not be degraded due to changes. Therefore, preceding-evaluation system of rural drinking water safety which uses object-oriented software development method is more conducive to the establishment and modification and have higher reliability and maintainability than traditional method.

5 Application of Preceding-Evaluation System of Rural Drinking Water Safety Project Selecting rural drinking water safety project in Xuyi County in Jiangsu Province, the paper carris out decision evaluation with this evaluation system. Evaluation results is showen in Table 2. This project design schemes have three types: new construction city-west surface water treatment plant; new construction city-east surface water treatment plant; joint Table 2. Decision evaluation of rural drinking water safety project in Xuyi County in Jiangsu Province

Project scheme Technical feasibility Economic Special feasibility Operation evaluation condition scores Social and environmental influence evaluation Decision evaluation score

New construction citywest surface water treatment plant

New construction cityeast surface water treatment plant

Joint water supply of ground water treatment plants

36.8

30.8

32.8

16.0

14.0

18.4

20.0

16.0

12.0

20.0

20.0

20.0

80.8

83.2

92.8

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water supply of ground water treatmen plants. It’s known by the evaluation results that the scheme of new construction city-west surface water treatment plant is the best and the scores of technical feasibility, economic feasibility, operation condition and comprehensive evaluation are all the highest. The actual application shows that the index system is reasonable and that system operation is simple and convenient. It can greatly reduce the workload and improve objective accuracy of the evaluation results.

6 Conclusion According to the objective analysis and functional analysis, system design adopts modular architecture. Function modules include engineering information module, project necessity evaluation module, project decision evaluation module, project investment benefit evaluation module, help module and other modules. They are independent and interrelated and keep system stable. Preceding-evaluation system of rural drinking water safety uses simple manmachine dialog window and multi-page switching framework, sets help module to facilitate user operation. System uses powerful VFP language to write programs and uses object-oriented development mothod to keep high maintenance and reliability. The preceding-evaluation results are very important to project construction of rural drinking water safety, preceding-evaluation system of rural drinking water safety project can better measure and compare project feasibility, and provide reference for scientific decision.

References 1. Liu, J., Li, Y., Zhang, X.: Research on Synthetical Indexes System for Post Evaluation of Water-Saving Irrigation Project in Gansu Province and Effect Evaluation. Water Saving Irrigation (1), 31–34 (2010) 2. Xu, C.: Research on the Financial Evaluation of Transportation Companies. Orient Acadenvc Forum (Special) 10, 126–130 (2001) 3. Zhou, L., Zhang, J., Liu, Q.: Study on the Performance Appraisal System for Chinese Water Supply Enterprises. Chian Water & Wastewater 22(2), 75–78 (2006) 4. Yang, S., Gary, A.D.: Baycsian Estimation of Classified Mean Daily Traffic. Transportation Research Part A 36, 365–382 (2002) 5. Li, J.: Project Evaluation Methodology. Nankai University Press, Tianjin (2009) (in Chinese) 6. Zhang, X.: Theory and Method of Water Conservancy Investment Benefit Evaluation. China Water Power Press, Beijing (2005) (in Chinese) 7. Du, D., Pang, Q., Wu, Y.: Modern Comprehensive Evaluation Methods and Case Picking. Tsinghua University Press, Beijing (2008) (in Chinese)

Research of Evaluation on Cultivated Land Fertility in Xinjiang Desert Oasis Based on GIS Technology––– Taking No. 22 State Farm as the Example* Ling Wang1,**, Xin Lv2,***, and Hailong Liu3 1

Department of Geography, Teachers College, Shihezi University, Shihezi, Xinjiang Province, P. R. China 832003 [email protected] 2 Key Laboratory of Oasis Ecological Agriculture of Xinjiang Production & Construction Corps(XPCC), Shihezi University, Shihezi, Xinjiang Province, P. R. China 832003 Tel.: +86-13909931721 [email protected] 3 College of Water Conservancy Construction Engineering, Shihezi University, Shihezi, Xinjiang Province, P. R. China 832003

Abstract. This paper, with No.22 State Farm, Agricultural Division No.2, XPCC as the example and under support of GIS, takes cultivated land plot as the evaluation unit and chooses 13 influence factors from five aspects of nutrient of cultivation layer, physical and chemical properties of cultivation layer, soil management, climate conditions and obstacle factor to establish desert oasis cultivated land fertility evaluation system and model. On the basis of a great quantity of information related to cultivated land fertility through field sampling and laboratory analysis, and supported by GIS, the papers takes advantages of mathematics method and mathematical model such as hierarchical analysis process and fuzzy comprehensive evaluation process to realize automatic and quantitative evaluation on cultivated land fertility, analyze area statistics and distribution characteristics of cultivated land fertility at different levels and main attribute of cultivated land at various classes in State Farm No.22, and give analysis of the space distribution. Results show that cultivated land in state farm No.22 can be divided into six classes, of which Class 1 Land IFI (integrated fertility index) is 0.74~1.00, area 3788.11 hm2 , accounting for 7.67% of the total area; Class 2 - Class 6 Land for36.45%, 43.47%, 12.32%, 0.089%and 0.0001% of the total area respectively. Research results are of significance in guiding local cultivated land quality management and promoting construction of new rural area. Keywords: Geographical information system (GIS), Cultivated land fertility, Desert oasis.

*

Foundation project: the Foundation of Xinjiang Uygur Autonomous Region for Philosophy and Social Sciences (07JYB027). ** Primary author. *** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 290–299, 2011. © IFIP International Federation for Information Processing 2011

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1 Introduction Cultivated land is the foundation of agriculture and one of important resources guaranteeing sustainable development of the society and national economy. Evaluation on cultivated land fertility means a process assessing production potential and suitability of cultivated land for better utilization of land, so as to reveal bio-productivity capacity and potential productivity[1]. For recent decades, several surveys of land resources and general surveys of soil have been made in China. However, with social and economic development, great changes have taken place in quality and quantity of land utilizations and agricultural land(particularly cultivated land), existing data have become outdated and traditional survey method backward, failing to meet the need of modern agricultural production. GIS technology, however, can realize effective management of cultivated land resources with space characteristics. In recent years, with application of 3S technology, management and use of cultivated land resources have become more reasonable. For example, Zhang Haitao and others, by using GIS, introduced hierarchical analysis principle and method into evaluation on cultivated land fertility, for determining weight of involved factors, quickly and accurately completing comprehensive evaluation on natural fertility of cultivated land in Houhu Area in Jianghan Plain [2]; Zhou Hongyi and others, with SORER database as the foundation, made evaluation on fertility class of 53 cultivated land units in the typical area (Pengzhou) in upstream Yangtz River [3]; Wang Ruiyan and others took Qingzhou, Shandong Province as the test area and comprehensively used hierarchical cluster method, hierarchical analysis process, fuzzy evaluation method and mathematical model, realizing automatic and quantitative evaluation on cultivated land fertility[4]; Lin Bishan and others took soil species as the unit and adopted limiting factors method and the inductive method to made systematic analysis and appraisal of cultivated land fertility factors and evaluation on cultivated land (soil species ) Ilk force class [5]; He Yurong and others used soil quality coefficient to make evaluation on cultivated land fertility of Chuanjiang River Basin and several typical ecological agricultural zones in the surrounding area, to direct ecological environment construction and agricultural structure adjustment [6]; Liu Youzhao and others, taking Pizhou City, Jiangsu Province as the study-covered area and under support of GIS, made research of evaluation on cultivated land fertility, realizing automatic cultivated land classification and improving results’ scientificalness [7]. Therefore, the scientific research and practice using GIS technology to carry out cultivated land fertility survey and quality evaluation, on the one hand, can prepare information reserve for establishment of China’s soil fertility information system and precision agriculture system, and help realize global soil information exchange and sharing; on the other hand, will be of great theoretical and practice significance in sounding out about what China has got in soil resources, reasonably utilizing and scientifically administrating land resources, promoting sustained, steady and concerted development of China in population, resources, environment, society and economy[8]. This paper mainly takes State Farm No.22, Agricultural Division No.2 as the example to analyze its cultivated land fertility situations, so as to make contributions to high quality and high yield of grain and economic crops in the Regiment.

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2 General Description of the Study-Covered Area The State Farm No.22 in Yanqi, Ba-Prefecture, Xinjiang, located at the hinterland of Eurasian Continent and southern foot of Tianshan Mountains, with Kaidu River passing through the center and geographical position 86°30′E and 42°09′N, enjoys typical continental North Temperate Zone cold climate, with annual accumulated temperature of 3379 , annual average sunshine of 2973hours, annual precipitation of 93.7mm, annual average temperature of 7.9 , extremely highest temperature of 37.7 , extremely lowest temperature of minus 26.5 ; accumulated temperature≥10 of 3353 , frost-free period of 160~180. The Regiment neighbors Bosteng Lake in the east, is encircled by mountains in the south, borders Hular Mountain in the west and is against Tianshan Mountains in the north, making a typical basin area. The Regiment has a square topography, higher in the north and lower in the south, with slope of l/1000 and altitude of 1057m. The Regiment has land area of 0.0668 million hm2. Of which cultivated land area is 9530 hm2; mountain meadow area is 50, 000 hm2; forest and fruits area 2467 hm2; water area 3133.33 hm2; and town-administrated area 3240 hm2. In the Regiment soil is fertile and mainly consists of Quaternary-System sand loam soil, moisture soil, meadow soil, clay, salty soil and humult, etc, belonging to the ancient- past shallow marsh zone of Kaidu River and ancient Bosteng Lake delta. In terms of texture, the soil has sandy loam as the primary part and light loam as the secondary; moderate grain size, organic matter content average of 1.85%, total nitrogen of 0.11%, hydrolyzable nitrogen of 76mg/kg, rapid available phosphorus of 63mg/kg, rapid available potassium of 178mg/kg and PH of 7.7~8.1, suitable for planting wheat, tomato, pepper, beet, chrysanthemum, oil sunflower, confectionery sunflower, Black Seed Melon and maize, etc as agronomic crop.

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3 Data and Research Method 3.1 Data Sources Collecting Data. Social and economic statistical data, including social and economic index data of population and land area on the basis of basic unit of administrative division; basic and thematic maps data, including relief map, administrative division map, current land utilization map and soil map, etc. Field Survey Data. Including topography, landforms, soil parent material, soil layer thickness, cultivation layer texture, current utilization of cultivated land and irrigation and drainage conditions, etc. Soil Laboratory Analysis Data. According to actual situations of the study-covered area, we decide to arrange points for sampling on the basis of strip field and by the same density (interval). And we give full considerations to utilization of factors of current situations, type of soil species and fertility level, and concurrent consideration to uniformity. Our sampling works are mainly carried out in autumn, with some samplings in summer. On the basis of indoor point arrangement, we select a representative plot in the field, position it with GPS positioner, and adopt “S” method, “X” method or chessboard method to randomly specify 10~15 sampling points. Each

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sampling point’s earth borrow depth and sampling volume should be even and identical, with sampling depth of 0~20cm. After full mixing of soil samples, reserve sample by quartering, with 1.5kg for key point and 1kg for common point. In the State Farm No.22 total 917 soil samples have been collected. Soil sample analysis and determination items consist of: organic matter, alkalihydrolyzable nitrogen, rapid available phosphorus, rapid available potassium, effective zinc, effective iron, effective manganese, effective copper, salt content and pH value. Analysis method is subject to the Technical Regulations for National Cultivated Land Fertility Survey and Quality Evaluation. 3.2 Cultivated Land Fertility Evaluation Method This paper, by using geographical information system software ArcGIS and establishing the basic database for State Farm No.22’s cultivated land fertility evaluation, comprehensively analyzes factors influencing the State Farm No.22’s cultivated land fertility, so as to establish the State Farm No.22’s cultivated land fertility evaluation system and model. Determination of Evaluation Unit. Evaluation unit is the basic space unit for carrying out cultivated land class appraisal and division. The unit has uniform internal quality but big difference between units. Unit division needs comprehensive considerations of natural factors and social and economic factors. According to specific situations of the study-covered area, all sampling points in the State Farm No.22 are divided into evaluation units by strip field. Determination of Evaluation Index and Its Weight. The substance of cultivated land fertility evaluation is the evaluation on how natural elements such as topography and soil limit growth of local major agronomic crops. Cultivated land fertility evaluation factors consist of climate, topography, soil, vegetation, hydrologic and hydrogeological factors and social and economic factors. Many factors can influence cultivated land quality, involving various aspects of natural ecological conditions and social, economic and regional conditions. Our research, on the principle of stability, leading, comprehensiveness, difference, quantitation and reality, by referring to the index system of the “National Cultivated Land Fertility Survey and Quality Evaluation” by the Ministry of Agriculture, considering characteristics of the cultivated land resources in Agricultural Division No.2 and following the principle of availability, difference, importance, stability and combination of qualitative and quantitative indexes, selects five standard levels (nutrient of cultivation layer, cultivation layer’s physical and chemical properties, soil management, climate conditions and obstacle factor), and 13 indexes (texture, PH, organic matter, alkali-hydrolyzable nitrogen, rapid available phosphorus, rapid available potassium, effective zinc, effective copper, effective iron, effective manganese, accumulated temperature, irrigation guarantee rate and salt content) from the national cultivated land fertility survey and quality evaluation index system, to establish Agricultural Division No.2’s cultivated land fertility evaluation index system, which is arranged in manner of target level (A), standard level (B) and index level (C) (See Table 1). Our research is carried out according to actual situations of the cultivated land soil in Agricultural Division No.2 and on the basis of opinions from relevant experts and

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technicians with practice experience. And weight of every factor is determined as shown in Table 1 according to actual situations of cultivated land soil in the Division and to influence of various factors, and by using hierarchical analysis process and fuzzy comprehensive evaluation process (See Table 1). Table 1. Cultivated Land Fertility Evaluation Factor and Weight of the State Farm No.22, Agricultural Division No.2 Target Level A Standard Level B

Cultivated Land Fertility Index Level C

Single Factor Weight

Nutrient of cultivation layer Cultivation properties Soil

layer’s

physical

and

chemical

0.1732

management

0.1681

Climate conditions

0.1432

Obstacle factor Nutrient of cultivation layer

Cultivation properties

layer’s

Soil management Climate conditions Obstacle factor

physical

Combined Weight

0.3819

0.1336 0.1788 0.2124

0.0683 0.0811

Rapid available phosphorus Rapid available potassium

0.1584

0.0605

0.1057

0.0404

Effective copper

0.0783

0.0299

Effective iron Effective zinc Effective manganese

0.0915 0.0932 0.0817

0.0349 0.0356 0.0312

PH value

0.4132

0.0716

Texture Irrigation guarantee rate Accumulated temperature

0.5868 1.0000 1.0000

0.1016 0.1681 0.1432

1.0000

0.1336

Organic matter Alkalihydr-olyzable nitrogen

and

chemical

Cultivation content

layer’s

salt

Quantizing Treatment of Evaluation Index. According to land fertility and geological survey in Xinjiang, combining specific experiment and expert judgment, we process the quantitative data through combination of fuzzy comprehensive evaluation process and membership function to determine membership function of various evaluation indexes, and introduce evaluation factor value into the membership function, so as to calculate its membership value. For organic matter, alkali-hydrolyzable nitrogen, rapid available phosphorus and rapid available potassium, etc, Type S membership function is established for qualitative index, the method of grading by multiple experts but taking value on average is adopted. Quantizing treatment of concept factors. Concept factors such as soil texture, irrigation guarantee rate and accumulated temperature, etc are valuated as follows by adopting experts’ grading according to their influence on fertility, namely taking their characteristic value as the membership function (See Table 2.). Membership function of quantitative index. By using others’ research achievements [3], and through induction, feedback, gradual shrinkage and concentration, membership function of the following indexes is established.

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Table 2. Membership Function of Concept Evaluation Index Accumulated temperature membership Irrigation guarantee rate membership Soil texture membership

2000ć

2000~2200ć

2200 ~2400ć

>2400ć

0.21

0.45

0.65

0.85

Fully met

Basically met

Generally met

Not met

1

0.8

0.7

0.4

loam

clay

sandy loam

sandy soil

0.9

0.7

0.5

0.3

①

Determining Type S membership function of organic matter, alkali-hydrolyzable nitrogen, rapid available phosphorus and rapid available potassium membership:

f(x)=

1.0 0.9(x-x1)/(x2-x1)+0.1

x≥x2 x1≤x<X2

0.1

(1)

x˚x1

②

Determining Type S membership function of cultivation layer salt content membership:

f(x)=

1.0 0.9(x-x1)/(x2-x1)+0.1

x≤x1 x1<x≤x2

0.1

(2)

x˚x2

③

Determining Type S membership function of effective copper, effective iron, effective zinc, effective manganese membership:

f(x) =

1.0 (x-x1)/(x2-x1)

x≥x2 x1≤x<x2

0

(3)

x<x1

Break for value-taking of factors is shown as Table 3. Table 3. Value Taking of Type S Membership Function’s Curve Break Break Organic matter % Alkalihydrolyzable nitrogen mg/kg Rapid available phosphorus mg/kg

X1 0.5 30 5

X2 2.5 150 25

Rapid available potassium mg/kg

50

200

Salt content % Effective copper mg/kg Effective iron mg/kg Effective zinc mg/kg Effective manganese mg/kg

0.3 0.5 2.5 0.5 1

2 5 5 5 30

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④ Determination of pH membership

The corresponding membership function for pH value, etc in soil is a med-type (trapezoid) membership function. The closer the index to certain range, the better the quality of the evaluation object; the farther the index from the range, the poorer the quality of the evaluation object.

(x-3.5)/3 f(x)= (9.5-x)/2 1

3.5<x<6.5 7.5<x<9 . 6.5≤ x≤7.5

0

(4)

x≤3.5 or X≥9.5

Comprehensive Evaluation on Cultivated Land Fertility. Using weighted summation formula to calculate IFI(Integrated Fertility Index) reflecting cultivated land fertility situations. The formula is: (5)

IFI=∑Wi×Ni.

Where, Ni and Wi respectively mean membership value and weight coefficient of No. i fertility index.

4 Evaluation Results and the Analysis 4.1 Quantity of Cultivated Land of Various Classes According to the above-mentioned cultivated land fertility evaluation method to calculate IFI of cultivated land fertility in State Farm No.22, Agricultural Division No.2. Using equidistance method to divide IFI into six classes (See Table 4.) ; calculate area of plot at various levels and the proportion (Table 5.). From Table 5. it can be found that the cultivated land in State Farm No.22’s 7486.67 hm2 basic farmland can be divided into six classes, of which Class 1 Land has IFI(integrated fertility) of 0.74~1.00 and area of 378.11 hm2, accounting for Table 4. Division of Agricultural Division No.2’s Cultivated Land Fertility IFI Class

0.74~ 1.00 Class 1

0.68~ 0.73 Class 2

0.63~ 0.67 Class 3

0.57~ 0.62 Class 4

0.51~ 0.56 Class 5

0~ 0.51 Class 6

Table 5. Class of Cultivated Land Fertility in State Farm No.22, Agricultural Division No.2 Unit State Farm No.22

Fertility Class Class 1 Land Class 2 Land Class 3 Land Class 4 Land Class 5 Land Class 6 Land

Area (hm2) 378.11 2566.03 998.71 1836.09 1587.08 119.21

Proportion % 7.67 36.45 43.47 12.32 0.089 0.001

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7.67% of the total area ; Class 2 Land has IFI of 0.68~0.73 and area of 2566.03 hm2, accounting for 36.45%; Class 3 Land has IFI of 0.63~0.67 and area of 998.71 hm2, accounting for 43.47%; Class 4 Land has IFI of 0.57~0.62 and area of 1836.09 hm2, accounting for 12.32% ; Class 5 Land has IFI of 0.51~0.56 and area of 1587.08 hm2, accounting for 0.089%; Class 6 Land has IFI of 0~0.51 and area of 119.21 hm2, accounting for 0.0001%. 4.2 Space Distribution of Cultivated Land at Various Levels With support of GIS software ArcGIS 9.2, on the basis of evaluation unit map and according to cultivated land fertility evaluation of the units, same-class and neighboring units are merged to get polygon of cultivated land fertility classes (See Fig. 1). In State Farm No.22, Agricultural Division No.2, Class 1 Land, within 10% of the total, is mainly distributed in Company 3, Company 6, Company 7, Company 8 and Company 9. The Class 1 Land belongs to high/stable-yield field, characterized by highest fertility, flat land, assured irrigation andconcerted water and air, where high yield is common for crops. In the Class 1 Land, soil has good arability, long cultivation period, good permeability and irrigation and quick fertilizer effect. However, attentions should be paid to application of more organic fertilizer and phosphorus/potassium fertilizers to prevent fertility from decreasing.

Fig. 1. Cultivated Land Fertility Class of State Farm No.22, Agricultural Division No.2

Class 2 Land accounts for about 35% of the total, and is distributed in all companies, with the most in Company 1 and the least in Company 2; Class 2 Land boasts higher content of alkali-hydrolyzable nitrogen, rapid available phosphorus, effective

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iron, effective manganese and effective copper, but lower content of organic matter and effective zinc that average of other classes, and soil mainly consisting of loam and sandy loam. In the project area Class 2 Land is characterized by high fertility, flat land, basically-matched irrigation system, concerted water and air, good arability and high nutrient content. Class 3 Land accounts for up to 43.47%, the highest proportion, and is distributed evenly, with the most in Company 1 and the least in Company 10. Class 3 Land has high content of organic matter and rapid available potassium, alkali-hydrolyzable nitrogen, low content of rapid available phosphorus, and soil mainly consisting of sandy loam and little loam. In the area utilization of soil under Class 3 Land is limited to certain extent, soil’s physical property is poor, and irrigation conditions are common, making crop subject to selection. In production, some improvement tests are needed to guarantee yield. The soil here has nutrient under med. level or short partially, needing more organic fertilizer and deepened mature cultivation layer to improve overall growth in soil. Class 4 Land is mainly distributed in Company 12, Company 13, Company 14 and Company 15. Class 4 Land boasts high organic matter and effective zinc content but low effective iron content, and soil mainly consists of sandy loam. Class 4 Land in the project area is more selective to agronomic crop and poor in soil quality and has considerable part of low-yield field as obstacle level. Class 4 Land has soil characterized by short arable period, hard control, poor overall quality (influencing germination), poor permeability, physical performance and ability against floodwater and drought. Proportion of Class 5 Land is slightly higher than that of Class 6 Land, very little compared with other classes and only in Company 14. Class 5 Land has average content of rapid available phosphorus and effective zinc the highest, and of other factors the lowest, compared with the average in other classes respectively. Its soil consists of sandy soil and sandy loam. Class 5 Land mainly belongs to low-yield field, basically has obstacle level and is badly limited for agricultural production and more selective to agronomic crop. The soil is poor in quality, fertility, physical performance and permeability. Soil in Class 5 Land should be applied with more organic fertilizer and supported with phosphorus potassium fertilizer. There is only a plot of Class 6 Land. Class 6 Land is characterized by obviously lower organic matter content and lower content of nutrient factors than those in land of other classes. Its soil mainly consists of sandy soil. Class 6 Land basically belongs to low-yield field, and the soil has poor fertility and physical performance. Yield of crop from the land is very low. Overall content of nutrients is not low, but difference of nutrients is higher, meaning uneven distribution of nutrients. Meanwhile, main limiting factor of Class 6 Land lies in the soil’s site conditions and obstacle factor, and the Land needs long-term comprehensive treatment and improvement.

5 Conclusion (1) From the above-mentioned researches it can be found that using GIS technology can quickly and effectively make quantitative and scientific evaluation on cultivated land fertility and the space distribution, saving time, work and effort compared with traditional evaluation method.

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(2) Application of hierarchical analysis process and fuzzy comprehensive evaluation method in cultivated land fertility evaluation can to certain extent reduce influence of subjective factors from evaluator, better reflect fertility difference of cultivated land and improve precision of cultivated land fertility evaluation results. (3) Using cultivated land plot as evaluation unit means clear space boundary and relationship of administrative subordination, accurate area, identical landforms type and soil type, and basically same utilization mode and cultivation method, making evaluation results more comprehensive and objective and implemented more directly in practice. (4) The results of evaluation on the basis of cultivated land fertility index of State Farm No.22, Agricultural Division No.2 are in line with actual conditions very much, and have very high guiding significance and practical value. Acknowledgments. Funding for this research was provided by the Social Science Fund program. The first authors are grateful to Dr. Xin Lv for his insightful comments on this manuscript. We wish to thank Dr. Hailong Liu for his help in organizing the data. The authors also appreciate other members of the Research on the Social Science Fund Project for their assistance.

References 1. Lu, M., He, L., Wu, S.: GIS-based Research of Evaluation on Cultivated Land Fertility in Central China Hilly Area. Transactions of the Chinese Society of Agricultural Engineering 22(8), 96–101 (2006) (in Chinese) 2. Zhang, H., Zhou, Y., Wang, S.: Comprehensive Evaluation on Natural Fertility of Cultivated Land in Houhu Area, Jianghan Plain by Using GIS, RS Data and Hierarchical Analysis Process. Transactions of the Chinese Society of Agricultural Engineering 19(2), 219–223 (2003) (in Chinese) 3. Wang, R., Zhao, G., Li, T.: GIS-supported Evaluation on Cultivated Land Fertility Class. Transactions of the Chinese Society of Agricultural Engineering 20(1), 307–310 (2004) (in Chinese) 4. Lin, B., Tang, J., Zhang, M.: Research and Evaluation on Cultivated Land Fertility Class in Guangdong. Ecological Environment 14(1), 145–149 (2005) (in Chinese) 5. He, Y., Zhou, H., Zhang, B.: Cultivated Land Fertility and Agricultural Structure Adjustment in Typical Area of Upstream Yangtz River – Taking Chuanjiang River Basin and its Surrounding Area as the Example. Journal of Soil and Water Conservation 17(3), 86–88, 92 (2003) (in Chinese) 6. Bi, R., Wang, B., Wang, J.: Establishment and Application of MAPGIS-based Cultivated Land Fertility Evaluation System. Journal of Shanxi Agricultural University 25(2), 97–101 (2005) (in Chinese) 7. Liu, Y., Wang, J., Liu, J.: Research of County-level Cultivated Land Classification und er Support of Geographical Information System. Journal of Nanjing Agricultural University 24(3), 106–110 (2001) (in Chinese) 8. Niu, Y., Xu, H., Qin, S.: Research of GIS-supported Cultivated Land Fertility Evaluation Method. Journal of Hebei Agricultural University 27(3), 85–88 (2004) (in Chinese)

Research of Pest Diagnosis System Development Tools Based on Binary Tree Yun Qiu and Guomin Zhou Agricultural Information Institute of CAAS, Beijing 100081 [email protected]

Abstract. A visual pests diagnosis system development tools based on binary tree (abbreviation: knowledge system development tools) was developed by techniques of computer visualization, binary tree, XML database and reasoning. This tool combined tree knowledge expression with rule-based reasoning machine, proposed the knowledge acquisition and the reasoning technique based on binary tree, and solved the problem of knowledge acquisition used for pests diagnosis with an expert system development tools--CLIPS. The reasoning machine with stateless continuous reasoning technology can improve the efficiency of knowledge acquisition and system reasoning. In this article, we introduced the components and working principles of the pest diagnosis system development tools, and described its usage with a simple example. Keywords: pest diagnosis; binary tree; CLIPS system; knowledge system; knowledge expression.

1 Introduction Pest is one of the most terrible threats to crops. The statistics shows that there are 236 million hectares fields are threatened by pests and about 15 percents food are lost in our country every year. An effective measure to reduce crops lost and improve its quality is to get accurate information in time by pest diagnosis and take proper measures in the growth period. Taxa Key and Classification Tree which can be combined into a binary tree key by optimization analysis (shown in Figure1) are often used to summarize the experience of pest diagnosis by plant protection experts. The system based on Binary Tree Key usually includes a series of questions (named “decision node”), the decision makers will answer each questions in sequence till the terminal object (named “answer node or leaf node”) is identified. The development process of expert system which can be expressed by binary tree is shown in Fig.2. As shown above, program compiling is a complicated work must be done by professional training engineers. Can we develop a tool to package these difficult programs and make program compiling automatically? The answer is positive. A kind of visual pests diagnosis system development tools based on binary tree can function properly. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 300–308, 2011. © IFIP International Federation for Information Processing 2011

Research of Pest Diagnosis System Development Tools Based on Binary Tree

decision node

data for deciding

data for deciding

decision

decision node

answer

answer

node

node

node

decision node

decision node

decision node

………

………

……………

answer

301

data for deciding

Fig. 1. Structure of Binary Tree Key

generate the decision tree by consultation

compile program to set the knowledge base

compile program for reasoning and output the results Fig. 2. The development process of expert system

2 Materials and Methods 2.1 Introduction of CLIPS System “CLIPS” is the abbreviation of “C Language Integrated Production System”. It is a rule-based general expert system development tools developed by National Aeronautics and Space Administration (NASA) in 1985. As a knowledge engineering language, CLIPS inherits its traditional writing habits and operating characteristics, and has the basic characteristics of production systems. After deeper development by NASA,

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CLIPS has several knowledge expression methods and fuzzy reasoning function. The knowledge expression methods include the production system (rule-based), the framework structure, the object-oriented and process programming. The fuzzy reasoning function may improve the accuracy of reasoning results. In addition, CLIPS system has a good openness that can integrate with other systems easily. A dynamic linked functions library-- clips.dll is provided in the software package of CLIPS development environment. The mixed programming can be completed by calling functions in clips.dll. For example, we can embed calculation unit into CLIPS to improve its poor calculate function as well as embed CLIPS into calculation unit to improve its poor logical reasoning. There are three basic elements used for expert system development is provided by CLIPS. (1) fact-list and instance-list. They usually consist of some self-defined facts and include the data needed for reasoning. (2) knowledge-base. It includes all rules that usually formed by self-defined. (3) inference-engine. It reasons according to reasoning mechanisms and control procedures overall. The fact in fact-lists is the basic form of the data in CLIPS system. Each fact is information in fact-lists. Weather the rule condition is satisfied or not depends on the state fact. A fact is made up with a series of domains separated by spaces. The fact can be inserted or deleted from fact-list before reasoning, and it also can be inserted and deleted as an act section of rule during reasoning. Forward reasoning is the only rule adopted in CLIPS, it forms in “IF …THEN…”. Its implementation technology is the famous Rete Algorithm, also named net-matching algorithm. To run the program of expert system is to fire and run each “IF… THEN …” rule defined in program till getting the specific conclusion. The necessary condition to fire the rule is each pattern in “IF” sentence matched with the facts in fact-list. It’s called be-activated rule. Be-activated rules may not fire immediately, thus a waiting agenda is required. The waiting agenda will fire the rule with the highest priority and run the action in “THEN” sentence. As thus, CLIPS includes four parts shown in Fig.3.

fact-list

Reasoning machine

rule-list

waiting agenda Fig. 3. The structure of CLIPS

2.2 Components of Development Tools Knowledge System Development tools is composed of two sections. One is knowledge editor functions for knowledge editing and uploading. Another is knowledge reasoning

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components and knowledge service webpage, they plays a role in receiving input information, calling knowledge from knowledge base to reason, and returning results to users by webpage. There are three main functions with knowledge system development tools: (1) visual knowledge editing function which can edit the knowledge in books and experts’ mind; (2) easy knowledge publishing function which can output knowledge to Internet by knowledge reasoning components and corresponding ASP script without compiling program; (3) flexible webpage customizing function which can customize webpage according to requirements to develop some personalized knowledge system.

Fig. 4. The structure of development tools

2.3 Working Principles of Development Tools Knowledge expression is very important in the knowledge system. The rule-based knowledge expression method of CLIPS is used in this development tools. Each node is a fact. The information style in answer nodes and decision nodes is different, so the different models are needed. The model of answer nodes is expressed as: node answer ). In this model, is the name of answer node, and is the answer in storage nodes. The model of decision nodes is expressed as: (node decision

<no-node>). In this model, is the name of decision node, and is the question proposed by this node, and <no-node> are the nodes performed respectively for positive and negative answers. An example of knowledge expression is shown below:

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(deftemplate rule

(defrule goal-satified ""

(multislot if)

(declare (salience 30))

(multislot then))

?f <- (goal is ?goal)

(defrule propagate-goal ""

(variable ?goal ?value)

(goal is ?goal)

(answer ? ?text ?goal)

(rule (if ?variable $?)

=>

(then ?goal ? ?value))

(retract ?f)

=>

(format t

(assert (goal is ?variable)))

"%s%s%n" ?text ?value)) ……………

However, the knowledge expression method above is difficult for common users. In order to solve this problem a visual editing method is chosen, so the common users can express knowledge by some simple icons. Users can express knowledge by icons. After finished knowledge editing, the development tools will translate knowledge expressed by icons into knowledge expressed by rules. The work principle of development tools is show in Fig.5.

Fig. 5. The work principles of development tools

After being edited and translated, knowledge is formed into rule-based ones and stored in knowledge base of web server. The reasoning machine implemented with COM+ technology of Microsoft can call the knowledge stored in knowledge base. The user interface is realized by ASP script. (Fig.6)

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Fig. 6. The logic structure of knowledge system

In fact, it isn’t as complicated as the structure shown in Fig.6. The development tool has packaged some main functions into COM and provides a standard ASP script. Therefore, users can run the system by the program sentence as “ks run.asp?kdb = name of knowledge base & title =name of system ” without compiling complicated program.

3 Results and Analysis 3.1 An Example of Pest Diagnosis Suppose that we will develop a networked pest diagnosis system named “pest diagnosis experiment system”, the development process is shown in Fig.7.

knowledge management knowledge input and revise by editor upload knowledge to server test satisfy?

end Fig. 7. The development process of knowledge system

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According to the process above, knowledge management is the first step. Let’s suppose that there is only one pest diagnosis knowledge information in the system, the decision rule is: if it occurs under cotton root then the pest is cutworm, else the pest is bollworm. As shown in Fig.8.

find where is the pests occurs above cotton root

occurs under cotton root cutworm

bollworm Fig. 8. Chart of pest diagnosis

In this case, we can resort to knowledge system development tools. The pest diagnosis chart can be generated in the knowledge editing area with the icons in object toolbox, such as the decision node the connecting line

, the answer node

and

. The pest diagnosis char in Fig.9 is similar to Fig.8.

Fig. 9. The picture of knowledge system development tools

The decision information provided to users to find the sites of pests in Fig.8 can be inputted and edited with knowledge system development tools. Clicking the decision node in Fig.9 to open its interface editor, and then input the decision information for users to choose.

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Fig. 10. The node edit picture of knowledge system development tools

After editing the nodes and their interface information, saving the knowledge in server with the icon of (net write) and named “test”. And then generate the rule-based information base that can be recognized by reasoning machine with the icon (knowledge base). Lastly, the knowledge base for “pest diagnosis experiof ment system” is founded and transmitted to the server. The webpage of knowledge service system is shown in Fig.10 after inputting the address of “http://127.0.0.1/ks/webapp/ks_run.asp?kdb=test&title= pest diagnosis experiment system” in URL of the browser. Click the button of “next” to reason. As thus, the pest diagnosis experiment system is finished.

Fig. 11. The system of knowledge service system

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3.2 Result for Application With the knowledge system development tools, we have developed several knowledge systems successfully, such as “the rice diseases diagnose system”, “the rice pests diagnose system”, “the cotton diseases and pests diagnose system”, “the apple tree diseases and pests diagnose system”, and so on. The application shows that the user-friendly interface makes the development tools easy to operate. The speed for knowledge generation using this knowledge system is about 35% faster than traditional way and the accuracy is also increased. Thus, experts of pants protection could generate a pests diagnose system knowledge system based network after simple training. This system is feasible and has a high practical value.

4 Conclusion and Discussion In this passage, we develop the visual pests diagnosis system development tools based on binary tree using techniques of computer visualization, binary tree, XML database and reasoning. This tool combined tree knowledge expression with rule-based reasoning machine, proposed the knowledge acquisition and the reasoning technique based on binary tree, and solved the problem of knowledge acquisition used for pests diagnosis with an expert system development tools--CLIPS. The reasoning machine with stateless continuous reasoning technology can improve the efficiency of knowledge acquisition and system reasoning. Application results show that the speed and accuracy of this system are greater than the traditional way. Thus, the development cycle of diseases and pests diagnose system is shortened, and promoted the information technology to be widely used in agriculture. Acknowledgements. Our research is funded by the “863” project of research and application of field crop diseases intelligent diagnosis system (Number: 2007AA10Z237).

References 1. Liu, Y.-t.: The design and realization of the front-end system of crop plant diseases and insect pests ground survey. Agriculture Network Information (7) (2006) 2. Wu, H.-l.: The introduction and application of Clips–an expert system tool. Beijing Institute of Technology Publishing House, Beijing (1991) 3. Wu, H.-l.: The introduction and application of Clips—a new expert system tool 5(3) (1992) 4. Shen, A.-h.: Research and application of forward and backward reasoning based on binary taxonomic key. Journal of Zhejiang University (Agriculture and Life Sciences) 32(5) (2006) 5. Ming, B.: Design and Realization of the Maize Disease Diagnosis System Based on Image Rules. Journal of Maize Sciences 17(6) (2009) 6. Yang, J.-h.: Expert System of Diagnosis and Controlling for Maize Disease, Pest and Weeds in Province. Master Thesis of Jilin Agricultural University (2006) 7. Brown, M.P.S., Grundy, W.N., Lin, D.: Knowledge-based analysis of microarray gene expression data by using support vector machines. PNAS 97(1), 262–267 (2000) 8. Horowitz, E., Zorat, A.: The Binary Tree as an Interconnection Network: Applications to Multiprocessor Systems and VLSI. IEEE Transactions on Computers C-30(4), 247–253 (1981)

Research of Soil Moisture Content Forecast Model Based on Genetic Algorithm BP Neural Network Caojun Huang, Lin Li, Souhua Ren, and Zhisheng Zhou College of Information and Technology of Heilongjiang Bayi Agricultural University, Daqing 163319, China

Abstract. Soil moisture forecast model based on genetic neural network is established because the soil moisture forecasting is nonlinear and complex. The weights and threshold value of BP network are optimized according to the total situation optimization ability of genetic algorithm, which can avoid effectively that BP network is vulnerable to run into the local minimum value as its poor total optimization ability. The model is applied to Hongxing farm in Heilongjiang Province to predict the soil moisture. The forecasting result shows that the model has favorable forecasting precision, which indicates that the genetic neural network model is feasible and effective to predict the soil moisture. Keywords: GA-BP, soil in the field, moisture forecasting, modeling, forecast precision.

1 Introduction With the increasing development of industrialization, the human environment and the global extreme climate deteriorates more and more, the drought and flood disaster happen continually. All of these restrict the agricultural production and the healthy development of social economy badly. To ensure food safety, fertility and get in timely, the accurate forecast information of soil moisture of farmland is needed to be achieved in time and exactly. Therefore, establishing the soil moisture prediction model is conducive to science management of farming and has important significance for the food fertility and stable grain. The different soil moisture prediction models were established by experts and scholars, such as: empirical formula of the model, the water balance model, the dynamic model of soil water, time series model, remote sensing model and neural network model. These models are always restricted in the practical application [1-6]. For example, although the empirical model is simple and useful, the model parameters have limited applied scope. Other models such as the water balance model, the dynamic model of soil water, time series model etc. have better suitability, but they need a mass of measurement data. The remote sensing model has poor stability. Neural network model get into the local minimum values easily, which makes the network cannot search the global optimal solution [7]. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 309–316, 2011. © IFIP International Federation for Information Processing 2011

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According to the complexity and the shortages of forecast of soil moisture model, a suitable algorithm for processing the non-linear problem and with global optimization ability of Genetic Algorithm (GA) is used. It optimizes neural network coefficients and threshold and form a hybrid GA-BP Algorithm, which has the practical value of the soil moisture prediction model of the soil moisture forecast.

2 Genetic BP Network Model The genetic BP network utilizes the overall situation optimizing ability of genetic algorithm to search optimization for weights and threshold value of BP network within large scope, then the optimum solution is endued to the weights and threshold value of BP network, finally BP network is used to search precisely to obtain the optimal weight and threshold value [8]. The specific implementation process is as follows [9-10]: 2.1 Population Initialization and Coding Population initialization is used to refers to initialize the BP network's weights and threshold. According to population design experience, the population A with M inT dividual is produced, A = {X 1 , X 2 , X 3 , ", X m } , individual X i = {x1 , x2 , x3 , " , xm } M genes in X i = (x1 , x 2 , x3 , " , x m ) are composed of BP network's weights and threshold. The length computation is as follows:

m = r ⋅ S1 + S 1 ⋅ S 2 + S1 + S 2

(1)

Where r is the input node numbers, S1 is the hidden nodes, and S 2 is the output layer nodes. As the soil moisture prediction is more complicated, the real number coding scheme is chosen, which can effectively avoid weight step change. Next, with real coding method, the vector of three-layers BP networks weights and threshold is:

Xi=[ω11，"， ω1S1，"， ω1S1*r， ω21，"， ω2S2，"， ω2S2 *S1， B11，"， B1S1， B21，"， B2S2 ]

(2)

Where ω1 is the weights value between input and hidden layer, ω 2 is the weights value between output and hidden layer, B1 is the hidden layer's threshold value, and B2 is the output layer's threshold value. 2.2 Fitness Function

The fitness function is used to evaluate individual. Each individual is evaluated according to the fitness function value, then BP neural network input sample is obtained by decoding corresponding individual. Finally, neural network output error is calculated, the fitness function is used: 1 (3) F(i) = E Where E is BP network output error, its computation formula is:

Research of Soil Moisture Content Forecast Model

E=

1 2

∑(y −Y )

311

2

b

(4)

Where y is network actual output, Yh is network standard output. 2.3 Choose Operation

According to the calculation results of fitness, the highest fitness groups are retained. High fitness individuals are selected to pass down offspring directly. The other individuals are chosen by roulette selection method, so the small fitness of individuals also have the opportunity to pass down offspring. This ensures the diversity of the individuals in the group, and avoids the local optimum. Select formula is as follows: F (i ) Pis = M (5) F (i )

∑ i =1

Where Pis is probability for selection: M is the population size: i is the first i individual in group M . 2.4 Crossover Operation

Since the weight coefficients are obtained by using real number coding, arithmetic crossover is used. If Xτa and X bτ are two crossover individuals, then the new individuals are generated after operation:

X aτ +1 = αX bτ + (1 − α ) X τa

(6)

X bτ +1 = αX τa + (1 − α ) X bτ

(7)

Where α is linear combination coefficient, and usually α ∈ (0,1) . 2.5 Mutation Operator

The non-uniform mutation operation can improve the algorithm accuracy, which makes the final search closer to the optimal solution. So, non-uniform mutation is selected. Mutation operating is as follows: x m is variation point of variance X = x1 x2 x3 " xm " xn , xm ∈ [a, b] , the new genetic ~ x m of variation point can be obtained by:

，

t ⎧ (1− ) b T ⎪ ) ⎪ xm + (b − xm )(1 − r ~ xm = ⎨ t (1− )b ⎪ ⎪⎩ xm − ( xm − a)(1 − r T )

(8)

Where r ∈ [0,1] and it has the uniform probability distribution; t is the current evolution algebra, T is the termination algebra, b is shape parameters of the system.

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According to (8), search scope is large in the early stage of algorithm operation(when t is small).While it is small in the late stage(when t is close to T ) ,which is equivalent to local search. 2.6 Generate New Population

A new population will generates when new individuals are inserted into the primary populations, then the connection weights of individuals in the new population are given neural network, and the new individual fitness function is calculated, if it meets predefined criteria, then goes to the next step, otherwise continues to genetic operation. 2.7 Optimal Operation

The optimal individuals which have been decoded are endowed to neural network's weights and threshold value as the net's initial value. BP neural network is trained until the error square sum reach the specified accuracy, or reach the maximum iteration times. The flow chart of Genetic neural network algorithm is shown in Figure 1:

Fig. 1. Realizing flow chart of GA-BP

3 Implementation of Soil Moisture Forecast This research is carried out in Heilongjiang Hongxing farm, which is located in the northern of Heilongjiang province Songnen Plain and Xiaoxing'an Mountain zone,

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belongs to cool transitional continental monsoon climate. Location: latitude 48°02'~ 48°17', longitude 126 ° 47 '15 ° ~ 127. Annual average temperature is 0.8 , the average frost-free period is117 days, annual rainfall is about 553.0mm. The temperature is below 0 in five months every year. Hongxing farm has plenty of rain and heat than adequate, long sunshine hours, high soil organic matter content, strong water storage capacity, which provide more advantageous conditions for soybeans, wheat and other crops of high economic value.

℃

℃

3.1 Feature Extraction of Model Input and Output

The actual soil moisture data, under10cm, 20cm, 30cm, 40cm and 50cm from 2004 to 2009, is used in the model. The 10 factors effecting soil moisture remarkably, such as temperature, rainfall, evaporation, relative humidity and sunshine hours and other meteorological factors, are applied to the input of the model. The model output is soil moisture data. According to the training results, it can be known that single output factor has higher precision and faster convergence speed. Therefore, we select single soil moisture data as the model output. Research shows that with the crops growing, the main water area of rhizome has downward trend [11-14]. Therefore, we focus on this layer of the surface 10cm of soil moisture conditions in April and May, in June, layer of the surface 20cm, in July, August, September, layer of the surface 30cm. 3.2 Hidden Layer and Hidden Layer Nodes Determination

As the three-layer neural network can approximate any function, we use a single hidden layer BP network structure [15]. Based on the above analysis, there are ten input factors and one output factor. According to the calculation formula of the hidden nodes [16], the optimal hidden nodes number is 6 to 23. According to the training results, the optimal hidden nodes is 22. Therefore, the network topology is: 10-22-1. 3.3 Description of Training Samples

Firstly, the input and output sample data are normalized for network training [17]. Input samples and the corresponding output data are inputted to the network with the recurrence relations, thus completing the genetic neural network model training, modeling process, the input and output data table as follows: Table 1. Correspondence of the input and output data Input sample data

Output sample data

X 1 , X 2 , "" , X n X n+1 , X n + 2 , "", X n + n

Y1 , Y2 , "" , Yn Y

n

+

1

,

Y

n

+

2

,

"

"

,

Y

n

+

n

……

……

X kn +1 , X kn + 2 , "" , X kn + n

Ykn +1 , Ykn + 2 , "" , Ykn + n

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Where X1 to X n and Y1 to Yn are input and output sample data in the first year arranged by time serial. X kn +1 to X kn + n and Ykn +1 to Ykn + n homoplastically. The input sample data includes: 10 ~ 50cm of five levels of soil moisture data, temperature, rainfall, evaporation, relative humidity and sunshine duration, etc; output sample data is a certain level of soil moisture data According to its requirement by Heilongjiang province of soil moisture report, soil moisture data should be reported to Land Reclamation Bureau monthly from April to October each year on the 8th, the 18th, the 28th. To this end, each of the input sample data arranged in chronological order: April 8 sample data ( X1 ), April 18 sample data ( X 2 ), April 28 sample data( X 3 ), ... ..., October 18 sample data X n ; output sample data arranged in chronological order: April 18 sample data ( Y1 ), April 28 sample data ( Y2 ), May 8 sample data ( Y3 ), ... ..., October 28 sample data ( Yn ). 3.4 Forecast Implementation

The genetic neural network parameters are set up firstly, then the input and output data are put into the network for training. When the training result is in accordance with established standards, the training stop, the genetic neural network structure and parameters are saved. The trained neural network is used to forecast the soil moisture from April to October in 2009, and the forecast results are compared with BP neural network forecast model results, the detailed as follows: Table 2. Comparison of forecasted results by two models

Date (day/month) 18/4 28/4 8/5 18/5 28/5 8/6 18/6 28/6 8/7 18/7 28/7 8/8 18/8 28/8 8/9 18/9 28/9

Measured results (%) 34.98 29.15 17.49 22.79 23.64 33.74 35.52 42.62 40.25 45.51 49.37 43.99 30.54 35.22 38.14 36.62 34.87

GA-BP network model Forecasting Error results (%) (%) 36.253 1.273 29.775 0.625 17.363 0.127 23.711 0.921 24.672 1.032 30.381 3.359 37.585 2.065 37.128 5.492 44.842 4.592 47.498 1.988 50.62 1.25 42.98 1.01 32.409 1.869 32.587 2.633 39.48 1.34 37.356 0.736 32.51 2.36

BP network model Forecasting Error results (%) (%) 36.676 1.696 29.255 0.105 15.386 2.104 23.627 0.837 25.235 1.595 29.525 4.215 38.61 3.090 35.444 7.176 43.704 3.454 49.681 4.171 53.164 3.794 39.015 4.975 32.227 1.687 31.683 3.537 40.63 2.490 38.034 1.414 31.503 3.367

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It can be seen from the table that BP network prediction of soil moisture average absolute error is 2.92%; GA-BP network prediction of soil moisture average absolute error is 1.92%. Obviously, GA-BP network prediction accuracy increases one percentage point. The results show that the genetic algorithm of BP neural network has better mapping capabilities and better generalization ability comparing to the single BP neural network. It satisfies modeling requirements and is applicable to the soil moisture forecast.

4 Conclusion The soil moisture prediction model was established based on the combination of genetic algorithm and neural network. This model offsets the shortcomings that the neural network for global searching capability is weak and easily fallen into the local minimum. It also gives full play to the neural network nonlinear mapping capability. The model has been applied to Heilongjiang Hong Xing Farm on soil moisture prediction and has achieved good results. Compared to the BP neural network model, the genetic BP neural network model has better prediction accuracy and application value, and favorable application prospect in the soil moisture forecast.

References 1. Chen, H.: Research on Soil Moistrue Prediction Models of Inadequate Irrigation Rice Field in South China. Yangzhou University, Yangzhou (2004) 2. Mahmood, R., Hubbard, K.G.: An analysis of simulated long-term soil moisture date for three land uses under contrasting hydroclimatic conditions in the northern greet plains. Hydrometeoroloy 5, 160–179 (2004) 3. Zhou, L.: Study on Estimation of Soil-water Content by Using Soil-Water Dynamics Model. Water Saving Irrigation 3, 10–13 (2007) 4. Zhang, H., Yang, J., Fang, X., Fang, J., Feng, C., et al.: Application of Time Series Analysis in Soil Moisture Forecast. Research of Soil and -Water Conservation 15, 82–84 (2008) 5. loess soil are. J. Remote Sensing, 1 19(2), 237–243 (1998) 6. Ma, X.: Forecast of Soil Moisture Content during Critical Period of Spring Sowing Based on Precipitation in Last Autumn. Chinese Journal of Agrometeorology 29(1), 55–57 (2008) 7. Luo, C., Liu, Z., Wang, C., et al.: Optimized BP neural network classifier based on genetic algorithm for land cover classification using remotely-sensed data. Transactions of the CSAE 22(12), 133–137 (2006) 8. Zhou, S., Li, Z.: Genetic Algorithm for Optimization of Neural Network Structure and Weight Distribution. Ordnance Industry Automation 23(4), 48–49 (2004) 9. Zhi, J., Zhang, D., Jiang, P.: Based on PCA of genetic neural network prediction of stock index. Computer Engineering and Applications 45(26), 210–212 (2009) 10. Vittorio, M.: Genetic evolution of the topology and weight distribution of neural networks. IEEE Transactions on Neural Networks 5, 39–53 (1994) 11. Zhang, X., Yuan, X.: A Field Study on the Relationship of Soil Water Content and Water Uptake by Winter Wheat Root System. Acta Agriculturae Boreali-Sinica 10(4), 99–104 (1995)

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12. Zhang, Y.: Croproot system and Soil water application. China Meteorological Press, Beijing (1999) 13. Cao, M.: Principle of cropping system and soil management. China Agriculture Press, Beijing (2002) 14. Cui, L., Xu, B.: Experimental study of Soil moisture content in the Midwest of Jilin province. Jilin Water Resources 1, 14–15 (2003) 15. Hecht-Nielsen, R.: Counterpropagation Networks. Applied Optics 26, 4976–4984 (1987) 16. Paola, J.D., Schowengerdt, R.A.: A review and analysis of backpropagation neural networks for classification of remote-sensed multi-spectral imagery. International Journal of Remote Sensing 16(16), 3033–3058 (1995) 17. Wang, L., Zheng, D.: Two hybrid learning strategies of feedforward network. Journal of Tsinghua University (Science and Technology) 38(09), 95–97 (1998)

Research of the Measurement on Palmitic Acid in Edible Oils by Near-Infrared Spectroscopy Hui Li, Jingzhu Wu*, and Cuiling Liu College of Computer & Information Engineering, Beijing Technology and Business University, Fucheng Road 33, 100048 Beijing, P. R. China [email protected]

Abstract. A method for determination of palmitic acid in edible oils by the near-infrared spectroscopy was addressed in this paper. 56 samples were collected in the experiment. In terms of concentration content gradient method, 44 samples were selected for modeling set and 12 for testing set. This paper described the utilization of PLS for establishing a quantitative analysis model for predicting the content of palmitic acid in edible oils by near-infrared spectroscopy. The result shows that the model has a high accuracy for predicting the palmitic acid content with vector normalization and first-derivative preprocessing spectra with its best main factorial number of 8.The determination coefficients (R2), root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP) and average bias are 99.59, 0.162, 0.306 and 0.148, respectively. The model used in the paper can be adopted for the measurement of palmitic acid in edible oils accurately. Keywords: Near-infrared Spectroscopy, Palmitic Acid, Edible Oils, quantitative analysis.

1 Introduction The near-infrared reflectance is a kind of electromagnetic wave whose wavelength range is between 780nm and 2526nm. It is the earliest non visible light founded by people. The spectrum is produced by frequency doubling of vibration at fundamental and frequency composition absorbing in middle infrared. The compounds contain XH groups which make the analysis possible. Near Infrared spectroscopy is one of the most rapidly developing detection technology. It has the characteristics of fast, accurate, non-polluting and non-destructive. With the improving of hardware and software of near-infrared (NIR), the applying area of NIR must be enlarged rapidly. It is widely founded that the edible oils are often adulterated with some other inferior quality materials. A fast and accurate detection method to determine the content of the major constituent of edible oils is extremely urgent. Palmitic acid is an important index of the quality of edible oil. The content of it affects the quality of oils directly. For this reason, a method for the determination of palmitic acid in edible oils by the near infrared spectroscopy is addressed in this *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 317–321, 2011. © IFIP International Federation for Information Processing 2011

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paper. It also established a more accurate prediction model and thereby fulfills the fast detection of palmitic acid content.

2 Apparatus and Materials 2.1 Apparatus and Spectrum Determination Use MPA in the agricultural institute to collect spectrum and fiber optics probe to scan the samples. Path length is 2mm.The resolution is 8cm-1 and the number of scan times is 32. The range of spectrum is between 12500cm-1 and 4000cm-1. Software OPUS 6.5 is adopted to establish the analytic model. 2.2 Materials There are 56 edible oils which come from different brands and batches in the experiment including peanut oil, corn oil, palm oil, olive oil and soybean oil. In terms of concentration content gradient method, 44 samples are selected for modeling set and 12 for testing set in different palmitic concentration range. The actual palmitic content is measured by the national standard method. The relationship between the amount of modeling set samples and chemical value distribution range of palmitic is shown in Figure 1.

Fig. 1. Chemical value distribution range of palmitic

2.3 NIR Scanning All the samples are without any chemical treatment before experiment. Put the fiber optics into the bottles which content the oils and scan them in sequence. In order to avoid cross contamination, petroleum ether is used to clean the fiber optics before every scanning. The NIR spectrograms of samples are shown in Figure 2.

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Fig. 2. Near infrared spectrogram of edible oil samples

3 Results and Discussion 3.1 Choosing of the Best Main Factorial Number The main factorial number affects the predictive accuracy of the model. An excessive number will lead to over-fit phenomena. Conversely, a too small one will cause lack of fit. By implication, the model has a high accuracy forecast precision when the factorial number is 8. The relationship between main factorial number and RMSECV is shown in Figure 3.

Fig. 3. Relationship between main factorial number and RMSECV

3.2 Choosing of the Spectrum Preprocessing Method In the experiment, it gets the information of spectrum by scanning the full waveband NIR and describes the utilization of PLS for establishing a quantitative analysis model for predicting the content of palmitic acid in edible oils by using software of OPUS. After optimization analyzing, the determination coefficients (R2) and root mean square error of cross validation (RMSECV) are both at low forecast precision by using first derivative, second derivative, multiplicative scattering correction and vector normalization. However, the preprocessing method of first derivative with vector

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normalization can get a high accuracy model. R2 and RMSECV are 99.59 and 0.162, respectively. The result is shown in Figure 4. 3.3 Analysis of Unknown Samples In the range of wavelength 12492.9-7498.1cm-1 and 6101.8-4246cm-1 .The 12 samples are used to test the prediction model. R2 is 99.1 and RMSEP is 0.306. The

Fig. 4. Optimized model of calibration with partial least squares (PLS)

Fig. 5. Predicted and true value of validation with the model Table 1. True value and predicted value of the 12 samples Sample number 46 47 48 50 52 53 55 56 57 58 59 60

True value (%) 14.4 11.2 11.5 11.5 10.3 7.6 12.7 13.2 15.1 12.8 12.7 12.5

Predicted value (%) 13.785 11.126 11.552 11.621 9.983 7.601 12.903 13.161 14.461 12.898 12.462 12.174

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relationship between predicted value and true value is shown in Figure 5.The angle between prediction curve and standard curve is small. The predicted value converges towards the true value and the samples points distribution on both sides of the curve closely. There are no samples beyond the range of the model and so that it has a general applicability. The predicted and true value of the 12 testing set samples are shown in Table 1.

4 Conclusion (1). In the wavelength range of 12492.9-7498cm-1 and 6101.8-4246cm-1, the model has a high accuracy for predicting the palmitic acid content with vector normalization and first-derivative preprocessing spectra. 44 samples are selected for modeling set. The best main factorial number, R2 and RMSECV are 8, 99.59 and 0.162, respectively. Use this model to test the 12 samples. R2, RMSEP and average bias are 99.1, 0.306 and 0.148, respectively. (2). It is plausible to use NIR spectroscopy to test the content of palmitic in edible oils. However, this experiment is still at the initial stage. Regarding to the other important components in edible oils, the model still needs further improvement so as to get more generalization and application. Acknowledgments. This work is supported by superior and refined talents of Beijing funding project No. 20081D0500300130. The authors would like to thank Professor Xiaochao Zhang from Chinese Academy of Agricultural Mechanization Sciences to supply the NI Spectrograph to help us collect samples’ spectrum.

References 1. Christie, O.H.J.: Data Laundering by Target Rotation in Chemistry-based Oil Exploration. J. Chemometr 10, 453–461 (1996) 2. Lu, W.Z., Yuan, H.F., Xu, G.T.: Modern Near Infrared Spectroscopy Analysis Technique, China. Petroleum Press, Beijing (2000) 3. Cui, X.J., Yuan, C.M., Xu, L.H.: Near-Infrared Spectroscopic Analysis of Peroxides in Peanut Oil. Chinese Journal of Applied Chemistry 25(3), 375–377 (2008) 4. Yu, Y.B., Zang, P.: The Rapid Analysis of Fatty Acids in Vegetable Oils by Near Infrared Spectrum. Spectroscopy and Spectral Analysis 28(7), 1554–1558 (2008) 5. Kvalheim, O.M.: Interpretation of Latent Variable Regression Models. Chemometrics and Intelligent Laboratory Systems 7, 39–51 (1989) 6. Zhang, D.Q., Chen, X.N., Sun, S.Q.: The Quantified Analysis of Fresh Mutton Tenderness Using PLS Methods and Fourier Transform Near Infrared Spectroscopy. Spectroscopy and Spectral Analysis 28(11), 2550–2553 (2008) 7. Mcshane, M.J., Cote, G.L., Spiegelman, C.H.: Assessment of Partial Least-Squares Calibration and Wavelength Selection for Complex Near-infrared Spectra. Applied Spectrpscopy 52(6), 878–884 (1985)

Research on a Heuristic GA-Based Decision Support System for Rice in Heilongjiang Province Ran Cao*, Yushu Yang, and Wei Guo Engineering college of Northeast Agricultural University, Haerbin, China Tel.: 13796637319 [email protected]

Abstract. As we know rice is the main food in China. In recent years, the rapid development of agricultural decision support system provides new management methods for rice production. GA has been regarded as an effective analytic tool and stochastic search technique to solve large and complicated problems. However, traditional GA-based decision support system does not always result in good solutions and search efficiency due to random method. This study presents a heuristic genetic algorithm-based decision support system to search the optimal solution. Heuristic method provides a robust and efficient approach for solving complex real-world problems. This hybrid algorithm incorporates concepts from GA and heuristic method and creates individuals in a new generation not only by crossover and mutation operations as found in GA but also by heuristic mechanism. The result of experiments reveals the proposed algorithm is more computationally effective and efficient than GA alone in terms of solution quality. Keywords: Decision support system; Genetic algorithms; Heuristic method.

1 Introduction Heilongjiang Province is a major production base of rice in China. Cultivation area of rice is nearly 166,700 hectare. It makes up19% percent of the arable land area in our province. However, Heilongjiang Province is located in the cold and remote area. Economic advantages of rice are not play at all. In order to improve the output of rice, it is inevitable to make use of modern information technology to reform traditional crop cultivation. With the fast development of information technology, there are more and more tools available to decision makers. The most widely deployed systems are decision support systems. Decision support systems (DSS) are computer technology tools that provide solutions that can be used to support complex decision making and problem solving. In a word, it is designed to assist in identifying patterns, problems, opportunities, and eventually in making decisions [1]. In order to more accurately find an optimal solution in DSS, this paper significantly presented a GA scheduling algorithm which was enhanced by heuristic *

Supported by Heilongjiang Youth Science Funds project (QC2009C87).

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 322–328, 2011. © IFIP International Federation for Information Processing 2011

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method. Taking advantage of the compensatory property of GA and heuristic method, the proposed algorithm combines the evolutionary natures of both (denoted as HGA). The robustness of HGA will be tested by a set of experiments and the results are compared extensively with GA and this hybrid algorithm.

2 Structure of the System The framework of this DSS includes three parts: First: decision-making. The system provides a variety of possible strategies for users. It is based on the data of species, weather, soil and measures of cultivation management which is provided by database. Second: optimization. HGA algorithm analyzes a variety of strategies and then acquires optimal solution. Third: comparison and feedback. The system compares and analyzes suggestions from domain experts and users with the optimal solution to derive the best practice approach, and then feeds it back into the database in order to provide a favorable basis for the future decision. The system structure diagram is shown in Fig.1.

Suggestions from experts and users

Interface Data input User

Data output Table output Graphic output Text output

DSS

Comparison and feedback

Strategies

HGA

Optimal solution

Database Basic database Result database

Fig. 1. Architecture of decision support system

3 Genetic Algorithm (GA) There are three well-known randomization optimization techniques, SA, TS, and evolution algorithms (EA). The results of past research indicate that SA can converge

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to an optimal solution in all probability. However, it is practically impossible to satisfy certain conditions that ensure the global convergence of SA. In fact, the computation burdens of SA are extremely heavy. TS depends heavily on heuristic techniques. Hence, although they can converge faster than other randomization optimization techniques, they frequently fall into local optima. EA include all algorithms simulating the phenomena of living systems. GA is the most popular among all algorithms of EA [2]. GA is stochastic search techniques based on analogy to Darwinian natural selection. Individuals who fit the environment best should have a better chance to propagate their offspring. By the same reason, solutions that have the best “fitness” should receive higher probability to search their “neighbors”. The main advantage of GA lies in its powerful implicit parallelism. Moreover, a GA starts its global search simultaneously from many initial points, while both SA and TS start from only one initial point and search sequentially. Therefore, compared with the other randomization techniques mentioned above, a GA probably has the highest possibility of reaching global optima in a defined time interval and makes the best compromise between solution effectiveness and efficiency. Because all advantages of GA we have said above, GA is adopted in this paper. However, GA have three shortcomings. If the fitness value is set improperly, convergence can occur prematurely. The second is that the ability of local search is poor and the speed of convergence is slow. The third is that a traditional GA is a random process to generate initial population for each chromosome [3]. Most of the randomly generated chromosomes have poor total performance and thus pass unfavorable traits to their offspring. As we known, the heuristic method can not guarantee the optimal solution but only get the approximate optimal solution. We combine genetic algorithm with heuristic method because complementary characteristics of genetic algorithm and heuristic method. This hybrid algorithm can improve the effect of genetic algorithm.

4 Proposed Heuristic Genetic Algorithm (HGA) The hybrid method between genetic algorithm and heuristic method is various. For example, we can integrate heuristic method with initialization in order to generate initial population with good performance. We also can use local search heuristics as basic cycle plug-ins of genetic algorithm in order to execute quick local search [4]. This paper adopted these two methods. 4.1 Heuristic Search We introduced heuristic search in this algorithm. Heuristic search is that evaluating each search location in the state space to get the best location and then searching from this location until it reaches the target. It can omit a large number of insignificant search path, thereby improving efficiency. It is very important to evaluate the location in heuristic search. Different evaluation may produce different results. Evaluation is denoted as evaluation function in heuristic search. Such as: f(X)=g(X)+h(X), where f(X) is the evaluation function of node X. Assumed that setting out from starting node (denoted as S). We will calculate the minimum evaluation

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of reaching the target node (denoted as T) which is pass through node X. Evaluation function f(X) consists of two parts: one part is the minimum cost which is from S to X, denoted as g(X). The other is the minimum cost which is from X to T, denoted as h(X). In the actual process we use f(X)=CSX+l(X,x)+h(x), where CSX is denoted as the minimum cost which is from S to current node X. l(X,x) is the straight length between the current node X to expanding node (denoted as x). h(x) is the path length between x and T. l(X,x)+h(x) is heuristic function. We introduced heuristic search to improve the overall performance of population. It can make local search which is based on the corresponding phenotype of each individual in population because of the introduction of heuristic search. Local search can identify local optimal solution in the current environment. 4.2 Encoding and Initial Population In solving problems by GA, first task is to represent a strategy of the DSS as a chromosome. Each solution of the DSS has a value. This value corresponds to the actual performance of the solution. In this paper, strategies will be sorted from big to small according to their value and then make a queue. Fig.2 shows the encoding used in the GA. Each number represents the value of a unique strategy, with 0 being used to delimit different queues.

8 6

5 0

9 8

0

7 3 1

0

6 2

Fig. 2. Encoding of HGA

The distribution of the initial population has an important impact on convergence rate of genetic algorithm and the phenomena of avoiding premature. Too fragmented initial population will affect convergence rate. However, the oversimplified distribution of population will result in the lack of plurality and the genetic algorithm is prone to premature. Premature is the main reason of trapping in local optimization. It is very important to think about how to utilize some prior knowledge to restrict random distribution by combining heuristic rules and how to improve convergence rate of system. We propose heuristics to obtain good solutions for the initial population. While the first five chromosomes of the initial population are created by heuristics, others are randomly generated. We referred to priority rules as the heuristic information in this research. Priority rule is a basic heuristic rule. The individual which is based on priority rules usually has a high fitness. And the individual which is generated randomly ensures the diversity of initial individual. The combination of the two methods could unite diversity and high adaptability of initial population. It also could accelerate the evolutionary process. 4.3 Fitness Function It doesn’t utilize external information when genetic algorithm is in evolutionary search. It is only based on fitness function and makes use of fitness value of each

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individual in population to search. Therefore, fitness function directly affects convergence rate of genetic algorithm and whether the global optimal solution can be found. In order to not producing invalid chromosomes, it doesn’t need to introduce penalty function when we evaluate individual fitness. Therefore, the objective function itself is considered as the fitness function. Accordingly, feasible and infeasible solutions can be obtained for each population group. In this case if solution is feasible, value of fitness function is value of the objective function, otherwise, value of fitness function is equal to zero. 4.4 Genetic Operators An initially chosen chromosome leads to several other chromosomes through operators such as crossover and mutation. These operators are properly used to obtain improved results in each generation and finally the process is stopped when the improvement process stops. 4.4.1 Crossover Crossover is the main genetic operator. It operates on two chromosomes at a time and generates offspring by combining both chromosomes’ features [5]. There are several methods to crossover, partial-mapped crossover (PMX) is adopted in the proposed GA. 4.4.2 Mutation Similar to crossover, mutation is done to prevent the premature convergence and explore new solution space. However, unlike the crossover, mutation is usually done by modifying gene within a chromosome [6]. There are also a number of methods to mutate, such as insertion, swap and reversion. Reversion is employed in this paper. 4.5 Termination Condition When the stopping conditions are met, the evolution of the population will stop. This is to prevent the GA from running forever. We use two stopping conditions: First, there is an upper bound on the maximum number of generations, to guarantee evolution will halt. Secondly, if the fitness value of best individual in continuous t generations is no longer raise, the evolution will stop.

5 Computational Results The parameters of HGA are set as follows: population size is 100, crossover rate is 1.1, and mutation rate is 0.02. The termination condition when fitness values of chromosomes in a population are the same. And when the number of generations exceeds 100,000, searching will stop automatically. Because genetic algorithm is a stochastic searching heuristic, the result of every test is unlikely to be the same. In order to compare the average performance, 9 tests were conducted. To evaluate the algorithm’s efficiency and effectiveness, comparing the performance of HGA and traditional GA by exhaustive simulation running. The simulation results are depicted in Table 1 and Fig.3.

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Table 1. Comparison between the performance of pure GA and HGA

Number of solutions from DSS

Mean computation time(s) HGA GA 3.58 26.75 12.24 29.31 23.16 34.72 25.05 37.82 48.00 63.25 75.16 97.10 100.06 216.42 142.43 292.45 153.48 346.12

3 5 6 8 12 15 18 19 22

Average improvement of HGA over GA(%) 0.11 0.32 0.84 1.41 1.62 1.94 1.98 2.15 2.23

90 80 )% (o it ar no tia zi mi tp o

70 60 50

GA HGA

40 30 20 10 0

3

5

6

8 12 15 18 Number of solutions from DSS

19

22

Fig. 3. Comparison between pure GA and HGA on optimization

Table 1 reports the CPU time, and the improvement rate of HGA over GA in our experiment according to the number of strategies. From Table 1, it is clear that GA and HGA have similar performances on small number. However, when the number of strategies became large HGA is about 1.5–2.0% better than GA at the cost of additional computational time. And then examine the optimization behaviors. As shown in Fig.3, when generation number is small the optimization ratio of HGA is a little higher than GA. However, with the generation number increases the gap of them became bigger. And GA reached a saturation point soon. Through the simulation experiments, we can see that HGA had better efficiency and effectiveness than GA.

6 Conclusion This paper proposes a heuristic genetic algorithm to optimize solutions from DSS. Although the proposed HGA adopted classical genetic operators, its evolutionary mechanism is completely different and much more effective than classical GA. First of all, in this research, the algorithm has a more powerful search-oriented ability by

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using heuristic search method. Additionally, performance of the algorithm has been further improved by using sorted encoding and heuristic method of priority rule. This paper also introduced the design of fitness function and the choice of various genetic operators in details. In a word, a major gain is achieved in the addition of optimization ratio by the heuristic GA when compared with the traditional GA.

References 1. Zahedi, F., Song, J., Jarupathirun, S.: Web-based decision support. Handbook on Decision Support Systems 1, 315–338 (2008) 2. Low, C., Yeh, Y.: Genetic algorithm-based heuristics for an open shop scheduling problem with setup, processing, and removal times separated. Robotics and Computer-Integrated Manufacturing 25, 314–322 (2009) 3. Preechakul, C., Kheawhom, S.: Modified genetic algorithm with sampling techniques for chemical engineering optimization. Journal of Industrial and Engineering Chemistry 15, 110–118 (2009) 4. Lai, Y.C., Ding, M., Grebogi, C.: Controlling Hamiltonian chaos. Phys. Rev. E, 86–92 (1993) 5. Tariq, A., Hussain, I., Ghafoor, A.: A hybrid genetic algorithm for machine-part grouping. Computers & Industrial Engineering 56, 347–356 (2009) 6. Fulya, A., Mitsuo, G., Lin, L., Ismail, K.: A steady-state genetic algorithm for multi-product supply chain network design. Computers & Industrial Engineering 56, 521–537 (2009)

Research on Docking of Supply and Demand of Rural Informationization and “Internet Digital Divide” in Urban and Rural Areas in China Zhongwei Sun1, Yang Wang2,*, and Peng Lu2 1

Department of Resource & Environment, Shijiazhuang College, 050035 Shijiazhuang, China 2 Institute for Tourism Studies, Hebei Normal University, 050031 Shijiazhuang, China [email protected]

Abstract. Based on the cognition of “internet digital divide” from five respects in urban and rural areas in China, the paper analyzes the key links of relieving the question of “internet digital divide” in urban and rural areas and the construction of rural informationization. The research discovers that improving the human quality is the most important factor in solving the problem of the rural areas residents to contact network, and increasing the farmers' income is the key to raise internet popularity rate of the rural areas, and the mobile phone and construction of information service centre of various forms are important ways for the rural areas residents to contact network. This paper also proposes that the key point of the following work of rural informationization in China is to realize the docking of supply and demand through enhancing government information supplies and increasing information demands of rural areas. Keywords: Urban and rural areas, Internet digital divide, Rural informationization, Docking of supply and demand, China.

1 Introduction The rural areas informationization refers to construct the information service system to promote communication and knowledge sharing through strengthening the information infrastructure constructions in rural areas, broadcasting television network, telecommunication network, computer network and so on and to fully develop and use information resource, in order to realize the popularization application degree and process of the modern information technology in each aspect of the rural areas production circulation, the management and operation, the culture and education, the social management and the public service and so on (Li,2009). The internet construction and its application is the important contents of rural areas informationization in China, simultaneously it is also the most economical and convenient way to promote the informationization of the rural areas. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 329–338, 2011. © IFIP International Federation for Information Processing 2011

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2 The Present Situation of “Internet Digital Divide” in Chinese Urban and Rural Areas 2.1 Netizens’ Dimensions and Popularity Rate in the Urban and Rural Areas The difference of netizen dimensions and the popularity rate in urban and rural areas is one of the two major problems of internet digital divide. Up to the end of 2009, the population number in Chinese cities is 621,860,000, in which netizen dimensions is 277,190,000, the cities netizen popularity rate is 44.6%, the inhabitant population in rural areas is 712,880,000, in which netizen population is 106,810,000, the countryside netizen popularity rate is 15.0%.At present, the countryside netizen population in China occupies 27.8% in the total quantity of netizens.Although the speed-up of countryside netizens was very quick in recent years, but the rural areas netizen popularity rate was still far lower than the national average level, also is enlarged gradually with the cities netizen popularity rate disparity (to see Tab.1).In addition, the internet popularity rates of east, middle and west of China at the end of 2007 are 14.4%, 4.4% and 3.5%, this explains the internet digital divide question among different regional rural areas is also serious. Table 1. Netizens’ popularity rate in China from 2005 to 2009 Names of index Netizen popularity rate in urban and town (%) Netizen popularity rate in China (%) Netizen popularity rate in rural areas (%)

2005 16.9 8.5 2.6

2006 20.2 10.5 3.1

2007 27.3 16.0 7.1

2008 35.2 22.6 11.7

2009 44.6 28.9 15.0

2.2 Net Touching Place and Way of Netizens in Urban and Rural Areas The net touching place and way of netizen in Chinese urban and rural areas are different greatly (to see Figure 1). Firstly, the family and the internet bar are the most main places in which netizens access the net, but the choice of accessing the net by netizens from urban and rural areas are somewhat different. The proportion of family surfer crowds from rural areas is 68.0%, which is lower than the cities of 14.3%.But the proportion of internet bar netizen from rural areas is 54.2%, which is higher than the cities of 16.2%.Among 84,600,000 countryside netizens at the end of 2008, the population of internet bar netizen achieves 45,850,000, in which the population of the internet bar surfer netizen is only approximately 7,870,000.Secondly, the net touching way of netizens in Chinese urban and rural areas exists difference. The desktop computer is still the main equipment which the netizen accesses the net, the difference between town and county is not obvious, simultaneously the cities’ netizen proportion who uses the portable computer surfer is 10.9% higher than that of the rural areas. Up to the end of 2008, the handset surfer users in Chinese cities are 77,890,000, it occupies 36.5% over all cities netizen; the handset surfer users in Chinese rural areass are approximately 40,100,000, and it occupies 47.4% of all countryside netizens.

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100

（）（%）

90

Urban netizen % Countryside netizen

80 70 60 50 40 30 20 10 0 Home

Internet bar

Desktop computer

Portable computer

Mobile

Fig. 1. Internet access method difference of netizen in Chinese urban and rural areas

2.3 Netizen’s School Record and Occupation in Urban and Rural Areas The netizen's difference in school record and occupation distribution between cities and the rural areas is quite obvious. In school record, the cultural level of rural areas netizen is relatively low, the netizen’s school record in the junior middle school accounts for 42.3%, is 20% higher than the cities; The rural areas netizen’s proportion in high school is 38.6%, which is 1.2% lower than that of the cities. The netizen’s proportion in or above the technical college is 12.0%, which is lower 33.2% far than the cities.In addition, the proportion in the elementary school and following cultural level accounts to be low in the netizen from urban and rural areas. From the contrast of netizen’s occupational structure between rural areas and the cities, the students’ number from the middle-school occupies 38.8%, which is 7.6% higher than those from the cities’ middle-schools; the jobless, people who have laid off, the unemployed and the farming and forestry herd fishing workers occupy a high rate in the countryside netizens. 2.4 Netizen’s Application Goal of Network in Urban and Rural Areas At present, the network application of the countryside netizens is unitary, which is mainly to entertain them selves. The attraction of internet to farmers mainly comes from the function of network leisure entertainment, network music, network video frequency, network game as well as network chats become the main purpose of using the internet to the rural areas netizen, its user scale has surpassed 50,000,000.The online listening and looking rate of network music, network film and television surpassed the cities, 86.4% and 76% of countryside netizens use these two functions of the network separately at the end of 2007.Many countryside netizens only regard internet as chating and amusement tool, in which the proportion of playing the network game has reached as high as 58.2%.

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2.5 Netizen's Network Demand and Understanding in Urban and Rural Areas The non-netizens want to connect the Internet in urban and rural areas and only 15% of people is not interested in it. Two reasons why the rural areas inhabitants don’t often connect the internet are that they are lake of the skill and they don’t have the relevant equipment .The investigation results at the end of 2007 (to see Figure 2) showed that the reason why 53.3% of non-netizen which is no surfing the internet of in the rural areas is lacking play skill on internet, compared to near 10% higher than cities non-netizens. The non-surfer equipment is one of the important reasons for rural areas inhabitant’s surfer, its accounts for the proportion of 5.3% higher than the cities. In addition, the time for surfer has already leaped to the second tremendous influence factor which influenced the non-netizen to do no surfing instead of lacking the surfer equipment. In the investigation of rural areas non-netizens at the end of 2009, 38.8% of no surfer people are lack of surfer skill, and 19.7% of no surfer people are short of surfer equipment as a result, only 3.5% of no surfer people does not have the condition.

60 Urban

50

（%）（%）

Countryside 40 30 20 10 0 Without internet skills

No web device

No time

No interest

High internet access charges

Fig. 2. Reason for not online of Chinese non-netizen by the end of 2007

3 Analysis of “Internet Digital Divide” in Chinese Urban and Rural Areas 3.1 Construction of Rural Areas Informationization Infrastructure Has Begun to Take Shape The rural areas informationization construction has already became the important action of the socialism new rural reconstruction, in order to let the information technology and the service benefit trillion of farmers, the government has increased the support to the rural areas telecommunication and the internet. Up to the end of 2008, 98% of national villages and towns could access the net, 95% of villages and towns

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pass the wide band, and 27 provinces have realized “the township township to be able to access the net”. In 2009, it realized “the village to village has telephoned, the township and township can access the net” in the area phone project basicly, the proportion of nation telephones of the administrative village and the natural village respectively achieved above 99.8% and 93%, realized above 99% villages and towns to be able to access the net, 96% of villages and towns passed the wide band. 3.2 Long-Standing of Internet Digital Divide in Urban and Rural Areas A lot of factors lead to the internet digital divide in urban and rural areas. Firstly, the superiority of the population scale and the density aspect in cities and towns, especially in cities, causes the scale income of the information infrastructure construction to be remarkable and they become the first choice. Secondly, the application demands from urban population to information infrastructure and so on in the internet are more intense, this has drawn the information infrastructure construction and the service supplies to a certain extent. Thirdly, the cities inhabitants have the high scientific cultural quality, and they have the superiority in the internet cognition and the surfer skill. Fourthly, the cities inhabitant’s income is relatively high, this causes stronger bearing capaurban at aspects and so on in surfer equipment and fees. Fifthly, the urban inhabitants mainly do mental labor, while the rural areas inhabitants mainly do physical labor, the former is more convenient to connect the net .The above reasons which will exist for a long time have decided the long-term characteristic of internet digital divide question in urban and rural areas. 3.3 Improving the Human Quality Is the Most Important Factorto Solve the Problem of Touching the Net for the Rural Areas Inhabitant The most important reason why the non-netizen in rural areas does not access the net is that they don’t have the surfing skill. Currently speaking, the most essential factor with the surfer skill is inhabitant's scientific cultural quality. The height of scientific cultural quality often decide the height of a person with accepts ability to the new thing like internet cognition. Take the internet as the example, the junior middle school level was generally considered most elementary knowledge level which the surfer should have. This is because it should have writing recognition capability, as well as the information search, discernment and handling ability to use the internet skillfully, and it needs the cultural level of above the junior middle school at least. By contrast, the entertainment function is somewhat low to netizen's scientific cultural quality request. Therefore, to improve the rural areas inhabitant's scientific cultural quality becomes the most important question in solving the needs of rural areas netizen’s touches to the net in China. 3.4 Increasing the Farmer’s Income Is the Key to Enhance the Rural Areas Internet Popularity Rate Personal economy payment ability is one of the factors that affects surfer or not, the reason of non-surfer equipment and expensive surfer expense which impact the nonnetizen doing not access the net all belong to this kind of situation. The appearance of “the internet digital divide” with regional characteristic in east, middle and west of

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China is caused by the local economy development difference. At the same time, the cities inhabitant’s disposable personal income to maintain basically in 3 times (to see Figure 3) at the rural areas inhabitant’s in recent five year, on the other hand the surfer fees invested are almost the same in purchasing surfer equipment in the urban and rural areas actually. Although the policy of “the electrical appliances went to the country” and so on to the rural areas inhabitant provide the convenient way for purchasing the computer, but affect limitedly in impelling to use computer to the rural areas area. Speaking of the majority of farmers, the computer is the luxurious thing. Along with the maturity of information and communication technology gradually as well as the widespread of application and range of service day by day, the dropping of “the economical threshold” in surfer is the inevitable trend. Therefore, to increase the farmer’s income to enlarge the ratio of the average of per person net income/threshold of surfer economical unceasingly will be the key to enhance Internet popularizing rate in rural areas.

（）（）

20,000 18,000

Per capita income of urban residents yuan Per capita income of rural residents yuan

16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 2005

2006

2007

2008

2009

Fig. 3. China's per capita income from 2005 to 2009

3.5 Mobilephone and Construction of Many Kinds of Information Service Center Is the Important Way for the Rural Areas People to Touch the Net At present, high economical threshold is still the important factor which restricts our country rural areas inhabitants to access the net, and this kind of condition cannot change too greatly in a short-term. The difference in the surfer place and way of netizen in the urban and rural areas shows that. Since the rural areas area cannot equally access the net in the way to the cities through purchasing individual computer and surfing on line at home to enhance the internet popularity rate, the mobile phone and construction of many kinds of information service centers become two big important ways in which the farmer touches the net. The economical threshold of surfing on line by mobile phone is lower a lot than through the computer, although the difference in function is great, but chooses the former in situation of not too realistic to choose the

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latter also to be acceptable. In addition, constructs including the internet bar, the internet information service station and so on which belongs to the different forms of concentric information service center, provides the basic hardware equipment, the farmer only need pay a little to access the net.

4 Copying Strategy of Informationization Supply and Demand Docking in Chinese Rural Areas The informationization construction in Chinese rural areas, especially the solution of urban and rural areas, “internet digital divide” is a systematic work. It needs to start with both the supply and demand, namely “makes an offering needs”, the concrete implication is to fully develop government's leading role in rural areas informationization supplies, mainly through construction of information infrastructure and policy guidance to realize information supplies; At the same time, it needs to enhance the farmer’s information accomplishment through the multi-ways to increase information need in rural areas, thus help to realize the supply and demand docking of informationization construction in rural areas. 4.1 Proceed to Develop Leading Roles in Rural Areas Informationization Supplies by Two Levels of Government In China, the achievement of rural areas informationization construction in national stratification plane is remarkable. Because the highly attention from national stratification plane, our country will realize the goal with 100% of administrative villages to telephone, 100% of villages and towns will be able to access the net in 2010.Here, the rural areas informationization in China, especially the network infrastructure construction has begun to take shape, the emphasis from now on is to realize the transformation from the infrastructure construction to the application and promotion and consummate service.The key actions included consummating the construction of rural areas information platform, sharpening the rural areas information service ability; fully propeling the activity of information to the country, perfecting rural areas information service system; using each item of agriculture-friendly policies, promoting the information terminal and the service to enter the village residence. In addition, the Central Party Committee, the State Council should continue to pay attention to informationization in rural areas, strengthen the cooperation and coordination of the Ministry of Agriculture, the Industry and the Informationization Department, and mobilize the subjective initiative of China Telecom, China Mobile and China Unicom as telecommunication enterprise, only then will safeguard the result of Chinese informationization construction and supplies in rural areas. In various provincial levels administrative division, it should act according to the existing informationization construction foundation as well as the rural areas social economic condition, to choose the informationization construction measure in rural areas which suits its own. In different area of one province, it may choose the suitable way of rural areas informationization construction according to its own condition. South Korea implemented the construction plan of “the informationization demonstration village” which can provide the model for us. Through constructing the

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experiment site in the nation firstly, and it expanded and increased quantity way gradually to carry on after the success. Each of “the informationization demonstration villages” includes: The internet infrastructure with high speed, and realizes the broadband networks to enter the peasant’s household; Establishing the village information center, disposes computer as hardware and so on and realizes connection to the local administration information network; Constructing the network use environment of peasant’s household, the existing demonstration village has achieved above 73% that peasant’s household has provided the computer; Establishes management operation system, forms the demonstration village operation committee of the villagers, and the informationization instructors and the information center administrative personnel participates in the operation together; Carrying on the talented person to educate training and raising one batch of rural areas informationization backbone and the administrative personnel.

①

③

②

④

⑤

4.2 Through Multi-ways in Enhancing the Farmer’s Information Accomplishment to Increase the Information Need in Rural Areas Aims at the farmer to implement the informationization training popular project in rural areas, this project contains training and the propaganda & popularity. The training object is the first-line managers in rural areas with village cadre, village staff, village information officer, the specialized farmer as well as the ordinary farmer has the enthusiastic. Training may be divided into two levels as the basic class and enhance class, and unifies to organize, develop and compile training materials according to the rural areas characteristic. The training spot should have the basic condition as below: Having some places with certain quantity of computer, condition of surfing online; Having the training teacher who is familiar to rural education characteristic. The object of propaganda and popularization is all villagers; The content is to introduce knowledge of computer, internet, mobile phone and so on to the rural areas inhabitant, to guide them to know about the internet and to raise information consciousness; The propaganda and popularization material may be compiled voluntarily by the urban or by the actual situation of various county unions; The form of propaganda and popularization is mainly “the small information boxcar” to enter the market town, village, shop and so on, organizing the rural areas inhabitant to watch the propaganda short film, to join the experience activity and to read popular reader. Allocated the agricultural information officer to provide the surfer instruction for farmers, this paper has mentioned that construction of information service center with many kinds of form is one of important ways which the rural areas touches the net. At present, the information service center mainly manifests the form of agricultural information service station for the basic units as town, village and so on. But in the process of service station construction, we need to consider the flaw of rural areas inhabitant in surfer skill, therefore it must provide all levels of agricultural information officer and carry on the training to the farmer and provide the surfer instruction for them as necessary. At the same time, the agricultural information officer can also display the potency in the aspect of information supply, reorganize and issue the rural areas information through preparing for construction and manages local agricultural information network, and organize farmers to participate in it.

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To popularize the rural areas informationization popularization with the training by the university student village officials is a very important action. Apart from allocating the agricultural information officer to provide the surfer instruction for the farmer, but also another way may get up the effect of “fans out from point to area” in the rural areas, namely through the university student village officials as “spot” then leads its in the village as “surface”. The university student village official has the high scientific cultural quality, the strongly information consciousness, moreover they have grasped the highly surfer skill in school period, simultaneously they have the work superiority in village, therefore it only needs to make part of the development training pointedly, they may play the role which cannot be substituted in training farmer’s information consciousness, the promotion and use of rural areas informationization work and so on in a series of key links. The result is that each university student village official leads the informationization construction of a village, the general university student village official can lead the entire informationization level in local rural areas naturally. This paper also mentioned that it was necessary to understand student’s vital role in rural areas informationization correctly. At present, the middle-school student community occupies as high as 38.8% of the netizens in Chinese rural areas. The student is the main body of farmer informationization, simultaneously it is also the latent strength which will decide the future level of rural areas informationization. Therefore, we should correctly understand student's vital role in rural areas informationization.It needs to enlarge the promotion work of internet popularization in the rural areas school for enhancing the rural areas student's informationization accomplishment. The school must take the internet education as an important content of daily teaching, to guarantee students to have the basic surfing skill after the graduation from middle school, thus promoting enhancement of rural areas informationization level with steady steps. In this process, the school must also let students realize the positive aspects of the internet and restrain the negative ones through education.

5 Conclusion The internet construction in rural areas area must be based on the correct cognition of “the internet digital divide” in the urban and rural areas, this includes both level of development disparity in social economy between the provinces even in different areas of the same province, and it also includes long-term and the alleviation difficulty which this question exists. Therefore, we put forward that improving the human quality is the most important factor for the rural areas inhabitants to touch the net, increasing the farmer income is the key to enhance the internet popularity rate in rural areas, the mobile phone and information service center construction with many kinds of forms is the important way which the rural areas touches the net. The information network construction in Chinese rural areas has already began to take shape in the period of“The Eleventh Five-Year Plan”, the present key question has shifted to the questions of the lag of application conformity and the unsure grasp to farmer's true demand and so on. This needs us to obtain from two aspects as the supply and

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demand, we should display the leading role of government in rural areas informationization supplies, simultaneously enhance the farmer’s information accomplishment in multi-ways to increase the information need in rural areas, and thus helping to realize the supply and demand docking of rural areas informationization construction.

Reference 1. Li, D.: China Rural Informatization Development Report 2009. Publishing House of Electronics Industry, Beijing (2009)

Research on Evaluation of Rural Highway Construction in Hebei Province Guisheng Rao1, Limeng Qi1, Runqing Zhang1, and Li Deng2 2

1 Agricultural University of Hebei, Hebei, P.R. China Department of Economic and Management, Tangshan College, Hebei, P.R. China

Abstract. Rural highway is the foundation of highway network construction and the important bridge for balancing urban and rural harmonious development. Rural highway has the basic, pilot and driving function to new rural construction. This paper took a sampling survey on 173 villages in Hebei Province. According to the 687 questionnaires, this paper set up an evaluation system of the rural highway construction in Hebei Province, using ten factors as effect variables (such as the degree of hardening the roads and the width of the roads). This article sorted selected indexes by AHP, and this paper gave scientific basis to improve policies on rural highway construction in Hebei Province through the sciencific evaluation of the rural highway construction in Hebei Province [1]. Keywords: Rural highway of Hebei Province; Evaluation system; AHP.

1 Introduction As the important public welfare in rural infrastructure, rural highway plays a special role in facilitating the travelling needs of rural residents, advancing the rural logistics flow, speeding up rural economic and cultural development, and building a socialist new countryside. In the 30 years since the reform and opening up, Hebei Province has made brilliant achievements in using of the favorable opportunities of implementing the proactive fiscal policy, accelerating infrastructure construction, and concentrating on a large scale in rural roads construction. The length of rural highway in Hebei Province was 30,300 km in 1978, and reached 128,200 km by 2008, by an increase of 323.52%. The length of rural highway in Hebei Province was the 9th longest in China. Rapid development of rural roads played a very important role in advancing the economic and social development of Hebei province, especially the construction of new countryside. With the accelerated pace of rural highway construction and the improved grade of the highway, how to build the rural highway to adapt to the rural economic development and make the achievements of rural road construction benefit the broad masses is urgent. This study is concerning the problem. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 339–344, 2011. © IFIP International Federation for Information Processing 2011

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2 Evaluation Model of Rural Roads Construction in Hebei 2.1 Sample Description During July and August in 2009, 173 villages were selected and investigated in the 11 prefecture-level cities of Hebei province (Table 1), through the summer investigation of the Agricultural University of Hebei, and 687 effective questionnaires were received. The survey covered a wide scope, involved all household types, with strong representation [2]. Table 1. Evaluation system of rural roads construction

Target layer

Primary index Construction quality of rural Roads (B1)

Secondary Index Pavement width (C1) Sclerosis of roads(C2) Highway lighting(C3)

Facilities in rural roads(B2) Evaluation System of Rural Roads Construction in Hebei provence

greening of rural roads(C4) Trash clean-up facilities(C5) Villagers self-support(C6)

Management of rural roads (B3)

Unified management by counties and towns(C7) Combining with the former two factors(C8) Government investment(C9)

Rural roads Investment (B4)

Villagers raise funds by one project one discussion system(C10) Combining with the former two factors(C11)

2.2 Model of Hierarchical Structure According to the issues of survey questionnaire, we divideed construction system of rural highway in Hebei into four major categories. They are the construction quality of rural highway, facilities construction of rural highway, management of rural highway and investment on rural highway. We put pavement width and sclerosis of roads as the secondary indexes of the construction quality of rural highway; put highway lighting, greening and trash clean-up facilities as the secondary indexes of facilities construction

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of rural highway; put villagers self-support, unified management by counties and towns and combining with the former two factors as the secondary indexes of management of rural highway; put government investment, raising funds by one project among villagers with one discussion system and combining with the former two factors as the secondary indexes of investment of rural highway. Considering the willingness of farmers totally, we invited 23 experts to rate each of the indices, and get final score of each indicator through summing of the average. Based on this formula, we established the AHP model which could evaluate the construction of rural highway in Hebei. Due to the different levels of economic development in rural areas of Hebei, the effects of these four indicators are different to the development of rural highway in Hebei. Based on this, we construct hierarchical structure of the development of rural highway in Hebei. 2.3 Build Judgement Matrix As the effects of each index are different to rural roads satisfaction of Hebei, we interviewed different experts and relevant administrative departments through questionnaire form and got digital data which are effect factors of highway construction in rural areas of Hebei (The weight of each factor), and then constructed the first order judgment matrix (Table 2) and the second order judgment matrix (Table3), researching the cognition of farmers on rural roads. 2.4 Weight Calculation of Single Hierarchy and Consistency Test Eigenvalues and eigenvectors were calculated by AHP Software, the weight sorting results of each effect factor were listed in the table 2 and table3. The results of each matrix of the consistency test were listed in the bottom of each table. From table 2 we can determine the effect size of various indicators in evaluation of highway construction in rural areas of Hebei, the result of operation was λmax=4.181, Table 2. First order judgment matrix

A B1 B2 B3 B4

B1 1 1/4 1/3 3

B2 4 1 1/3 5

B3 3 1/3 1 4

B4 1/3 1/5 1/4 1

Sort Weight 0.269 0.068 0.134 0.529

Table 3. Second Order Judgment Matrix

B1

C1

C2

Sort Weight

C1 C2

1 7

1/7 1

0.125 0.875

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CI=0.060, RI=0.900, CR=0.067<0.1, so the test of consistency passed. Results showed that the top one of weight sort was the investment on rural highway of Hebei, and it was 0.529, playing an important part in this group. The second one of weight sort was the quality of rural roads construction, and it was 0.269. The sum of two was 0.798, the sum of other two was only 0.202. From table 3, we can determine the contribution size of pavement width and sclerosis of roads in Hebei construction quality of rural highway, λmax=2.000, CI=0, RI=0, CR=0<0.1, so the test of consistency passed. Results showed that the greatest impact on Hebei construction quality of rural roads is pavement width, which is much higher than the width of pavement. Table 3. (Continuation Sheet 1)

B2 C3 C4 C5

C3 1 3 1/3

C4 1/3 1 1/5

C5 3 5 1

Sort Weight 0.258 0.637 0.105

From Table 3 (Continuation Sheet 1) ,we can determine the contribution size of highway lighting, greening of highway and trash clean-up facilities in Hebei facilities construction of rural highway, λmax=3.039, CI=0.019, RI=0.580, CR=0.033<0.1, so the test of consistency passed. Results showed that the greatest impact in Hebei facilities construction of rural roads is greening of highway roads, and the weight sort is 0.637; and the next is highway lighting, and the weight sort is 0.258;the sum of two is 0.895. Table 3. (Continuation Sheet 2)

B3 C6 C7 C8

C6 1 1/3 4

C7 3 1 5

C8 1/4 1/5 1

Sort Weight 0.226 0.101 0.674

From Table 3(Continuation Sheet 2) ,we can determinehe the contribution size of villagers self-support, unified management by counties and towns and combining with the former two factors in management of rural highway, λmax=3.086, CI=0.043, RI=0.580, CR=0.074<0.1, so the test of consistency passed. Results showed that the greatest impact on management of rural roads is townships management combining with villages, and the weight sort is 0.674;the sum of the other two is 0.327. From Table 3 (Continuation Sheet 3), we can determinehe the contribution size of government investment, raising funds by one project among villagers with one discussion system and combining with the former two factors in rural roads investment in the investment on the rural highway, λmax =3.054, CI=0.027, RI=0.580, CR=0.047<0.1, so the test of consistency passed. Results showed that the greatest impact on the

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investment on rural highway is government investment combining with collecting money by villagers, and the weight sort is 0.691; the sum of the other two is 0.309. Table 3. (Continuation Sheet 3)

B4 C9 C10 C11

C9 1 1/3 4

C10 3 1 6

C11 1/4 1/6 1

Sort Weight 0.218 0.091 0.691

2.5 Weight Calculation of Overall Hierarchy and Consistency Test CI=0.021, RI=0.424, CR=0.050<0.1, so the test of consistency passed. From table 4, we can arrive at the conclusion that the top three indexes are government investment, combining with collecting money by villagers and sclerosis of roads and government investment. Results showed that the investment on rural highway construction in Hebei should be financed mainly by government and farmers concerned sclerosis of rural roads most, both of them have become the most important factors in the evaluation system. Table 4. Overall hierarchy

A C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

B1 0.269 0.125 0.875

B2 0.068

B3 0.134

B4 0.529

0.258 0.637 0.105 0.226 0.101 0.674 0.218 0.091 0.691

Sort Weight 0.034 0.235 0.018 0.043 0.007 0.030 0.014 0.091 0.115 0.048 0.365

3 Conclusions and Recommendations (1) The position of greatest importance is source of investment in evaluation of highway construction in rural areas of Hebei, government investment accounted for a dominant position, rural roads are consistent with the characteristics of public goods. Therefore various places should be mainly based on financial allocations, supplemented by a variety of channels to raise, and adjust the expenditure of the local governments in time, rural roads construction should increase the proportion of public expenditure, introduce the mechanism of multiple investments[3].

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(2) Under the premise of ensuring the rural highway construction funds, the quality of rural roads construction becomes the focus of evaluation of highway construction in rural areas of Hebei, sclerosis of roads has become the center of the quality of rural roads construction. After years of construction there are many rural roads paving with cement or asphalt pavement, but paving gravel broken stone or other materials is still very much, it has affected the living of local farmers and economic development. Therefore, government should increase the budget of rural roads construction, try to raise multiple funds, meantime act according to circumstances, on the basis of taking full advantage of original rural roads, improve the hardness of rural roads. (3) Rural roads maintenance policy is imperfect, management system is sick, so the phenomenon of deficient maintenance is very universal. We suggest that township government be the main responsibility of rural roads maintenance, highway departments guide the implementation of industry.Routine maintenance of rural roads may be managed by village committee, major repair of pavement and pothole repair of asphalt pavement may entrust professional maintenance organizing [4]. (4) With the raising of rural economy level, farmers pay more attention to the improvement of living environment. Therefore, greening of rural roads should manifest three features: leisure, culture and aesthetic, guide farmers through afforestation to improve their production and living conditions. Lighting facilities should be installed along rural roads to facilitate farmers travel at night. In addition to strengthening publicity, foster farmers the concept of protecting environment, enable farmers to designate dumping of solid waste in rural areas, and timely clean-up.

Acknowledgement This paper is supported by The Social Science Fund of Hebei Province (HB10EYJ168), The Main Problems and Suggestions of Public Infrastructure Building in Hebei Natural Villages.

References [1] Qi, L., Zhang, R., Zhao, X., Deng, L.: Analysis on The Regional Differences of Rural Infrastructure in Hebei. In: Proceedings of the 2009 International Conference on Public Economics and Management Xiamen, P. R. China, November 28-29 (2009) [2] Yi, H.-m.: The Provision of Infrastructure and Farmers Demand——Evidence from 5 Province. China Soft Science, 11 (2008) [3] Zhang, S.-l.: Research on the Peasants Desire of Supplying Rural Public Goods in Constructing New Rural Areas——Based on a surrey of 644 farming households of Hebei. Rural Economy 4 (2008) [4] Li, Z.: Research on Rural Infrastructure Investments- -An Case Study from Hebei Province. Agricultural University of HeBei (2007)

Research on Farmland Information Collecting and Processing Technology Based on DGPS Weidong Zhuang and Chun Wang College of Engineering, Heilongjiang Bayi Agriculture University, Daqing, China [email protected]

Abstract. In order to realize the farmland information pinpointing acquisition, this paper has conducted the technology research for farmland information collecting and processing based on the DGPS. According to the precision agriculture’s demand which agricultural condition information should be made positional, fast, precise, continuous acquisition, system uses the DGPS receiver and the touch-screen computer, uses Microsoft Access database and uses Visual Basic 6.0 to establish the application program, which used in the farmland information acquisition and processing. According to the farm land contract's demand, the method is established by using DGPS receiver to survey and drawing for field length of side and the area, which was applied in the farm and the effect was good. The result shows that this technology had very good usability. Keywords: DGPS, information collecting and processing, area survey, precision agriculture.

1 Introduction The application of Precision Agriculture can change the traditional agriculture of extensive and managing mode, essences the farmland homework, reduces the production cost, protect the ecological environment, increase economic benefits. GPS (global positioning system) and GIS (geographic information system) is the key technology of precision agriculture, and it is in large quantities of application in agricultural production [1-8]. This article is aimed at the need that is the measure of fast farmland. The system uses differential signals of the DGPS receiver, develop application software for receiving and dealing with GPS data, farmland circumference, measuring and calculating the area of land and drawing, in order to improve the efficiency of information acquisition and farmland quality, reduce cost, and easy work.

2 Receiving and Processing the GPS Data 2.1 Receiving the GPS Data GPS receiver usually adopts the provisions of NMEA 0183 data output of information, It is should be at least two GPS positioning data that contains GGA and RMC. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 345–350, 2011. © IFIP International Federation for Information Processing 2011

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The computer can receive data form GPS receiver by port RS - 232, and will locate data income with MSComm control. The GPS data is analyzed, and some information is got from GPS receiver such as latitude, longitude of the receiving antenna and differential information, satellite number, speed, etc. 2.2 Limiting Average Filtering Limiting average filtering are refer to the recent data which each time the sampling arrives to carry on Limiting processing first, then carries on average filter processing. It is advantage that it can eliminate the accidentally appears pulse interference which can cause sampling value deviation. Because the data obtained from DGPS receivers in the positioning information which includes velocity information, and the speed information can be used for limiting average filtering in the field survey, so as to improve the accuracy of the measurement. A position’s information for the field of sampling point, can use speed v is smaller than setting threshold VT, in n times average point positioning information as the measured value, Formula is as follows:

Y=

1 n ∑ X (i ) , if n 1

vi < VT

(1)

2.3 Projection Transformation Because the GPS receiving data is three-dimensional coordinates, but it usually uses the plane rectangular coordinates in the actual survey. Therefore it must be carried on the coordinate transformation. For actual surveys the application, WGS-84 ellipsoidal coordinates, which use latitude and longitude, should be transformed to the plane rectangular coordinate system according to 6 degree or 3 degree belt projections. Now, Gauss-Kruger or UTM projection is often be used, they belong to the horizontal axis of the Mercator projection. When the central meridian coefficient of length m0=1, it called the Gauss projection. When m0=0.9996, it called the UTM projection. It should be pointed out that when farmland cross standard project belt, the unique central longitude should be established for the all fields in same projection zone.

3 Perimeter Area Computation 3.1 Farmland Perimeter

（

）,（

）

x2 , y 2 , The distance of two IF we know two points coordinates x1 , y1 points dAB can be computed with the plane distance formula, Formula is as follows: d AB =

(x2 − x1 )2 + ( y 2 − y1 )2

Under the situation of every of farmland vertex coordinates perimeter P formulas is as follows:

(2)

（ x , y ）, farmland k

k

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n

(x k +1 − x k )2 + ( y k +1 − y k )2

P=∑ k =1

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(3)

Note: x n +1 = x1 ， y n +1 = y1 3.2 Farmland Area In consideration of undulating plot, plot is flat polygon farmland, In the case of meas-

（

）

uring every farmland vertex coordinates x k , y k , Farmland area can be available of plane deformation formula to calculate , formulas is as follows:

S=

1 n ∑ (xk y k +1 − xk +1 y k ) 2 k =1

(4)

Note: x n +1 = x1 ， y n +1 = y1 If the farmland vertex is clockwise arranged, its area S is negative, When it is arranged for counterclockwise, its area s is positive[9]. Therefore, the area can be calculated from absolute S, then all the farmland area is positive number.

4 Programming According to the precision agriculture’s demand which agricultural condition information should be made positional, fast, precise, continuous acquisition, we develop The main of the program

Receiving and processing GPS information

Reading measurement data

Length area measurement

Calculation and drawing

Measurement and storage data

Storage of diagram

Fig. 1. Structure of software system

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Fig. 2. Demonstration of Software Table 1. Example of field survey data ID 1 2 3 4

Date 09-06-27 09-06-27 09-06-27 09-06-27

Time 11:02:41 11:05:52 11:08:38 11:11:39

Longitude 125.1583 125.1581 125.1659 125.1660

Latitude 46.58145 46.58743 46.58799 46.58150

the application software .system uses Visual Basic 6.0 to establish the application program and uses MSComm controls and the DGPS receiver, uses Microsoft Access 2000 database to store location data. uses ADO database connection[10]. Software should finish the function that the GPS receives processing information, farmland area, perimeter of measurement, storage data and calculated data and drawing, the block diagram storage etc. The function of software diagram is to see figure 1. The development of software is operated as figure 2.The database for the measurement data of land is in table 1.

5 Area Measurement Test and Application 5.1 Area Measurement Test In order to test the performance of the system in June 2009, using AgGPS 332 receiver, the touch-screen vehicle carries the computer and the MSA2 satellite signal is to test the DGPS area measurement that chooses 3 quadrilateral plot in Heilongjiang Bayi Agriculture University campus, it is tested by 3 replications respectively. Measuring results in table 2.

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It can be shown from the table 2, area measurement using DGPS is repeated well and measurement error is very small. Furthermore, with the increase of measuring plot area, the measure of the relative errors is decreased. It can completely meet the requirement of agricultural land area. Table 2. Measurement and analysis of DGPS area Location 1 Circumference Area /m /m2 1 137.3 1096.3 2 137.1 1093.2 3 137.7 1102.9 Average 137.4 1097.5 Maximal 0.24 0.50 relative error/%

Location 2 Circumference Area / /m m2 185.3 1988.6 185.4 1985.4 185.6 1991.0 185.4 1988.3 0.09 0.15

Location 3 Circumference/ Area m / m2 2570.9 409723.4 2571.8 410017.6 2572.4 410221.4 2571.7 409987.5 0.03 0.06

5.2 Area Measurement Applications From June 2009 to October 2009, according to the land contracting requirements of one of the agriculture company in Qiqihar, heilongjiang province, China. AgGPS332 and the software is used for the measure the farmland area which was 22817 hm2. Using software and the DGPS receiver compared with traditional method of using altazimuth and meter rule that the first is measuring speed, high accuracy and the application effect is very good.

6 Conclusion The Satellite different signal high-precision DGPS receiver in agricultural production is widely used in the field, Such as farmland surveying and mapping, soil sampling, fertilization, crop growth, production monitoring etc. According to the demand of agricultural production units, using DGPS receiver, touch screen, the development of the farmland information collection and processing software can improve the efficiency and quality of the farmland information acquisition, reduce costs and the difficult work.

References 1. Weidong, Z.: Research on GPS and GIS Applied in Precision Agriculture. Guangming Daily Press, Beijing (2009) 2. Yang, Q., Pang, S., Yang, C., et al.: Variable rate irrigation control system integrated with GPS and GIS. Transactions of the CSAE 22(10), 134–138 (2006) (in English with Chinese abstract) 3. Devlin, G.J., McDonnell, K.P., Ward, S.M.: Performance accuracy of low-cost dynamic non-differential GPS on articulated trucks. ASABE Journals, Applied Engineering in Agriculture 23(3), 273–279 (2007)

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4. Yang, Q., Zhang, Z.P.S., et al.: Field vehicle position monitoring system based on GPS and GIS. Transactions of the CSAE 20(4), 84–146 (2004) (in Chinese with English abstract) 5. Stoll, A., Kutzbach, H.D.: Guidance of a Forage Harvester with GPS. Precision Agriculture 2, 281–291 (2000) 6. Smith, L.A., Thomson, S.J.: GPS Position Latency Determination and ground speed calibration for the SATLOC M3. ASABE Journals, Applied Engineering in Agriculture 21(5), 769–776 (2005) 7. He, Y., Fang, H., Feng, L.: Information Processing System for Precision Agriculture Based on GPS and GIS. Transactions of the CSAE 18(1), 145–149 (2002) (in Chinese with English abstract) 8. Wang, X., Wang, X., Wang, C., et al.: Application test of soybean sowing by variable fertilization seeder in Heilongjiang reclamation areas. Transactions of the CSAE 24(5), 143–146 (2008) (in Chinese with English abstract) 9. Zhuang, W., Wang, C.: The Arithmetic of DGPS Guidance for Agriculture Machinery Straight Line Operation. Journal of Heilongjiang Bayi Agriculture University 18(6), 50–53 (2006) (in Chinese with English abstract) 10. Zhuang, W., Yang, C., Wang, C., et al.: Agriculture Machinery Operation Guidance System Based on GPS and GIS. Journal of Heilongjiang Bayi Agriculture University 20(5), 32–34 (2008) (in Chinese with English abstract)

Research on Fertilizer Efficiency of Continuous Cropping Greenhouse Cucumber Based on DEA Model Xiaohui Yang1, Yuxiang Huang2, Shuqin Li1,*, and Sheng Huang2 1

College of Information Engineering, Northwest A&F University, Yangling, China 2 College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China [email protected]

Abstract. Fertilizer efficiency directly affects the production of continuous cropping greenhouse cucumber. So analyzing the fertilizer efficiency is of great significance in providing decision-making support for fertilization of greenhouse cucumber. By establishing the DEA models of continuous cropping greenhouse cucumber and taking existing data samples as study objects, the problems of fertilizer efficiency in traditional fertilization, target yield fertilization and mathematical model fertilization were studied. And the technical efficiency, pure technical efficiency, scale efficiency and redundancy rates of the three types of fertilization methods have been obtained by analyzing the relationship of three inputs (N, P2O5, K2O) and one output (Yield). Through analyzing fertilizer efficiency of the three types of fertilization methods, it could get the sort by the size of mathematical model fertilization, target yield fertilization and traditional fertilization. The results showed that the method of mathematical model fertilization is more reasonable in fertilizing and the DEA method is efficient and practical in analyzing the fertilizer efficiency. Keywords: Greenhouse cucumber, Fertilization, Efficiency, DEA method.

1 Introduction Due to the limited understanding, the dependence on experience and the lack of the guidance of scientific fertilization theory, problems, such as excessive fertilization and low fertilize efficiency, are universally existent in production practice of greenhouse cucumber in China. Meanwhile, owing to the higher economic benefits of planting greenhouse cucumber, the pursuit of high yield and utilization ratio of installations, the continuous cropping of greenhouse cucumber is fairly common. But continuous cropping causes many problems, such as soil nutrient imbalance and deterioration of soil properties. Fertilizing reasonably and balancing the soil nutrient are the fundamental ways to improve the fertilizer utilization ratio, reduce pollution, and solve the problem of continuous cropping in sunlight greenhouse. So the guidance of scientific *

Corresponding author.

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fertilization theory should be needed to avoid fertilizer over-using and fertilizer improper-applying. Data Envelopment Analysis (DEA) is an effective optimization method used for measuring the relative efficiency of Decision Making Unit (DMU) (Charnes, A., et al., 1978). C2R model and BCC model are the two most commonly used DEA models. Suppose there are n DMUs, and each DMU has m inputs and k outputs, then the technical efficiency(TE), pure technical efficiency(PTE), scale efficiency(SE), redundancy rates(RR) of each DMU can be calculated by using the C2R model and BCC model (Quanling Wei ,et al., 2004; Shoukang Qin, et al., 2003). In this paper, the fertilize efficiency analysis models of continuous cropping greenhouse cucumber were established. Based on the samples from [1] (Liying Wang, et al., 2008) and DEA method (Charnes, A., et al., 1978), the fertilization efficiency of input and output of three fertilization methods (mathematical model fertilization, target yield fertilization and traditional fertilization)were studied from the views of TE, PTE, SE and RR. Where TE and PTE reflect the relation between fertilizer rate and the yield of greenhouse cucumber under the assumption of that the scale benefit is constant and mutative, respectively. SE is the ratio of TE to PTE. And RR is the ratio of input slacks to the actual input. And the efficiency of three fertilization methods were compared and analyzed. This paper aims at finding out methods to improve the fertilization efficiency of greenhouse cucumber and provide information for fertilization decision-making and practice.

2 Materials and Methods 2.1 Data Resources In this paper, six 3-years continuous cropping plots were used for example to analyze the fertilize efficiency of greenhouse cucumber with the help of DEA Solver3.0 software. And the data of three inputs (N, P2O5, K2O ) and one output (Yield) from [1] were took as samples. The calculation formulas for the fertilizer rate of mathematical model fertilization and target yield fertilization are available from [5] (Lunshou Chen, Jingling Lu, 2002) and [6] (Yancai Zhang, et al., 2005). 2.2 Hypotheses To simplify, the fertilize efficiency analysis models of continuous cropping greenhouse cucumber were established under two hypotheses as follow: (1) N, P2O5, K2O are the major factors that affect the yield levels of greenhouse cucumber. (2) For different planting years and seasons, soil nutrient of different plots is the same. 2.3 Procedures The C2R model and BCC mode were used to establish the fertilize efficiency analysis models of continuous cropping greenhouse cucumber. For each plot, its TE, PTE, SE

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and RR were evaluated and analyzed. Each field (DMU) has three inputs (N, P2O5, K2O) and one output (yield). If θ =1 ( θ is the score of fertilizer efficiency), it indicates that the field (DMU) is a DEA effective unit, and its TE or PTE reach the highest level. If θ <1, it declares that the field (DMU) is not a DEA effective unit and its input of fertilizer is unreasonable. For a non DEA efficient (C2R or BCC) DMU, its input and output slacks and RR can be calculated by using the projection principle and related formulas. Comparing the value of θ of the same field which was under the same continuous cropping years and three types of fertilization, Fig. 1. The frame of analysis model for the greenhouse it can find out that which cucumber input-output efficiency type of fertilization is more reasonable or the unreasonable reasons of the irrational one. By comparing the RR of the same field, it can be analyzed how much input of N, P2O5, K2O can be saved and which input should be adjusted. Besides, the fertilize efficiency of the same field under different types of fertilization and different continuous cropping years can be compared. The analysis steps were showed in the Figure 1.

3 Results and Discussion By calculating the TE, PTE, SE and RR of greenhouse cucumber of three types of fertilization, the results were reported in table 1.In addition, the fertilization efficiency of three types of fertilization was described in the figure 2, figure 3 and figure 4. 3.1 The Fertilization Efficiency without Considering the Diversity of Fertilize Methods According to the table 1 and regarding the technical efficiency, there are 4 DEA effective units, accounting for 22.22 percent of the total samples. And the technical efficiency of 9 units exceeds the average of the samples’ TE which is 0.6826, about half of all. To the pure technical efficiency, there are 7 DEA effective units, accounting

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for 38.89 percent of the total samples. And the pure technical efficiency of 14 units outperforms the average of the samples’ PTE which is 0.8579, about 77.78 percent of all. And to the scale efficiency, there are 4 DEA effective units, accounting for 22.22 percent of the total samples. And the scale efficiency of 9 units exceeds the average of the samples’ SE which is 0.7734, about 50 percent of all. Table 1. The analysis results of fertilization efficiency of the continuous 3 years cropping greenhouse cucumber

Traditional fertilization

Target

yield

fertilization

Mathematical model fertilization

Serial Number

Technical

Pure Technical

Scale

of Field

Efficiency

Efficiency

Efficiency

Redundancy Rates (%) P 2O5

K2O

0.6771

0.7570

48.74

48.74

48.74

0.2123

0.4111

0.5160

78.77

82.63

78.77

0.2331

0.4400

0.5300

76.69

85.69

76.69

4

0.4330

0.5455

0.7940

56.70

56.70

69.88

5

0.8820

1

0.8820

11.8

31.72

61.00

1

0.5126

2 3

N

6

1

1

1

0

0

0

1

0.8328

0.9839

0.8460

22.42

0

16.72

2

0.5234

0.9802

0.5340

47.66

47.66

47.66

3

0.4746

0.8753

0.5420

52.54

52.54

52.54

4

0.5915

0.8616

0.6860

40.85

40.85

40.85

5

0.7266

0.9017

0.8060

27.34

0

27.34

6

1

1

1

0

0

0

1

1

1

1

0

0

0

2

0.6467

1

0.6467

35.33

0

35.33

3

0.5589

0.8716

0.6410

44.11

0

44.11

4

0.6846

0.8952

0.7650

31.54

31.54

34.92

5

0.9746

1

0.9746

2.54

0

25.53

6

1

1

1

0

0

0

Fig. 2. The pure technology efficiency of the continuous 3 years cropping greenhouse cucumber

Fig. 3. The scale efficiency of fertilizer-output of the continuous 3 years cropping greenhouse cucumber

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Regarding the average of efficiency, the average of pure technical efficiency is the maximum (0.8579), the scale efficiency second, and the technical efficiency the minimum (0.6826). And the average of redundancy rates of N, P2O5, K2O is 32.06%, 26.56% and 36.67% respectively, which can led to the preliminary inference that the unreasonable degree of the inputs was in the increasing order of P2O5, N, K2O.

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Fig. 4. The technical efficiency of the fertilizer-output of he continuous 3 years cropping greenhouse cucumber output

3.2 The Fertilization Efficiency with the Consideration of the Diversity of Fertilize Methods In the table 2, the fertilization efficiency of three types of fertilization was analyzed. According to the figure2, figure3 and figure4, the average of TE, PTE, SE of mathematical model fertilization are obviously highest (0.8108, 0.9611, 0.8379, respectively) among that of target yield fertilization and traditional fertilization. And the fertilization efficiency of target yield fertilization is the second highest with that of traditional fertilization following behind. Furthermore, the average of redundancy rates of the three fertilize methods were also showed in table 2. Obviously, the average of redundancy rates of N, P2O5, K2O were the lowest (18.92%, 5.26%, 23.32%, respectively) while using mathematical model fertilization. And the average of redundancy rates of target yield fertilization comes next while that of traditional fertilization turns out to be the maximum. Table 2. The comparison of the fertilization efficiency of three fertilize methods

Traditional Fertilization Target yield Fertilization Mathematical Model Fertilization

The Average of TE

The Average of PTE

The Average of SE

0.5455 0.6915 0.8108

0.6790 0.9338 0.9611

0.7465 0.7357 0.8379

The Average of Input Redundancy Rates (%) N P2O5 K2O 45.45 50.91 55.85 31.80 23.51 30.85 18.92 5.26 23.32

4 Conclusions The fertilizer efficiency of continuous cropping greenhouse cucumber was researched by using DEA. Based on two assumptions (N, P2O5, K2O are the major factors that

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affect the yield levels of greenhouse cucumber. For different planting years and seasons, soil nutrient of different plots is the same) and the analysis of technical efficiency, pure technical efficiency, scale efficiency and redundancy rates of three types of fertilization methods, it can come to conclusion that the fertilizer efficiency of mathematical model fertilization is the highest, and the second is target yield fertilization(the exception was that the average of SE lower than that of traditional Fertilization) while the traditional fertilization is the lowest.

References 1. Wang, L., Zhang, Y., Zhai, C., et al.: Effect of Balanced Fertilization on Yield and Quality of Sunlight Greenhouse Cucumber and Soil Characteristics under Continuous Cropping. Chinese Journal of Eco-Agriculture 16(6), 1375–1383 (2008) 2. Chrnes, A., Cooper, W.W., Rhodes, E.: Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 429–444 (1978) 3. Qin, S., et al.: Principle and Application of Comprehensive Evaluation. Publishing House of Electronics Industry, Beijing (2003) 4. Wei, Q.: Data Envelopment Analysis. Science Press, Beijing (2004) 5. Chen, L., Lu, J.: Fertilizer Application Technique on Vegetable Nourishment. China Agriculture Press, Beijing (2002) 6. Zhang, Y., Zhai, C., Li, Q., et al.: Study on the Optimum Fertilization Technology of Cucumber. Journal of Hebei Agricultural Sciences 9(1), 75–78 (2005)

Design and Implementation of Crop Recommendation Fertilization Decision System Based on WEBGIS at Village Scale Hao Zhang1, Li Zhang1, Yanna Ren1, Juan Zhang1, Xin Xu1, Xinming Ma1,2,*, and Zhongmin Lu3 1

College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China 2 College of Agronomy, Henan Agricultural University, Zhengzhou 450002, China 3 Hua County Agricultural Technology Popularization Center, Hua county 456400, China [email protected], [email protected]

Abstract. To study crop recommendation fertilization of the rural farmers as the main in our country, the paper took towns and villages of Hua County as the study area, took recommendation fertilization of wheat, maize and peanut as the study object, designed model components of crop balance fertilization by using Object-Oriented technique, and developed the decision-making system about crop recommendation fertilization based on ArcGIS Server at village scale. The decision-making system realized farmland nutrient management and fertilization recommendations decision-making according to soil output capacity, agricultural production level and crop target yield. It was successfully applied in crop production in Hua County. The research results show that the system has the characteristic of better expansibility than before, and it is significantly simple and practical to reduce crop production cost and increase agricultural production efficiency, which provides technical support for crop fertilization decision-making and is significant to improve agricultural ecological environment and increase the comprehensive production capacity of farmland. Keywords: Nutrient balance model, Fertilization decision-making, Crop, WEBGIS, Village scale.

1 Introduction Soil testing and balance fertilizer is one of the important technologies of precision agriculture, and also currently the development direction of scientific fertilization in agriculture production[1-3]. Precision fertilization is the optimized combination of information technology (RS, GIS and GPS), biotechnology, chemical technology and mechanical technology, and it can be divided into basic fertilizing and aftermanuring according to crop growth period, arable farming and broadcast application according * Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 357–364, 2011. © IFIP International Federation for Information Processing 2011

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to fertilization methods, real-time and non real-time precision fertilization according to the timeliness. Currently, using of modern information technology to build decision-making system of precision fertilizer for changing the traditional fertilization custom is the research hotspot of precision fertilization. Many scholars has researched and developed GIS-based precision fertilizer management systems and decision-making systems mostly taking farmland as object[4-5], but ignored the status of farmers as the main farming in the village of China. The developed system has limitations of regional application, and it led to complex management, high cost, high pollution and difficult popularization. So, it is significant to realize a simple, expansible and practical system for lowering system complexity, reducing crop production cost and strengthening system pervasiveness, in order to increase agricultural production efficiency, improve agricultural ecological environment and enhance agricultural comprehensive production capacity. With the development of modern information technology, ArcGIS Server is lowcost, complete and efficient. The paper took crop fertilization decision-making at administrative village scale as research object, collected soil information and map village-level vector by ArcView, then constructed crop recommendation fertilization decision-making system based on ArcGIS Server and implemented village-level farmland nutrient management and on-line fertilizer recommendation. It facilitated on-line soil information query and fertilization decision-making, and provided technical support for the scientific fertilization.

2 Data Source and Research Method 2.1 Data Source Research data included meteorological data, soil data and crop data. Meteorological data was made up of daily maximum temperature, daily minimum temperature, daily average temperature, daily rainfall and daily sun actual exposure hours, provided by the Weather Bureau of Henan Province. Soil data was consist of soil type, soil nutrients and soil texture, collected from the Henan TuRang DiLi[6] or provided by the county soil and fertilization station. Crop data was consist of crop species, crop varieties, planting region, planting area, yield per hectare, the annual production and the multiple cropping index, collected from the Statistical Yearbook of Henan Province. 2.2 Research Method Taking into account the decentralized status of crop fertilization management in village and the traditional hand fertilizing feature with farmers as the main farming, the paper took administrative villages in Henan Hua County as the study region, food crop (wheat and maize) and cash crop (peanut) decision-making fertilization as the study object, designed the components of nutrient balance model and fertilization decision-making model by using componentware technology[7-9], and implimented the system of on-line soil nutrient management and fertilization decision-making recommendation by using .NET Framework, ArcGIS Server and network database.

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2.3 Nutrient Balance Model and Fertilization Decision-Making Model Generally, crop fertilization models included ASI nutrient grading model[10-11], nutrient balance model[12] and subtraction method of soil fertility [13]. In view of the status of farmer as the main farming in China, decentralized farmland division, diverse soil type and different soil fertility, so, it was very difficult to extract all plots’ nutrients accurately. The paper extracted soil nutrient information of the representative point at all directions in the villages, and gave fertilization scheme by using nutrient balance model and fertilization decision-making model. The given fertilization scheme included crop target output, fertilizer recommendations and fertilization technologies recommendations. Fertilizer recommendations included fertilizer selection and main nutrient content conversion of organic fertilizer. Fertilization technologies recommendations included when and how to fertilize. In practice, in order to apply fertilizer expediently, the paper only considered N, P and K[14]. Formula 1 shows the balance fertilization model:

M = (U * T − S ) / P / C

(1)

In formula (1): M is fertilization weight per hectare. U is crop absorbable nutrient weight from soil pre 100kg, which is queried from table of crop absorbable nutrient. T is crop target output, which is divided into 3 grades according to the soil output capability. Table 1 shows the target output of wheat, maize and peanut in Hua county. S is soil nutrient supply, which equals to tested soil nutrient value multiplied by 0.15 and effective coefficient of soil nutrient. Table 2 shows the effective coefficient of soil nutrient about wheat, maize and peanut in Hua county. P is crop fertilizer absorptivity in planting season, which is queried from fertilizer classification table, shown by Table 3. C is fertilizer nutrient content, which is also queried from fertilizer classification table. Table 1. Crop target output(kg·hm-2) Crop type Wheat Maize Peanut

High target output 9 000~9 750 9 750~11 250 7 500~9 000

Middle target output 8 250~9 000 8 250~9 750 6 000~7 500

Low target output 6 750~8 250 6 750~8 250 4 500~6 000

Table 2. Effective coefficient of soil nutrient Crop type Wheat Maize Peanut

N 0.30~0.60 0.20~0.60 0.20~0.55

P 0.40~0.60 0.50~0.60 0.50~0.60

K 0.30~0.80 0.40~0.80 0.30~0.70

Table 3. Crop fertilizer absorptivity in planting season Crop type Wheat Maize Peanut

N 0.30~0.45 0.20~0.40 0.40~0.45

P 0.15~0.25 0.10~0.25 0.15~0.25

K 0.15~0.40 0.05~0.40 0.45~0.60

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3 System Design 3.1 System Architecture

System adopted three-tier structure: Brower/ ArcGIS Server/DBMS(application layer, logic components layer and data layer). Fig.1 shows the system architecture.

Fig. 1. System architecture

Data layer provides data service for logic components layer. The logic components layer is based on data layer and provides logic service for system application layer. It is consist of the balance fertilization model component and precise fertilization technology model component, designed and implemented by using C # and relatively independent from each other. Application layer is based on logic components layer and made up of the module of model management and the module of farmland and crop management. Model management function includes importing and exporting models, rule revise and parameter adjustment. Farmland and crop management function includes farmland information management, cultivated land fertility evaluation, crop high yield region and grain core region construction. Based on the unified database interface, data storage and operation are done through MS SQL Server 2005 and Arc SDE. The components of balance fertilization model and precise fertilization technology model are designed and implemented by using C # and are loosely integrated with ArcGIS 9.3 platform, where various thematic analysis and release are realized by using ASP.NET and ArcGIS Server. 3.2 System Function Structure

System function included data management, information statistic and summary, balance fertilization model management, fertilization recommendation and print, special

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topic analysis and release, user roles management and systems help. Fig. 2 shows the system function structure. Data Management. It included spatial and attribute data. Spatial data was made up of all types of point, line and plane vectorgraph. Attribute data was made up of meteorological data, soil data and crop data. Information Statistic and Summary. It included vectorgraph and attribute query, statistic and summary for all kinds of information. Crop Fertilization Model Management. Crop fertilization models included crop nutrient balance model and fertilization decision-making model. The management function was made up of model import and export, rule revise and parameter adjustment. Fertilization Recommendation and Print. According to monitoring point nutrient information and crop output target, system gave and printed the unit fertilization weight and recommendation technology. Special Topic Analysis and Release. It included the topic analysis and release of microelement(Cu, Ca, Mn), macroelement(N, P, K) and organic matter. User Rights Management. It distributed authority of user role and added, deleted, updated, and queried user information. 3.3 Database Design

System database structure was made up of spatial database, attribute database, model library and knowledge library, shown in Fig. 3(a). Spatial database was built by using ArcView in scale of 1:20,000, and was consist of town point and line vectorgraph, administrative village point and line vectorgraph and soil monitoring point vectorgraph, shown in Fig. 3(b).

Fig. 2. System function structure

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(a)System database structure

(b)Spatial database

(c)Attributte database

Fig. 3. System database design

Attribute database was built by using E-R model, and was consist of administrative division data, farmland data, soil monitoring point data and crop cultivation and harvest data. Administrative division was made up of towns and administrative villages. Farmland data included the latitude and longitude, area, soil type, soil productivity level, their villages and their farmers. Soil monitoring point data was made up of soil texture, total nitrogen, alkali-hydro nitrogen, P2O5, K, Organic matter, pH, and so on. Crop cultivation and harvest data included crop type, crop variety, crop planting acreage, crop yield and multiple index. Fig. 3(c) shows attribute database. Model library was made up of the decision-making field ID, model ID, model type, model name, model application conditions, model premise items, units, data type, data length, decimal digits and credibility. Knowledge library was made up of expert fertilization scheme, fertilizer type selection, main nutrient content conversion of organic fertilizer and fertilization technique recommendation.

4 System Implementation and Application Taking crop production in Hua county as an example, the paper collected nutrient information of 3,773 soil monitoring points at all orientations of 976 administrative

Fig. 4. The fertility information interface of crop monitoring points

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Fig. 5. Decision-making fertilization interface

villages in 22 towns, and built soil fertility database. The nutrient balance model component and fertilization decision-making model component were designed by using OO and UML2.0, and WEBGIS-based fertilization decision-making system was implemented by using C #, ASP.NET, ArcGIS Server and MS SQL Server 2005. System may be installed in Win2000/2003/XP on PC or touch screen system. First, farmer may select and query the fertility information of soil monitoring point at 4 orientations of each administrative village, shown in Fig. 4. Second, system buffered and analyzed the selected point, and the nearest soil monitoring point close to the selected point from the buffer was chosen. Third, crop target output was selected according to the level of local production. Finally, the system gave formula fertilization weight decision and fertilization technology decision according to soil monitoring point information, crop target output and local production level, shown in Fig. 5.

5 Conclusion System is developed by following OOAD standard and farmland nutrient balance components and fertilization decision-making model components are customized by using componentware technology. The system has the characteristics of modularization, encapsulation, reusability and inheritance. It can be installed in Win 2000/2003, and is easy and convenient for farmers to query farmland nutrient information and online fertilization recommendation at village scale through network. The system has been applied in Hua county agriculture production and it is popular with farmers. The results show that it lowers the complexity of system management by using componentware technology, and reduces crop production cost and strengthens system pervasiveness by using WEBGIS and crop nutrient balance model and fertilization decision-making model. According to the actual situation of soil fertility distribution, soil type and fertility are different in the same direction. So, it is deficient that one selected soil monitoring

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point represents all small-scale plots’ fertility, even selecting one sampling point in hundreds of acres of the same direction. So, further issue is the selection of soil fertility points at a smaller scale and the analysis of soil nutrient variability. Acknowledgments. This work is supported by "Eleventh Five-Year" national scientific and technological support and major project plan: "High-yield Crop Science and Technology Engineering"(2006BAD02A07-4), specific industry research sponsored by ministry of agriculture(20083028) and ”863” plan(2006AA10Z271). Sincerely thanks are also due to the Hua County Agricultural Technology Popularization Center for providing the data for the work.

References 1. Wang, M.H.: Farmers Need Real Information. IChina 13, 19 (2006) 2. Zhang, H., Li, F.C., Ma, X.M., Gao, R., Xia, B., Lu, Z.M.: Design and Realization of GISBased County Testing Soil for Wheat Formulated Fertilization System. Jour. Hen. Agri. Uni. 5, 566–569 (2008) 3. Zhang, H., Xi, L., Yu, H., Xiong, S.P., Qiao, H.B., Lu, Z.M., Ma, X.M.: Design Of Decision-making System about Wheat Survey and Directions for Soil Based on GIS in County. In: 3th Computer and Computing Technologies in Agriculture, pp. 661–670. Springer Press, New York (2009) 4. Sheng, J.D., Li, R.: An Information System of Soil Nutrition Management and Crop Fertilizer Recommendation Based on GIS. Soils 2, 77–81, 112 (2002) 5. He, L.Y., Miao, J.: Research of Farmland Resource Management and Application Model Based on Web GIS. In: 3th Computer and Computing Technologies in Agriculture, pp. 8– 14. China Agricultural Science and Technology Press, Beijing (2007) 6. Wei, K.X.: Henan TuRang Dili. Henan Science and Technology Press, He Nan (1995) 7. Zhao, C.J., Wu, H.R., Yang, B.Z., Sun, X., Wang, J.H., Gu, J.Q.: Development Platform for Agricultural Intelligent System Based on Techno-Componentware Model. Trans. CSAE 2, 140–143 (2004) 8. Xi, L., Ma, X.M., Li, F.C., Liu, H.B., Ren, Y.N., Li, Y.H.: Design and Implementation of Crop Growth Simulation System Based on Techno-Componentware Model. Jour. Hen. Agri. Uni. 3, 317–320 (2005) 9. Zhang, H.: Research of Crop Production Potential System Based on OMT and Software Component Technique. The PLA Information Engineering University, Zheng Zhou (2009) 10. Yang, L.P., Jin, J.Y.: Study on the Correlation between ASI and Routine Method to Determine Soil Available P, K, Zn, Cu, Mn and the Conventional Chemical Methods. Chinese Jour. Soil. Scie. 6, 277–279 (2000) 11. Xiong, G.Y., Liu, D.B., Chen, F.: Study on the Correlation between ASI and Routine Method to Determine Soil Available P and K and Ammonium Nitrogen. Soil. Fert. 3, 73– 76 (2007) 12. Wang, H.T., Bai, D.P., Chen, M.C.: A Systematic Study on Nutrient Status and Its Application in Balanced Fertilization. Jour. Shan. Agri. Scie. 4, 31–36 (2001) 13. Zhou, S.Z., Dai, Y., Yao, M.Y.: Application of Dissimilar Subtraction Method of Soil Fertility in Recommend Fertilizer Practice for Maize Planting in Yellow Clay Soil. Gui. Agri. Scie. 6, 34–36 (2003) 14. Xie, G.D., Chen, S.H.: Spatial Continuous Variation of Environment and Precision Agriculture, pp. 18–19. Meteorological Press, Beijing (2005)

Research on Influenced Factors about Routing Selection Scheme in Agricultural Machinery Allocation Fan Zhang1, Guifa Teng2,*, JieYao3, and Sufen Dong1 1

School of Information Science and Technology, Agricultural University of Hebei, Baoding 2 Graduate School, Agricultural University of Hebei, PRC [email protected] 3 Institution of Environmental Protection Research of Baoding

Abstract. Routing allocation scheme is a core issue in agricultural machinery allocation. It is related to the allocation cost, income and timeliness of crop harvest. How to select a farmland spot to harvest, which needs to consider not only the distance between a farmland spot and an agricultural machinery spot, but also road condition, farmland area, types of agricultural machinery and weather conditions. It analyzes the main influenced factors of routing selection solution in agricultural machinery allocation and has built an improved hierarchy analysis model about these influenced factors in this paper. For the uncertain influenced factors or influenced factors with incomplete information, it adopts an improved fuzzy hierarchy analysis method to evaluate the factor weights in the paper. And the resolving power is improved and the believable degree of the routing selection solution is increased. The evaluation model has supplied a good scientific basis for further research. Keywords: Agricultural machinery allocation, hierarchy analysis method, fuzzy consistent matrix.

1 Introduction During harvest time, agricultural machinery users choose proper routing and harvest crops for peasants timely and try to reduce allocation cost and obtain more income, according to crop areas, road conditions, distances of path and so on. How to select a farmland spot to harvest, which needs to consider not only the distance between a farmland spot and an agricultural machinery spot, but also road conditions, farmland areas, types of agricultural machinery and some sudden accidents. The optimal routing solution with lower cost and more economic benefits can be chosen under such factors. The reasonable allocation solution is the one with shortest distance, the minimum cost and the least to complete the harvest task. It means the allocation routing from agricultural machinery spot to the object farmland spot is affected by more than one target. These targets may be the maximum profits, the minimum allocation risks, the shortest allocation time and so on. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 365–373, 2011. © IFIP International Federation for Information Processing 2011

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Generally, the choice of multi-objective allocation solution may have conflicts between different targets. For example, allocation time is shorter, while the cost will not be little; or the cost is smaller, but the time will not be shortest one.

2 Description of Allocation Problem China is a large agricultural country which has vast territory, complex geographic environment and different natural conditions. Agricultural production has obvious variances about time and region. Some peasants have reaped crops spontaneously in some areas using combine harvester and already have achieved great economic results. However, there are still many problems in agricultural machinery allocation during busy seasons. The peasants don’t have proper allocation solution, and then they have to waste much time in changing areas, which has bad effects on oil, human, agricultural machinery, and even farm work may be delayed. On the other hand, road conditions, weather and farmland areas should be considered before agricultural machinery allocation. The routing selection for agricultural machinery allocation is based on these influenced factors in this study. This paper mainly analyzes six influenced factors, just as follows: (1)Allocation Time: Crop harvest has timeliness which should be fulfilled in a certain time range. Earlier or later than the time range will increase the allocation costs. So the time is one important factor for agricultural machinery allocation. (2)Farmland Area: The more farmland areas, the more incomes for agricultural machinery owners. (3)Allocation Distance: Different allocation schemes have different distances, and the cost for allocation is determined by the allocation distance directly. The less distance, the less allocation cost. (4)Road Condition: Roads can be divided into several types according to road grades. The different road grade has different influence on allocation time. (5) Price: Different regions have different harvest price in unit areas, which will directly affect the incomes. (6)Weather Condition: Weather can impact the allocation time, and then affect the allocation income.

3 Improved Analytics Hierarchy Process in the Agricultural Machinery Allocation As the complexity of objective things and ambiguity of human thoughts, decision-makers are difficult to determine the attribute weights. The incomplete weights information will lead to the uncertainty of solution selection. One method is to analyze the inherent characteristics of objective data by mathematical programming. And the other one depends on subjective judgment of decision-makers completely. While it may cause errors or inconsistent judgment, and the evaluation result may be easily influenced by the understanding level of decision-makers.

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3.1 Traditional Analytics Hierarchy Process AHP (Analytics Hierarchy Process) is one multi-target decision analysis methodology combining with qualitative and quantitative analysis, proposed by T.L.Satty, an American operational research experts in 1970s. AHP can solve decision-making problems with complex structures, many constrained criterions and uneasily be quantized. It can combine with qualitative and quantitative analysis to convert complicated problems into several levels and several parts, and establish a hierarchy structure model. The difficult decision-making problems transform into factors in the same level comparison with each other and build judgment matrix. The weights values of different influenced factors can be obtained through transforming and calculating fuzzy consistent matrix, and supply a scientific basis for the optimal routing solution. 3.2 Improved Analytics Hierarchy Process Traditional AHP is greatly impacted by human subjective judgment and preferences while making decision. This paper introduces fuzzy consistent matrix in the AHP, and overcomes significant differences between consistency of judgment matrix and consistency of human thinking.

Data preparation

Determination of influenced factors

Establishment of AHP model

Weights calculation and sorting

Establish comprehensive evaluation model

Fig. 1. Flowchart of Influenced factors analysis in the agricultural machinery allocation solution

The sorting method adopted in this paper can increase the resolution of factors important degree, and also can analyze the sensitivity of sorting results with choosing different values for parameter α , which will help decision-makers to make optimal decisions [4]. The flow chart of influenced factors analysis about routing selection in the agricultural machinery allocation is shown in figure1.

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4 Establishment of Comprehensive Evaluation System This paper mainly studies on how to optimize allocation routing from the two sides of allocation costs and harvest benefits. The optimal allocation solution is to achieve the lowest allocation cost and the maximum benefits. These influenced factors of agricultural harvest benefit are the capability of agricultural machinery and farmland areas. The influenced factors of agricultural allocation costs are allocation distances, road condition, and weather condition. 4.1 Establishment of Hierarchy Structure Model After the above discussion in section 2, this paper constructs the following AHP structure model. Optimized Solutions

Harvest Benefits

Price

Total Costs

Farmland Areas

Allocation Distances

Allocation Costs

Weather Condition

Time Costs

Road Condition

Fig. 2. Hierarchy structure model in agricultural machinery allocation

4.2 The Construction of Judgment Matrix in Hierarchy Analysis Traditional AHP quantifies human judgment, builds judgment matrix and calculates weights values by nine-scale representation method. To a great extent, this method depends on human experiences, and it is difficult to eliminate decision-maker’s one-sidedness. In terms of psychological, the classification in the nine-scale representation method is beyond human’s judgment. It increases the difficulties of judgment and can supply unreal data easily. According to the existed shortcomings, this paper defines the weights in evaluation system by three-scale hierarchy analysis method. Table 1. Three-scale representation and meanings

Scale 0 1 2

Meaning Factor i is less important than factor j. Factor i is as important as factor j. Factor i is more important than factor j.

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After building the hierarchy structure, precedence relation matrix are built in accordance with comparison between factors in two levels. Factors in the same level compare with each other, and then build judgment matrix by three-scale method.

⎧A1 A1 A1 A2 ... A1 An ⎫ ⎪ ⎪ ⎪A2 A1 A2 A2 ... A1 An ⎪ D=⎨ ⎬= ... ⎪ ⎪ ⎪ An A An A2 ... An An ⎪ ⎩ 1 ⎭

⎧d 11 d 12 ⎪d d ⎪ 21 22 ⎨ ⎪ ⎪⎩d n1 d n2

According to the meaning of three scale representation,

... d 1n ⎫ ... d 2n ⎪⎪ ⎬ = (d ij ) n×n ... ⎪ ... d nn ⎪⎭ d ij =0, 1, 2

After comparison, the judgment matrix in the first level can be obtained as follow:

⎧1 2 ⎫ D =⎨ ⎬ the judgment matrix in the second level is as follow: ⎩0 1 ⎭ ⎧1 0 ⎫ ⎧1 0 ⎫ D11 = ⎨ ⎬ , D12 = ⎨ ⎬ ⎩2 1 ⎭ ⎩2 1⎭ ⎧1 2 2 ⎫ ⎪ ⎪ The judgment matrix in the third level is as follow: D24 = ⎨0 1 0 ⎬ . ⎪0 2 1 ⎪ ⎩ ⎭ 4.3 Fuzzy Consistent Matrix The traditional AHP method should inspect consistency test of judgment matrix. When it is inconsistent, the judgment matrix should be adjusted to it. So the judgment matrix may be consistent through several times of adjustment and examination. Although some scholars have proposed some improved method, in fact, they are also difficult to realize. There are two ways to research whether the consistency between judgment matrix and human critical thinking have coordination or not. One is the direct method, and the other one is indirect. In order not to make the problems return to be complicated, which already have simplified, and try to make the decision-making process be closed to actual human thinking process, this paper adopts the indirect method. From the viewpoint of information theory, the consistent problem can be solved by fully using the known judgment information through clever mathematical transformation. In fact, it is impossible to assure that the judgment matrix has complete consistency, especially for multi-target problems. However, the requirement should be broadly consistent. The consistent test criterion of judgment matrix is just empirical data and lack of scientific and effective certification. So the fuzzy consistent matrix is introduced to solve consistent test problem in this paper. 4.3.1 Definition of Fuzzy Consistent Matrix Definition 1. If Matrix R = ( rij ) n×n , meets: 0 ≤ called fuzzy matrix[1].

，

rij ≤ 1 , (i,j =1,2,…,n) so R is

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Definition 2. If fuzzy matrix R

，

，

= (rij ) n×n meets ∀i, j, k , rij = rik − r jk + 0.5 so

fuzzy matrix R is called fuzzy consistent matrix[1]. 4.3.2 Conversion of Fuzzy Judgment Matrix After normalized the judgment matrix R = ( rij ) n×n , it sums by rows, which be n

denoted as

ri = ∑ rij . (i=1,2,…,n). The conversion formula is as follow: j =1

rij' =

ri − rj 2n

+ 0.5 . Using it, the matrix R can be converted into fuzzy consistent

matrix D. Compared to the traditional Analytical Hierarchy Process, Fuzzy Analytical Hierarchy Process has the following advantages. (1) Compared with testing whether the judgment matrix has the consistency through calculating its largest eigenvalue and its corresponding eigenvector, it is easier for fuzzy AHP to test whether the fuzzy matrix has the consistency. (2) The fuzzy inconsistent matrix elements can be adjusted to be consistent quickly, and the weak point,which is the complicate judgment process with several adjustment and inspection, can be overcome by Fuzzy Analytical Hierarchy Process method. (3) The matrix consistency criterion of fuzzy AHP is more scientific, accurate and simple than the traditional one. Fuzzy consistent matrix meets consistent condition, so matrix D never need to consistent test. Judgment matrix R is converted into fuzzy consistent matrix D:

⎧0.5 D =⎨ ⎩0.25 ⎧0.5 ⎪ D24 = ⎨0.167 ⎪0.333 ⎩

0.75 ⎫ ⎧0.5 0.25 ⎫ ⎧0.5 0.25 ⎫ ⎬ , D11 = ⎨ ⎬ , D12 = ⎨ ⎬ 0.5 ⎭ ⎩0.75 0.5 ⎭ ⎩0.75 0.5 ⎭ 0.833 0.667 ⎫ ⎪ 0.5 0.333 ⎬ ⎪ 0.667 0.5 ⎭

4.4 Weights of Influenced Factors and Sorting The factors weight value of the level in the fuzzy consistent matrix is calculated by the following formula in the paper [1]. n

∑r

' ij

1 1 j =1 − + ,i=1,2,…,n , n is the amount of factors. α Should meet n 2α nα n −1 α≥ . 2

ωi =

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α is a kind of measure about objects difference degree that perceived by people. This formula has flexibility. The bigger the value of α , the smaller difference of weighs; meanwhile the smaller the value of α , the bigger difference of weighs. When α

=

n −1 , the weight difference can achieve to the biggest value. Therefore, 2

the smaller value of α indicates that decision-makers may pay more attentions on the difference of important degree between elements [3]. After experiment in this paper, the value of α in the first level is chosen as 3, the value of α in the second level is chosen as 4. The weight values can be calculated as follows.

ω = (0.625,0.375) , ω11 = (0.375,0.625) , ω12 = (0.375,0.625)

ω 24 = (0.417,0.250,0.333) 4.5 Foundation of the Comprehensive Evaluation Model The comprehensive evaluation model is based on the hierarchy structure model of agricultural machinery allocation. After comparing the importance of factors and calculating the weight value of factors in the level, sorting is from the bottom up. Then weights value in the evaluation model of the optimal routing solution will be obtained. In accordance with agricultural machinery allocation, the evaluation model is

，

n

founded as follow.

M = ∑ ω i C i . C i stands for ith influenced factor ω i means the i =1

weight value of ith factor to the object in previous level. After discussion, the model is shown in the following tables. Table 2. The influenced factors and weight values of Time Cost

Factor Time cost

Allocation Distance 0.417

Weather Condition 0.250

Road Condition 0.333

Table 3. The influenced factors and weight values of Total Cost

Factor Total Costs

Allocation Cost 0.625

Time Cost 0.375

Table 4. The influenced factors and weight values of Allocation Cost

Factor Allocation Benefit

Farmland Areas 0.625

Price 0.375

Combing with Table 2, Table 3, Table 4, Table 5 and Hierarchy structure model in section 4, the final optimization routing scheme can be expressed in Table 6.

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Factor Routing Optimization Scheme

Allocation Benefit 0.625

Total Costs 0.375

Table 6. The influenced factors and weight values of Routing Optimization

Factor Routing Optimization

Farmland Areas 0.391

Price 0.234

Allocation Distance 0.293

Weather Condition 0.035

Road Condition 0.047

So Routing Optimization= 0.391 × farmland areas + 0.234 × price+ 0.293 × al-

location distance + 0.035 × weather condition + 0.047 × road condition. Seen from the above tables, the influenced weights of road condition, allocation distance, machinery types, farmland areas and sudden accidents, which are achieved by methods in the paper, supply a good theoretical support.

5 Conclusions This paper mainly discusses the influenced factors in agricultural machinery allocation, and analyzes the effects that the factors by improved AHP method under the circumstances of uncertain influenced factors or influenced factors with incomplete information. The formula for calculating and sorting the weight values of factors, which is adopted in this paper, can supply higher resolution when amount of factors is big and has better flexibility and practicality. This paper proposes a good decision-making idea for routing solution about agricultural machinery allocation. And it also supplies a better scientific basis for further research.

References 1. Lv, Y., Guo, X., Shi, W.: A New Way for Improving Consistency of the Fuzzy Complementary Judgment Matrices, vol. 16, pp. 55–59 (2007) (in Chinese) 2. Zhan, J.: The Research on Scale in AHP. Guangxi University (2005) (in Chinese) 3. Lan, J.: Research on Priorities of Analytic Hierachy Process and Problems of Fuzzy Multiple Attribute Decision Making. Southwest Jiaotong University (2006) (in Chinese) 4. Wang, Z.: Research on Evaluating Urban Expressway Construction Risk Based on Fuzzy Analytic Hierarchy Process. Tianjin University of technology (2009) (in Chinese) 5. Zhu, W., Hu, A.: Emergency Logistics Selecting Scheme with AHP. Logistics Technology 3, 45–47 (2005) (in Chinese) 6. Chu, M.: The construction problems of Judgment Matrices in Analytic Hierarchy Process. Nanjing technology University (2005) (in Chinese) 7. Singh, G., Pathak, B.K.: A decision support system for mechanical harvesting and transportation of sugarcane in Thailand. Computers and Electronics in Agriculture 11, 173–182 (1994)

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8. Hetz, E.J., et al.: Optimization of Machinery System. Agricultural mechanization in Asia, Africa and Latin America 17(1), 68–76 (1986) 9. Edwards, W., Boehlje, M.: Machinery selection considering timeliness losses. Transactions of the ASAE 23(4), 810–815, 821 (1980) 10. Singh, Dvindar, Holtman, J.B.: An heuristic agricultural field machinery selection algorithm for multi-crop farms. Transactions of the ASAE 22(4), 763–770 (1979) 11. Rotz, C.A., Muhtar, H.A., Black, J.R.: A multiple crop machinery selection algorithm. Transactions of ASAE 26(6), 1644–1649 (1983) 12. Wu, J.: Study on some problem in multiple attribute decision making based on interval complementary judgment matrices. Southwest Jiaotong University doctor degree dissertation (2004) (in Chinese) 13. Liang, X., Yin, C.: A Method of Determining Fuzzy Matrix Weight. Journal of Xi, an University of Arts & Science (Natural Science Edition) 1 (2010) (in Chinese) 14. Zhan, J., Lu, Y.: Least Square Method for Sequencing Judgment Matrix of Interval Numbers. Journal of Huaqiao University (Natural Science) 1 (2005) (in Chinese) 15. Zhang, Q.: Research on Performance Appraisal of Green Supply Chain with fuzzy AHP. Tianjin Normal University (2009) (in Chinese)

Research on Informationization Talented Person Training Pattern of the Countryside Area in China Yang Wang1 and Zhongwei Sun2,* 2

1 Institute for Tourism Studies, Hebei Normal University, 050031 Shijiazhuang, China Department of Resource & Environment, Shijiazhuang College, 050035 Shijiazhuang, China [email protected]

Abstract. In profits from the domestic and foreign successfull experience fully, this paper creates an informationization talented person training pattern of the countryside area in China which includes the leadership training stage, the information officer training stage and the populace popular stage, and explains the training main body, the training object and the training way and so on. In order to carry on this pattern healthily and effectively, this paper proposes six big principles that should be persisted, namely the principle of adjusting measures to local conditions and the prominent characteristic, innovation methods and the principle of stressing practical results, combination of regular and nonperiodical training, centralism training and long-distance training, participation of international society and the relevant enterprise, schools and students. Keywords: Countryside area, Countryside informationization, Talented person training, Pattern, China.

1 Introduction The countryside informationization refers to realize popular applying degree and the process about the modern information technology in countryside production circulation, management and operation, culture and education, social management, public service and so on through strengthening countryside information infrastructure constructions, such as broadcasting television network, telecommunication network and computer network, using and exploiting information resources to the full and constructing the information service system to promote information communication and knowledge sharing (Li,2009). The countryside informationization construction in China has made the remarkable progress. China will realize the goal that 100% of administrative villages can telephone to each other and 100% of villages and towns can access the internet in 2010. Up to the point, the countryside informationization in China, especially the network infrastructure construction has taken initial shape, the information supplies mechanism of government and the telecommunication enterprise was consummating gradually. At the same time, web cam scale of China’s countryside has achieved 106,810,000 and the popular rate enhances to 15%. At present, how to train to the web cam and the non-web cam in the countryside area and enhance *

Corresponding author .

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their information accomplishment to dock informationization supplies effectively is becoming a key question of countryside informationization.

2 Informationization Personnel Training Pattern of the Countryside Area in China The informationization personnel training in China’s countryside area must experience three stages approximately which both to distinguish mutually in the content and to intercross in the time, namely the leadership training stage, the information officer training stage and the populace training stage (to see Fig.1).

Fig. 1. The informationization personnel training pattern in Chinese countryside area

2.1 Leadership Training Stage The leadership training stage belongs to the first stage of the informationization personnel training in countryside.Because the time of informationization construction in Chinese countryside is not long, and the renewal of the information technology is very quick, new objects and situations appear constantly in the construction process. The administrative cadre of countryside informationization which works for the government department should often be trained. The main body of state-level training is the National Ministry of Agriculture, the Industry and the Informationization Department, the main object is the leading cadre of countryside informationization which are above the level conforms to the training request of national city or county, and the way is unifies centralism training, the training expense is undertaken by the country. Such as the leading cadre training class of national countryside which was conducted in Weihai by the Industry and the Informationization Department in October, 2008

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and the construction and development training class of agricultural countryside informationization which was conducted in Beijing by the Ministry of Agriculture in October, 2009, they have trained respectively different ranks of leadership. The main body of provincial level training is the related department in provinces, the object is the basic unit informationization staff in information manages of the counties, the towns and villages with far teaching to manage and so on, the main way is also unifies centralism training. Through the training, the countryside informationization staffs which work for the government can increase their information accomplishment effectively, then they will have the ability to direct the practice of local countryside informationization in a scientific way. 2.2 Information Officer Training Stage The information officer training stage belongs to the second stage of countryside informationization personnel training, and is also the main body of the countryside informationization training in China at present. The information officer who can provide the agriculture information service in the countryside and the main item enterprise of industrial production, the wholesale market of agricultural product, and the staff in the intermediary organization. The countryside information officer is the bridge and ligament in connecting the information service organization in basic unit of agriculture and farmers, and to strengthen the construction of countryside information officer troop is the effective way to solve the question about “the last kilometer” in information service. The main body of information officer training stage can be organized by the provinces and cities level, such as the information officer's training in informationization demonstration village which belongs to provincial and municipality level in informationization preliminary stage, but mainly be organized and implemented by the County Agricultural Bureau. Besides all levels of administrative department, it may mobilize the domestic telecommunication operation business and the overseas IT enterprise fully (for example Microsoft, AMD and so on), as well as the positive participation of European Union and International Organization. The key of each place must focus on the countryside information service manager, the kind of person which raises wealthy and powerful family, the main item enterprise in agricultural industrial production, the wholesale market of agricultural product, the intermediary organization as well as to develop the countryside information officer in cadres of village and group, and to carry on through fixed-point centralism training and the modern distance learning and other ways auxiliarily. At last, the countryside information officer who has finished the training and verification will be awarded the countryside information officer credentials which are inspected by the province agriculture department (bureau, committee) and printed uniformly by the Ministry of Agriculture. 2.3 Populace Popular Stage The populace popularize stage is the last stage of countryside informationization personnel training, and is also the symbol of countryside informationization construction which has reached the maturity. The training of this stage can be arranged by the locally administered level and implemented by the organization in counties, towns and villages. The objects of training are all inhabitants, the key point is people who have the good accepting ability and the degree of education above the junior middle school. The training way may be mainly carried on through the countryside

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information officer in information service station of villages and small towns, and the big or small information boxcar is also the quite good way in addition. The big information boxcar is the private car which is made to implement countryside informationization knowledge training and popularize countryside computer knowledge training through science, technology, information and the application training in a body by the Information Industrial Department. There are 15 computers, projecting apparatus and printer and so on in the vehicle, it has the wired and the wireless surfer function, and is a mobile technology training classroom and the information service station. The scale of small information boxcar must be small and be equipped with 1 driver, 1 propaganda work personnel, as well as notebook computer, projecting apparatus and turning on equipment of internet in common. The function of big or small information boxcar is especially remarkable, especially in districts of imperfect construction of information service station and inconvenient area of the personnel fixed point centralism training.

3 Basic Principle of Informationization Personnel Training of Countryside in China 3.1 Principle of Acting as Circumstances Permits with the Prominent Characteristic In the informationization personnel training of countryside in China, it must persist throughout acting as circumstances permit with the prominent characteristic principle. Firstly, the national territory in China is vast, there are big differences in terrain landform, informationization foundation, economy level of development, population characteristic, scientific cultural quality in the east, the middle, the west as well as the northeast area, therefore it needs to choose various suitable ways of informationization personnel training in provinces according to their own situation. Secondly, even if in the same province, it also has the big difference among local countrysides, and it also needs to make informationization personnel training plan according to act as circumstances permit formulation. Besides acts as circumstances permit, in this local countryside informationization personnel training, it may highlight its own characteristic, this characteristic itself is also the result of acting as circumstances permit to formulate the policies and measures. Only to insist on the acting as circumstances permit with the prominent characteristic principle, it can discover and determine the personnel training pattern which mostly suits the local countryside informationization, and obtain the desired effect in practice. 3.2 Principle of Innovation Ways and Emphasizing Actual Effect The information society is a new form of society. A lot of things are totally new which include the informationization construction in countryside and many other things, therefore we should carry on our work creatively. The training pattern of the informationization personnel needs be innovated constantly with the help of the successful experience of the foreign countries. The concrete innovation way may include the training main body, the object, the way, the place and so on. For example, it can develop the training project of computer study by 1,000,000 farmers in the province territory, organize ten thousand famous popular science volunteers, and take some

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months to train hundreds of thousands of network popular science model households; it can use information boxcar, the modern distance learning classroom for party member in the countryside, the basic unit party school in the village, the informationization training center and the professional skill training school in the county and the government conference room as the training place. It must be stressed that the innovation of informationization personnel training way in countryside must certainly pay great attention to the actual effect. The informationization construction in countryside is not “the flower trellis”, “the achievements project”, it is the important safeguard of constructing the new socialist countryside and harmonious society, and is also the request of advancing the countryside reform, increasing farmer’s income, constructing the modern standardization agriculture, enhancing the competitiveness of the agriculture. 3.3 Principle of Participation of International Society and the Relevant Enterprise The informationization construction and the personnel training in countryside are the matters of the entire society, and is not merely the business of government department; therefore it needs the participation of the international society, telecommunication operation business and the IT enterprise. The international organization has the superiority about talented person and the construction experience, but the telecommunication and the IT enterprise have the superiority of fund and technology. At present, the European Union is the main international organization that participates in the countryside informationization. It helps to carry on the informationization training in countryside through the Chinese - European Union information society project with the international successful cases and the advanced experience which they have grasped. The telecommunication operation business in China is the concrete executor who provides the informationization infrastructure construction as well as the service, and it affects significantly in informationization construction and the personnel training in countryside. In addition, international famous IT Corporation also may help to carry on the informationization construction and training in countryside. For example, The Microsoft has established the demonstration project of informationization synthesis service experiment site in countryside in 5 provinces, has donated 15 information boxcars, has established 15 information service centers and 6 training bases of comprehensive information, and has trained more than 6000 farmers and the basic unit government cadre. The AMD Corporation plans to construct 7 training centers of synthesis information service in countryside and the first has been established officially in Shandong Province at present. 3.4 Principle of Positive Participation of Schools and Students It should display the superiority of schools and students and enhance the training result in the informationization personnel training in Chinese countryside. Firstly, it must fully mobilize the initiative of the universities, colleges and institutes to participate in the informationization personnel training in countryside. These universities, colleges and institutes have the sufficient multimedia equipment, may take the centralism training spot on weekend and vacation; They have a lot of teachers who have grasped advanced knowledge, some even have pursuesd advanced studies in the information technology, the informationization research aspect. They may play the role of experts or ordinary personnel in training; They have tens of thousands of students,

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which may be competent to be the informationization popular science volunteers after simple training, and some prominent persons also may be competent to be the ordinary personnel. Secondly, it should make full use of the multi-purpose halls and the teacher’s microcomputer room resources in general middle school and the school of professional skill training to train farmers by stages and in groups which will guarantee both the centralism training and operation of the computer personally. Considering the slightly shortage of the hardware condition in some middle schools and the school of professional skill training, it will be very good if the government can provide some funds and work together with schools on hardware construction. This can not only guarantee the equipment condition of the informationization personnel training in countryside, but also can enhance the teaching level of the school, in fact it can also enhance the current and latent inhabitant’s information accomplishment in countryside. 3.5 Principle of Unifying the Regular and Non-periodical Training In the previous two stages of informationization training in countryside, because it is a new thing to the government personnel or the information officers in countryside, therefore it is necessary for them to grasp the elementary knowledge and the skill through regular training. In the Populace popular stage, the higher authority department also must aim at the new phenomenon, the new question which appears in the informationization construction in countryside, and conducts the centralism training class non-periodically, carries on enhancement training to the government personnel and the information officer in countryside. As for ordinary populace's informationization training, it may unify the big or small information boxcar and all levels of information service station situation, as well as training object time to develop regular or non-periodical training. In addition, it also may carry on non-periodical training to the training object according to the training material which is uploaded in network. 3.6 Principle of Unifying Centralism Training and Long-Distance Training Fixed-point centralism training is the main mode of informationization personnel training in countryside at present, and the long-distance training in network will be the latent way of personnel training from now on. Under the formerly limited condition of network transmission band width, it only could adopt the way of fixed-point centralism training, moreover the centralism training had the superiority of taking the face-to-face communication with the principal speaker, the training effect is better. To the training of informationization staffs who work for the government department as well as the information officer in countryside, it may train mainly in a unified way. Along with the extention of the information infrastructure and the maturity of technology, we have gradually obtained long-distance training condition. Long-distance training has the prominent advantages such as saving the training resources, the broadly popularization, being duplicate trained and so on, it especially suits the populace popular stage which acts as the training terminal, as well as the centralism training that inconvenient because of far away or vast territory with a sparse population area. We should emphasize that it will be able to get up the twice the result with half the effort effect if the long-distance training is carried on auxiliarily by information officer's on-the-spot guidance.

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4 Conclusion The infrastructure construction of countryside informationization in China has begun to take shape, how to enhance the countryside inhabitant's information accomplishment through the personnel training by the effective contract government informationization supplies is becoming a key question of countryside informationization construction in China. With the help of the successful experience of our own country and foreign ones, we propose a training pattern of informationization talent of countryside areas in China which includes the leadership training stage, the information officer training stage and the populace popularizes stage, and specifically point out the main training body, training object and training way on each stage. In the process of realizing this pattern, we must persist to act as circumstances permit with the prominent characteristic principle, the innovation way and the principle of emphasizing the practical effect, the principle of participation among international society and the relevant enterprise, the positive participation of schools and students, regular and non-periodical training unifies principle, centralism training and long-distance training unifies the principle. Thus, the informationization personnel training of countryside area in China can obtain the actual effect, and the effective docking of information supply and demand in countryside area can be realized.

Reference 1. Li, D.: China Rural Informatization Development Report 2009. Publishing House of Electronics Industry, Beijing (2009)

Research on Quality Index System of Digital Aerial Photography Results Wencong Jiang1, Yanling Li1, Yong Liang1, and Yanwei Zeng2 1

School of Information Science & Engineering, Shandong Agricultural University, Taian, Shandong 271018, P. R. China 2 State Supervision and Testing Center of Surveying and Mapping Product, 1982nd Section Renmin Road, Chengdu 610081, China [email protected]

Abstract. Aerial photogrammetry is one of the main methods obtaining geospatial information. After entering the 21st century, aerial photogrammetry technology has fully entered the digital age, and the quality of digital aerial photography results will directly affect the quality and accuracy of results of surveying and mapping .Therefore, it is necessary to inspect the quality of digital aerial photography results. However, in the practical application, the production and quality inspection of digital aerial images have not had a complete norm to guide all the time. So in the subsequent application, image data appears lots of problems. Based on development situation at home and abroad and national existing regulation, generalize and establish a complete quality index system has a far-reaching significance in perfecting digitalization and informatization surveying and mapping technological system. The contents of quality inspection of aerial photography results are comprehensive, which include data quality, flying quality, the image quality and annex quality. From the perspective of productive practice, combined with practical experience of scanning digitalization production and a series of experiences of the quality inspection process of digital aerial images, the article focuses on the process to establish a complete quality index system. Keywords: digital photogrammetric image, aerial photography results, film scanning, quality inspection, quality index.

1 Introduction With the rapid development of surveying and mapping and the advancement of computer technology and CCD technology, aviation digital cameras have become the main technical means of a high-resolution and high-precision aerial remote sensing and uncontrolled aerial survey. Digital aerial photography has shown obvious advantages, especially in large area, large-scale and speedy mapping, and digital aerial image map, which has some features of rich information and intuitive truth, is widely used in surveying and mapping fields. With more and more mature digital aerial photography technology and more and more results, the quality of aerial photographs directly D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 381–391, 2011. © IFIP International Federation for Information Processing 2011

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determines the mapping precision, aesthetics, reliability and current situation. In the process of establishing informatization surveying and mapping system, as the important data source of the basic geographic information data, digital aerial photography results will become more and more, and “real-time and automation of data processing” of informatization surveying and mapping requires automation and rapid response of quality inspection and evaluation, which proposes a challenge how to check the quality of digital aerial photography results. At present, quality inspection index of the aerial photography results distributes in the relevant national and industry standards and quality inspection index of the quality inspection and evaluation of digital aerial photography results does not form a complete system; still using manual methods inspects the quality of digital aerial photography results, so the above technical means are backward. However, digital aerial photography provides image data which makes it possible to use computer programs making more comprehensive and rigorous automatic quality inspection. At present, scanning the original negatives to acquire digital images is still the primary means of digital image acquisition. The digital system of aerial imaging aerial photo scanner scans film to acquire digital aerial image map. Therefore, quality inspection of aerial images inherits the traditional inspection content and increases some contents of the aspects of image digitalization[1].On the basis of collecting research results of the quality inspection of digital aerial photography results at home and abroad, understanding new developments of flight quality inspection and image quality inspection of digital aerial photography results, and analyzing the current situation of quality inspection technology of aerial photography results at home and abroad, according to national relevant norms, standards and regulations etc, it is essential to research and establish a perfect quality inspection index system of digital aerial photography results.

2 Quality Inspection Index of Digital Aerial Photography Results The contents of quality inspection of digital aerial photography results are comprehensive, which include data quality flying quality, the image quality and annex quality. According to the four aspects, and analysis of quality inspection elements of digital aerial photography results, it is reasonable and scientific to establish a complete quality inspection index system of digital aerial photography results. 2.1 Data Quality Data quality includes: aerial camera testing data, negative pressing detection information, and aerial designing data. 2.1.1 Arial Camera Testing Data Generally, we can use wide-angle aerial camera with 152mm focal length. In order to meet the need for elevation measurement precision or have some difficulties of height of airplane flight in high altitude, we can use special wide-angle aerial camera with 88mm focal length.

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We should use the same main distance aerial camera in aerial photography zonings of the same area. For large areas of the community, we can use up to aerial cameras of three different main distance, but we must use the same main distance aerial camera in the same route. Under the cloud photography we should use aerial camera of ensuring high-quality. Aerial camera should be check by infection department with appropriate qualification. Items and accuracy of infection are in the Table 1. Table 1. The accuracy test

items checking the main distance

accuracy ±0.005mm

coordinates of optical mark

±0.005mm

radial distortion

±0.003mm

coordinates of best symmetric main point

±0.005mm

the main point of auto-collimation

±0.005mm

comprehensive resolution of the lens shutter speed

no less than 25 line pair per millimeter 1/100s∼1/1000s

2.1.2 Negative Pressing Detection Information The quality of aerial photography data affects mapping precision. In the process of aerial photography, we do not only to strictly carry out the aerial plan and aerial design, but also flying qualities and photographic quality is crucial. Inspection of Negative pressing accuracy is indispensable. Each cassette of photo zone should be checked two or four continuous flat area photographs. Selecting good quality of negative images, the normal overlapping, small obliquity and rotation angle, and clear and complete frame mark images are important. Distance of orientation point (standard point) to the azimuth line should be not less than 9.5cm, and the checkpoint should be evenly distributed. Analytical method is used to inspect the quality of the negative pressing, according to the principles of photogrammetry, after two or four consecutive stereo image pairs, which are needed to check, make azimuth line orientation with the precise three-dimensional coordinate measuring apparatus, exterminating the orientation point and check point's coordinates and parallax of each image pairs, and then the computer can be used to make relative orientation calculation of consecutive stereo image pairs. While calculating the relative orientation elements, it must check the size of the remaining parallax from top to bottom of the model orientation points and checkpoints to determine the negative pressing situations (generally not more than 0.02mm, the individual points more than 0.03mm)[2].

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2.1.3 Aerial Design Data In aerial photography design work, aerial photography can be deal with automation by computer, and can obviously enhance the reliability of data and improve data accuracy to ensure quality control of the design work effective. The superiority can not be compared with calculation by hand. 2.2 Image Quality The overall requirements of image quality are as follows. Good image must have some features, such as moderate contrast, color saturation, clear frame mark, histogram with normal distribution, the image histogram values in the 0-255 gray scale and consistent color in adjoining flight course. Image quality includes the quality of image resolution, color mode, image clarity and frame mark, image color, geometric accuracy and integrity of the image, richness of veins information, data formats and compression standards. 2.2.1 Image Resolution Image resolution is the main parameter of reflecting the amount of information of digital image. Currently, acquisition of digital aerial images is basically through the use of high precision aerial photo scanner, so we use the scanning resolution to measure the size of image resolution, which is different from ground resolution. Scanning resolution is that a pixel on digital images corresponds to the size of the original film. In general, the scanning resolution of image should ensure the scale of 15-25um. Of course, the determination of image resolution is also based on photography scale of aerial photograph, mapping scale and the quality of the original aerial photograph. Typically, the image resolution of a project should be consistent, otherwise in the subsequent photogrammetric operations, the software system will not normally display threedimensional model and can not match images. 2.2.2 Color Mode Color model is the type of showing image color, that is the binary mode, grayscale mode, and color mode etc. color models can also be expressed as RGB color space and CMYK color space and so on. 2.2.3 Image Clarity and Frame Mark Image clarity expresses the clarity of the edge of images. Image clarity is the main indicators of reflecting image quality. Clear images are not only to help interpret surface features, but also to improve capacity of visual identification and the accuracy and to improve the success rate of image matching. Images are not clear mainly due to the following aspects: Unlearned images may be the cause of the original photograph or pressed glass not pressing film in the process of scanning; Image pixel is rough, and generally decomposition of the original photograph is too low, and silver halide particles are too large, or sampling parameter is not set to the best in the process of scanning; Impurities on the photograph may be due to improper handling with the fingerprints,

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hair, scratches and other marks in the process of photography, flushing, preservation and scanning. Images and the frame mark are clear directly determine eligibility of the scanning data. Usually, checking image clarity and frame mark is through the means of visual view and contrasting. Frame mark basically is divided into two categories, such as the "crosshairs" and "center point". Checking the frame mark is at least to enlarge 100% of canning pixel with the digital image to check. If the outline of cross wire or center point is clear, the scanning is qualified, otherwise it is needed to see the frame mark of original film or check the scanner flatten device. Inspection of clarity of the image requires a combination with film image quality checklist to check. Usually, the clarity of image is closely related to the clarity of original photograph. If the film is clear, scanning image should be clear, however, the clarity of scanning image is affected by the image gray level and contrast, and gray level and the contrast which are too large or too small will affect the visual perception judgment of image clarity. In such special circumstances, it should be carefully examined, combined with histogram of gray level and other inspection tools, to see whether to meet the requirement after treatment. 2.2.4 Image Color The color requirements of digital aerial images are as follows. Good image must have some features. Brightness and contrast are well-distributed and moderate, and image histogram which is normal distribution is full of 256 gray scales as much as possible. Image brightness and contrast which are too big or too small will result in the loss of information. Color aerial images must be paid special attention to color balance, especially for identification and inspection of the color of key surface features on the photograph the pivotal features on. For example: on the true color aerial photograph, green vegetation displays as green, while on the color infrared aerial photo, it is dark red. Image color should be paid special attention to the color difference of within and between photographs. The image tone and color of all photograph of the surveyed area are consistent, and internal color of sole photograph should be consistent, especially not appearing the apparent color seams in the photograph because of the scanner failures. 2.2.5 Geometric Accuracy and Integrity of the Image Image geometric accuracy mainly depends on the size of the errors which are caused by photography and scanning. Photographic apparatus and scanner which are used for aerial photographs must be a dedicated instrument. Especially the scanner must be image digitizing system which is the high precision and high resolution. A variety of geometric information on the original film should reflect on digital images. The geometric precision of the scanner image can make calculation of interior orientation on the photograph, and usually the interior orientation residuals of photographs can not more than the size of half-pixel. Image integrity mainly ensures the integrity of photograph, and the integrity of the marginal additional information and frame box. The frame box of aerial photograph is an important symbol to determine the coordinates of digital images and the reference point position of the frame box and the additional information of the photographical

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edge should be complete and clear, in order to easily understand the parameters of the camera lens. 2.2.6 Richness of Veins Information Veins information of digital aerial image is a comprehensive reflection of spatial characteristics of objects, and integrated veins information is foundation of obtaining special subject information extraction. Digital aerial images should have rich veins information, fine veins information, and clear veins information. 2.2.7 Data Formats and Compression Standards TIF format is international universal image format, and the general image scanners and processing software can be supported. But the different versions of TIF data may affect the software processing capability of data. At present, TIFF6.0 format is universal, and it can support a variety of compression modes. Of course, some software manufacturers have developed their own data formats and compression methods which are used in the specific software, so some users need this particular data format. Data storage check: the file name should be normally consistent with the scanned image, that is, scanning data file name were named according to the number of film. Scanning data file formats are usually uncompressed * tif format. Black and white data is 256 gray scales and color data is RGB24bit. Checking stored data is correct, whether according to requirement scanning data is stored in the corresponding carrier or not, and the number of stored data files should be consistent with the source file number. 2.3 Flight Quality Flight quality includes the quality of longitudinal overlap and sidelap, the absolute loopholes, relative loopholes, and supplemental photography of loopholes, angle of inclination and rotation angle of photo, flight course curvature, the keep of flight height, photo area, subarea map profile coverage guarantee, and control of routes. 2.3.1 Longitudinal Overlap and Sidelap In relatively flat terrain areas, longitudinal overlap of photographs usually should be 60% -65%, and individually the maximum is not more than 75% and the minimum is not less than 56%; sidelap usually should be 30% -35%.When elevation of adjoining flight course is large and can not satisfy the sidelap requirements, subarea aerial photography are required. The deviation of flight route and designing route can not more than 10% of the width of photograph cover; the difference of actual photographic scale and design scale is not more than 5%. Because of the effects of air onflow, instability of airplane flight will cause that the main axis of the camera deviates from the vertical line, and the general deviation is not more than 3 °. 2.3.2 The Absolute Loopholes, Relative Loopholes, and Supplemental Photography of Loopholes When the value of longitudinal overlap and sidelap is 0, it can be called the absolute loopholes. When the value of longitudinal overlap and sidelap is less than specified

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requirements, it can be called the relative loopholes. In aerial photography, the value of the photograph overlap is small or the photograph has no overlap part, it can be called the aerial photography loopholes. Aerial photography data does not allow having any loopholes. Absolute loopholes and relative loopholes are both needed to make supplemental photography in time, and supplemental photography must according to original design requirements to do, and the length of supplemental photography route should be beyond a baseline of outside of the loopholes length [3]. 2.3.3 Angle of Inclination and Rotation Angle of Photo In the process of flight, changes of the adjoining photograph generally are not more than 12 degrees. Angle of inclination of photo is controlled by the level control knob of the camera, and generally is not more than 3 degrees. During aerial photography, the bias correction and aircraft inclination will generate overrun of angle of inclination and rotation angle. 2.3.4 Flight Course Curvature Flight course curvature is the greatest degree of deviation of ligature, which connects main points of photograph within a photographic flight course with main points of the first and last photograph. Flight course curvature is not more than 3 °. 2.3.5 The Keep of Flight Height The difference of the actual flight height and design flight height within subarea is not more than 5% of design flight height. In the same flight route, the difference of the maximum flight height and the minimum flight height is usually not more than 50m. when aerial photograph scale is more than or equal to 1:8000, in the same flight route the difference of the maximum flight height and the minimum flight height is not more than 30m, and flight height difference of the adjoining photo is not more than 20m[4]. 2.3.6 Photo Area, Subarea Map Profile Coverage Guarantee Splicing photograph by overlap arrangement, it is essential to check coverage situation in photo area which is marked on topographic maps. Photo area boundary coverage guarantee: Coverage of flight routes which is beyond photo area boundary lines is not less than one baseline. Coverage of direction of side which is beyond photo area boundary lines is not less than 50% of frame of image, and not less than 30% of frame of image at least. When routes are laid according to the center lines of maps and public map profile lines of two adjacent maps of direction of side, direction of side is beyond photo area boundaries which is not less than 12% of frame of image at least. Subarea coverage guarantee: If direction of routes in adjoining subarea is the same, direction of side is the normal flight and direction of course is beyond a baseline of subarea boundaries. If direction of routes in adjoining subarea is not the same, direction of course is beyond a baseline of subarea boundaries and direction of side is beyond subarea boundaries which is not less than 30% of frame of image, and not less than 15% of frame of image at least; When routes are laid according to the center lines of maps

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and public map profile lines of two adjacent maps of direction of side, direction of side is beyond subarea boundaries which is not less than 12% of frame of image at least. Map profile coverage guarantee: As the photo area boundary lines and subarea boundary generally coincide with the map profile lines, requirements of the map profile coverage are as above. 2.3.7 Control of Routes In the photographic survey area, to reduce the layout of the photograph control point, control of routes is many adding flying routes which are approximate vertical with mapping routes. It is essential to check photograph scale of control routes, overlap, coverage, the absolute loopholes, relative loopholes. 2.4 Annex Quality It is important to check the correction, integrity and complete, and standard of the documentation. data includes technical design, design summary, inspection reports, photographs index map, aerial identification table, number, annotation, packaging, data transfer books (aerial design books, photo area books, photos combination chart and so on), the measurement of photo behavior feature of aerial film and aerial photographic film washing records, scanning data, inventory of scanning data, design of scanning production, scanning table and records of problems and so on [5].

3 Results and Discussion 3.1 Results of Forming a Complete Quality Index System According to the above discussion and analysis of the quality elements and their specific parameters of digital aerial photography results, a more complete quality index system of digital aerial photography results can be created. 3.2 Discussions In the process of production of aerial image, lots of factors affect the quality of aerial image, such as film density and the quality of washing and the scanning of aerial photograph and determination of resolution. 3.2.1 Film Density and the Quality of Washing In the process of aerial camera work, it is through the air suction device to ensure the film leveling, at the time of the instantaneous exposure, and the difference is ± 0.0005 " on the focal flat. In the film's preservation period, it should be a storage environment which is secure for sensitive material. In the preservation, transportation and the process of use, it should be paid attention to moistureproof, and it can not be exposed to places which are directly shinned by sunlight or have high temperature.

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Table 2. Elements of quality of digital aerial photography results

the first quality element data quality image quality

flight quality

annex quality

the Secondary quality element aerial camera testing data, negative pressing detection information, and aerial designing data image resolution, color mode, image clarity and frame mark, image color, geometric accuracy and integrity of the image, richness of veins information, data formats and compression standards longitudinal overlap and sidelap, the absolute loopholes, relative loopholes, and supplemental photography of loopholes, angle of inclination and rotation angle of photo, flight course curvature, the keep of flight height, photo area, subarea map profile coverage guarantee, control of routes the accuracy, completeness and standardization of the document

The imaging and photographic fixing of photograph should use treatment control conditions recommended by manufacturer, and residual sulfur compounds, which have been handled, are not more than 0.04 mg per square inch according to international common standards. Processed film should not be defective, torn and worn, and should not have watermarks, fingerprints, hair, dirt and other circumstances affecting image quality [6]. 3.2.2 The Scanning of Aerial Photograph and Determination of Resolution Acquisition of digital images is that scanner makes the process of digitalizing film images. High-precision digital imaging system is used to scan aerial image, and inherent geometric accuracy of the scanner is at least 7um, and the maximum scanning resolution should reach 15 um. Digital imaging system is commonly used at home, such as VX4000, 5000 model, the DPW scanning system of Leica / Helava, Imatizer of China Surveying and Mapping Institute, GeoSystem system of Ukraine, Photoscan of I / Z Corporation and so on. The main parameters of the process of scanning image for conclude file name, data format, compression type and compression ratio, scanning resolution, the color type, file size, image pyramid, the file header and color tone, etc, and tone color is the focus of quality control . many scanners are through setting up light transmission rate of a transparency film and Camma parameters to adjust brightness and contrast of the output digital imaging .Therefore, in the process of work, according to colors of photograph, we can make the definition of the maximum light transmission rate, minimum light transmission rate and Camma parameters in order to make output image color tone even and moderate. The accuracy of operation should be paid attention in the process of scanning. For example, Film must be pressed flatten, and fingers do not directly touch the film, and it needs to keep temperature and humidity of the scanning room, and to maintain a clean

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working environment, etc. Moreover, it is important to avoid appearing fuzzy, hair, dirt, fingerprints of the output image and other phenomena. CCD width of many scanners is 5cm below, so it should need multi-band scanning for back and forth, which can cover an aerial photo. If CCD and scanner have the dust pollution, it will inevitably lead to color difference between images within the photograph [7]. Apart from having the relationship with film itself, resolution of digital images has the relationship with the size of scanning pixel. The smaller the size of scanning pixel is, the higher the resolution of digital image. At the same time, unit images have the greater amount of data. Scanning resolution directly affects the accuracy of follow-up results.

4 Conclusions In the twenty-first century, aerial photogrammetry technology has fully changed from the simulation method, analytical method to digital photogrammetry technology, and original data is digital aerial images instead of simulated photograph, and means and methods have been greatly improved. With more and more mature digital aerial photography technology and more and more results, as the basis data of aerial photogrammetry technology, the quality of aerial photographs directly determines the mapping precision, aesthetics, reliability and current situation. On the basis of collecting research results of the quality inspection of digital aerial photography results at home and abroad, understanding new developments of flight quality inspection and image quality inspection of digital aerial photography results, and analyzing the current situation of quality inspection technology of aerial photography results at home and abroad, according to national relevant norms, standards and regulations etc, it is essential to research and establish a perfect quality inspection index system of digital aerial photography results. The contents of quality inspection of digital aerial photography results are comprehensive, which include data quality flying quality, the image quality and annex quality. According to contents of the four aspects and analysis of quality inspection elements of digital aerial photography results, it is reasonable and scientific to establish a complete quality inspection index system of digital aerial photography results. Moreover, in the process of production of aerial image, lots of factors affect the quality of aerial image, such as film density and the quality of washing and the scanning of aerial photograph and determination of resolution. From the perspective of productive practice, combined with practical experience of scanning digitalization production and a series of experiences of the quality inspection process of digital aerial images, the article focuses on the process to establish a complete quality index system.

References 1. Yuan, G., Gu, B., Huang, J.: The quality control and inspection of digital aerial images. In: 2003 Academic Annual Meeting Album of Institute of Surveying and Mapping of Jiangsu Province (2003) 2. The implementation details of inspection and acceptance and quality assessment of national aerial photography products. National Mapping Bureau (2001)

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3. Zhang, Z., Zhang, J.: Digital Photogrammetry. Science and technology University of Surveying and Mapping of Wuhan press, Wuhan (1995) 4. Qiao, R., Sun, H., Li, X.: Photography and aerial photography. Wuhan University Press, Wuhan (2008) 5. Supplementary technical requirements of national aerial photography. National Mapping Bureau (May 2003) 6. Yu, G., Li, W., Lin, Z.: The Discussion of influence of quality of aerial film pressure level on the mapping accuracy. Surveying and Mapping and Spatial Geographical Information (April 2009) 7. Ren, J.: The quality control of scanning digital production of aerial film. In: The Research of Materials of Photographis Institute of China of and Application Academic Symposium

Research on Quality Inspection Method of Digital Aerial Photography Results Xiaojun Wang1, Yanling Li1, Yong Liang1, and Yanwei Zeng2 1

School of Information Science & Engineering, Shandong Agricultural University, Taian, Shandong 271018, P. R. China 2 State Supervision and Testing Center of Surveying and Mapping Product, 198 2nd Section Renmin Road, Chengdu 610081, China [email protected]

Abstract. Photography is the main access to obtain geospatial information, the quality of the results will directly affect the follow-up results of the quality of surveying and mapping. Therefore, we need a comprehensive aerial photographic quality inspection results. Correct evaluation of the quality of digital images is an important but difficult to solve the research topic. Many of the current image quality evaluation mainly rely on the subjective judgments. With the rapid development of image acquisition and processing technology and people's increasing demand for image information, It has become increasingly important economic significance and practical value to research the objective evaluation method that can accurately reflect the subjective evaluation. In this paper, I analyzed the traditional methods and several cutting-edge methods of the digital image quality evaluation. According to the analysis and comparison of image quality evaluation methods, I forecast the development of the image quality evaluation methods. The methodology, based on modulation transfer function combined HVS model evaluation methods for image quality evaluation is expected to achieve breakthroughs in difficult problems. With the development of the image analysis and processing technology, especially the development of computer vision and intelligence technology, the difficulty of image quality evaluation also will be resolved. Keywords: digital aerial photography results, quality evaluation, modulation transfer function, human visual system, gray prediction error, visual interests.

1 Introduction With the rapid development of surveying and mapping, computer technology and CCD technology progress, aviation digital camera has become the main technical means to control the high resolution and precision aerial remote sensing and the aerial technology without control, digital aviation photography has obvious advantages. In the process of the information surveying and mapping system construction, as an important foundation geographical data source, digital aviation photography will be more and more. The real-time data processing and automation of information surveying and D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 392–399, 2011. © IFIP International Federation for Information Processing 2011

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mapping requests the automation and rapid response of quality inspection and assessment , and it is a challenge to how to check digital aviation photography results . The content of aerial photography results quality is extensive, and it needs for the 100% inspection of data quality, flight quality, image quality and annex quality. Now it needs for a long time to use manual inspection method to check aerial photography results quality, and the efficiency is low. Digital aviation photography results also provide print pictures, but the picture is small, the overlap is big, and the number is big. so the manual inspection efficiency is lower . But digital aviation photography provides data, so it makes it possible to use the computer to check the quality comprehensively and carefully. To sum up above all, in order to adapt to the needs of information construction of surveying and mapping system, basic surveying and mapping and post-disaster reconstruction, it needs to study the quality inspection methods of digital aviation photography results. This project aims to research and analysis aerial photography quality inspection results based on current technology research and establish digital aviation photography results quality inspection methods, and explore the flight quality, computer automatic quality inspection method. So it lays the foundation for the development of practical "digital aviation photography results quality inspection system" software.

2 The Traditional Image Quality Inspection Methods 2.1 Subjective Evaluation Method According to some observers’ assessment scales or their experience, the quality scores are given. All scores take averages and it is the subjective quality evaluation. Two kinds of observers are included, some are experienced experts, others are normal, because their experience and angle of concern is different, finally comprehensive consideration of the evaluation result is more persuasive. Subjective evaluation includes two kinds of measurement scales, namely absolute scale and relative scale. So-called absolute scale is giving a score according to the quality of images, and the relative scale is giving a score according to the comparison of some image. Subjective quality assessment standards level 1 2 3 4 5

absolute scale very good good general poor very poor

relative scale the best More than the average the average Below the average the worst

Subjective evaluation method can better reflect the quality of images, but it excessively affected by the knowledge background, emotion, motive and fatigue etc. From engineering point of view, this kind of method is too time-consuming and is not described by mathematics model, so that it does not facilitate computer calculation.

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2.2 Objective Evaluation Method Use algorithm to evaluate image quality evaluation. This method is convenient, quick, easy to realize and combined with the application system, but it is different from subjective feelings. Below are some typical image evaluation methods. 2.2.1 Variance Variance is a statistics which reflects the whole image gray-scale distribution. The variance is larger, the contrast is larger, and conversely, if the variance is smaller, the contrast is smaller. Computation formula is as follows

σ

2

=

1 M • N

M −1 N −1

∑ ∑ ( f (i, i=0

2

j) − μ

j=0

M and N are the row and the column of the image, is the grayscale average.

)

.

(1)

f (i, j ) is the gray value, and μ

2.2.2 Average Gradient Average gradient refers to reflect the degree of contrast images subtle. Computation formula is as follows

σ

2

=

1 M • N

2

M −1 N −1

∑ ∑ ( f (i, j ) − μ ) i=0

.

(2)

j=0

M and N are the row and the column of the image,

f (i, j ) is the gray value, ∇ i f (i, j ) is the gradient in row direction, and ∇ j f (i, j ) is the gradient in column direction. Generally speaking, the average gra-

dient is bigger, the image is more clearly and contrast is good, but gradient is affected greatly by noise. 2.2.3 Information Entropy Information entropy reflects image information abundance extent from the angle of information. According to the Shannon information entropy principle, Computation formula is as follows L −1

H ( X ) = − ∑ Pi log( Pi ) i=0

L is the biggest grayscale level and

(3) .

Pi is the probability of the grayscale which

value is i . 2.2.4 Mean-Square Error MSE is a statistics which reflects image gray-scale between the original image and the transformation image. Computation formula is as follows

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1 M−1 N−1 ˆ ( f (i, j) − f (i, j))2 . ∑∑ M • N i=0 j=0

(4)

MSE=

MSE is Mean-square error,

f (i, j ) is the gray value of the original image, and

fˆ (i, j ) is the gray value of the results image. 2.2.5 Peak Value Signal-to-Noise Ratio PSNR is mainly used in evaluating image quality changes through compression, transmission and treatment, its nature is like mean-square error. Computation formula is as follows

PSNR = 10 log10

256 × 256 MSE .

(5)

MSE is Mean-square error. The information entropy can be used to compare information changes when images are compressed or processed, but it is not appropriate to consider it as the standard of image quality. Because the information of the same remote sensing images in the same area but in different time is different. MSE and PSNR can reflect the overall difference between the original image and the restore image, and can not reflect the local case like a bigger gray difference and a small gray difference among more pixels. Obviously all pixels can not be treated the same, thus it can not reflect the human visual characteristics.

3 Several New Evaluation Methods 3.1 Method Based on Modulation Transfer Function

Method based on modulation transfer function is objective, can transfer and is easy to measure, which is a comprehensive image quality evaluation method accord with evaluation standard. Steps are as follows Use edge extraction technology to obtain blade and determine the scope and direction of blade in the digital image. Do re-sampling of blade and filter. Get line diffusion function through derivation of blade brightness curve and obtain its equivalent width. Obtain modulation transfer function through flourier transform of line diffusion function. Generally speaking, the low frequency part of modulation function decides image contrast, the high frequency part decides image details and clear expression ability. Therefore, we always hope modulation transfer function curve containing area that is bigger is better, thus we can use envelope area of the transfer function to judge image quality.

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3.2 Method Based on Human Visual System

It is a method that meets human visual system. The method is added up to some stage results of human visual system and its evaluation results can reflect people's subjective feeling. The typical model includes three significant characteristics, namely the visual non-linear characteristics, the visual resolution sensitivity band pass characteristics, the visual multi-channel structure and cover effect. 3.3 Method Based on Gray Prediction Error

DPCM (differential pulse code modulation) system is a classical image linear coding system. It includes two basic premises. One is that images can be considered to be a smooth airport and its correlation function has nothing to do with pixel position , another is that image has high correlation in the local area , so image grey value of a point can be estimated though adjacent pixel grey value. This evaluation method is that comparing real and estimated values to get the images evaluation results. Set f ′(m, n) as the estimate of pixel, e(m, n) as estimated deviation, If use third-order prediction, then

fˆ(m,n) =a1 f (m,n−1)+a2 f (m−1,n−1)+a3(m−1,n).

(6)

e(m, n) = f (m, n) − fˆ (m, n) .

(7)

According to the principle of least squares estimate and separation characteristics of image correlation function, we can get coefficients of gray estimate function.

Input image

Visual nonlinear

Visual sensitivity band-pass

The visual multi-channel structure and cover effect

Output measurement results

HVS

a1 =

R(1,0) R(0,1) . R(1,1) . a3 = a2 = R(0,0) R (0,0) R(0,0)

(8)

If coefficient values can be obtained, we can get the gray prediction deviation of images.

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σˆ e =

1 M •N

M −1 N −1

2

∑ ∑ [e ( m , n ) ]

.

m =0 n=0

397

(9)

Average variance of deviation accumulation can be used to evaluate the quality. 3.4 Method Based on Fuzzy Measure

The uncertainty of digital images contains both randomness of imaging process and ambiguity of their own. In gray fuzzy measure, using the method of fuzzy entropy, and fuzzy entropy reflects the overall image information and pixel gray average. In space fuzzy measure , method to calculate the area , perimeter , a degree of index and the image information of fuzzy sets , which reflects the geometrical characteristics of the image’s fuzzy degree . Such as gray measure method, at present the most influential method is the fuzzy entropy algorithm. It can reflect the average fuzzy degree of fuzzy sets pixel. A Two-dimensional image which grayscale is L and size is M x N can be considered to be a fuzzy array, and the membership of each element stands for the brightness degree compared to brightness levels. Research shows that we can use S function to take the gray value of image space domain to mapping the eigenvalue of the fuzzy feature plane, and S function is defined as follows 0 ⎧ xi , j ≤ a ⎪ xi , j − a 2 ⎪⎪ 2( c − a ) a ≤ xi , j ≤ b . Pi, j = S ( xi , j , a, b, c) = ⎨ c − xi , j 2 b ≤ xi, j ≤ c ⎪1 − 2( ) c−a ⎪ xi , j ≥ c ⎪⎩ 1

(10)

b = (a + c ) 2 is A boundary , and if xi , j = b , then Pi , j = 0.5 . Δ = c − b = b − a is bandwidth of mapping function . 3.5 Method Based on Visual Interest

People in the observation and understanding the image are often interest of some areas, and these areas are called areas of interest. The whole image visual quality often depends on the quality of areas of interest and sometimes the drop quality of the area that is not interested in is not be perceived . Method based on visual interest reflects interest degree of the area of interest through the weighted average for different areas. Set only one interest area can be included in test image which is A1, and its area is S1. Areas that are not to be interested which is A2, and its area is S2. The total area is S=S1+S2. Computation formula is as follows IMSE =

⎤ 1⎡ 2 2 . ⎢λ1 ∑ ( f ij − f ij′ ) + λ2 ∑ ( f ij − f ij′ ) ⎥ S ⎣ ( i , j )∈A1 ( i , j )∈A2 ⎦

(11)

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is the weighted value of A1 and

equation

λ1 ∗ S1 + λ 2 ∗ S 2 = S

.

λ2

The weighted value is bigger and the interest de-

gree of the area is bigger. The formula of

λ2 = 1 − λ1 =

is the weighted value of A2 , and meet the

λ1

and

λ2

2k S1 ( S − S1 ) . S

S (1 − λ2 ) + λ 2 . S1

as follows (12)

(13)

k ∈ [0,1] is adjustment factor , and it reflects the emphasis of the human eye to those areas that are not to be interested . We can see that if S1=0 then λ2 = 1 and if S2=0 then λ1 = 1 from the equation, at this time IMSE degenerate into MSE. Traditional objective evaluation methods are the exception of this method. It can better reflect the subjective visual. Method based on visual interest can better consistent with the subjective visual, but this method is still in the initial stage of study, there are still many problems to research. Such as, determine the areas of interest, how to do if the test image contains multiple areas of interest, and how to determine the weights of some areas etc.

4 Conclusions Firstly, the progress of visual perception biological psychology experiment is the basic research of image quality evaluation, the human visual system (HVS) is based on this. A large number of experiments show that people’s knowledge in visual perception is very poor. Secondly, continue to study the HVS model. The mature HVS model makes us not to find an analytical formula to simulate human visual process. But common evaluation model means easy to calculate like square error. It requires us to fully consider visual physiological and psychological tests and to use the latest mathematical tools for research. Thirdly, method based on modulation transfer function combined with the HVS model is expected to solve problems of image quality evaluation. With the development of image analysis and processing technology, especially the development of computer visual and intelligent technology, the image quality evaluation difficulties will become a breakthrough.

References [1] Xiong, X.: Digital image quality assessment. Science of Surveying and Mapping (2004) [2] Xu, L.: The method to evaluate image quality, wavelet transform, luminance, definition, correlation, multi-scaling, ZheJiang University (2003)

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[3] Wang, K.: Quality Assessment of Digital Images. Measurement & Control Technology (2000) [4] Wang, Y.: Explortion on digital image quality assessment method. Remote Sensing Information (2004) [5] Yu, L.-f.: The quality evaluation of digital mapping camera digital image. Technological Development of Enterprise (2009) [6] Wang, Y., Hu, Z., Zhang, B.: Research on digital image quality assessment. Bulletin of Surveying and Mapping (2002) [7] Wang, Y.: Research on digital image quality assessment, Zhengzhou, Henan (2001) [8] Wei, Z., Yuan, J., Cai, Y.: The History, Status and Future of Image Quality Evaluation. Journal Of Image And Graphics (1998) [9] Zhang, M.: Research on image quality assessment method. Shandong University (2007) [10] Xiong, X., Zhang, L.: An Image Quality Evaluation Method Based on Gray Prediction Error. Journal of Image and Graphics (2004) [11] Wei, X.: The precision analysis and quality assessment of RS image data processing. Tongji University (2004) [12] Wang, K.-q., Shen, L.-s, Xing, X.: Quality Assessment Method of Image Based on Visual Interests. Journal of Image And Graphics (2000)

On RFID Application in the Tracking and Tracing System of Agricultural Product Logistics Weihua Gan, Yuwei Zhu, and Tingting Zhang School of Mechanical and Electronical Engineering, East China Jiaotong University, 330013, Nanchang, P. R. China, [email protected], [email protected], [email protected]

Abstract. Nowadays the low quality of agricultural products has resulted in some accidents in China. People are waiting for the arrival of the safe and high quality agricultural products than before. This paper takes the agricultural products’ safety as an example to analyze the RFID application. Firstly, this paper describes the characteristics of logistics activities about the agricultural products. Then it introduces RFID technique in tracking and tracing system. Next this paper discusses the RFID application along the agricultural product supply chain step by step. Finally, it presents the prospect of RFID technique in tracking and tracing system of agricultural products by a case study of 2008 Olympic Games in Beijing. Keywords: Agricultural products, Radio Frequency Identification (RFID), Tracking and Tracing System (TTS).

1 Introduction Owing the chemical abusing and environment worsening, the quality of agricultural products is becoming a hot topic in China during the recent decades. Although developed countries have attached importance to the tracking and tracing system in agricultural product logistics since the mad cow disease appeared, some developing countries are just beginning to realize the importance of agricultural products logistics [1]. Chinese government has established some corresponding laws and regulations, such as The Special Provision about Strengthening Safety Supervision and Management on Foods proposed by the State Council, Law on Agricultural Product's Quality Safety of The People's Republic of China [2]. It can be seen that the research on tracking and tracing in agricultural product logistics has been a priority of the agenda.

2 Agricultural Products’ Safety 2.1 Concept of Agricultural Products’ Safety Agricultural product refers to the primary product in agriculture, including vegetation, animal, microorganism, etc. Agricultural product safety is an important component of D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 400–407, 2011. © IFIP International Federation for Information Processing 2011

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food sanitation quality, which means that the agricultural product must have no negative effects on human healthy directly or potentially. Safe agricultural product refers to the agricultural product should neither contain the elements which harm or threaten health nor acute or chronic diseases to consumers and their offspring [3]. 2.2 Influencing Factors of Agricultural Products’ Safety Generally there are four factors influencing agricultural products’ safety, they are [4]: (1) Growing environment: soil, water and air are the top three significant factors for agricultural products in their growing environment. (2) Production condition: some pesticides, fertilizers, etc. are applied inevitably during the production of agricultural products. The biggest threats to public health are those high-poisonous and high-persistent pesticides, which are prohibited or restricted. (3) Pest factor: all kinds of agricultural plants would suffer from pests along the processing. Accordingly it would influence the quality and appearance of those plants themselves. (4) Management factor: the management activities related to growth and production are the key factors for agricultural products’ safety, too. Moreover, the service quality and efficiency of agricultural product logistics would influence safety.

3 Agricultural Product Logistics 3.1 Definition of Agricultural Product Logistics It is defined agricultural product logistics is the economic activity from agricultural product producer to the consumers in order to satisfy customers’ demands, including

Fig. 1. Agricultural Product Logistics

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the links such as agricultural product production, purchasing, transportation, storage, loading and unloading, handling, package, processing, distribution and information processing [5], it is shown in Figure 1. 3.2 Characteristics and Classification of Agricultural Product Logistics Contrary to the industrial product, agricultural product has its own characteristics embodied in its life [6]: (1) Restricted by natural conditions, the growth of agricultural product is obviously seasonal, and the output is unstable. However, the requirement of agricultural product is lasting and stable. (2) The planting regions are usually centralized within some areas, but the consumers are distributed all over the nation. (3) Agricultural product almost grows depending on the nature, so it is not standardized. (4) Agricultural product has short life cycle. Owing to these characteristics, agricultural product logistics is different from industrial product logistics. Emphasis should be put on the package and processing of agricultural products. According to the different classifying criterion, the categories of agricultural product logistics can be diversified. The classification of agricultural product logistics as shown in the Table 1: Table 1. Classification of agricultural product logistics No. 1 2

Criterion logistics phase logistics object

3

logistics subject

Contents of categories production logistics, distribution logistics, waste logistics grain logistics, crops logistics, animals logistics, aquatic products logistics, forest products logistics producer-logistics-suppliers, buyer-logistics-suppliers, third-party-logistics-suppliers

4 Tracking and Tracing System of Agricultural Products 4.1 Definition of Tracking and Tracing System of Agricultural Products Tracking system refers to the ability of tracking the path of a particular unit or a batch of products from upstream to downstream along supply chain. Tracing system means the ability to distinguish the particular unit or a batch of products’ source from downstream to upstream along supply chain [7]. In other words tracing is mainly used to discover quality problem. The process of tracking and tracing is shown in Figure 2: Tracking and tracing system (TTS) of agricultural product logistics is an information management system linking each sector such as manufacture, inspection, supervision, consumption, etc. in order to display the health and safety-from farm to table. In addition, it helps establishing agricultural product safety information database, once food accidents occur, effective measures can be taken to control and recall along the food supply chain.

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Fig. 2. Process of Tracking and Tracing

4.2 Task of Tracking and Tracing System of Agricultural Products The information system plays an important role in the tracking and tracing logistics system of agricultural products. Such information at least should include, e.g. phase information, geography information and object information. In detail, phase information means the data over the whole life cycle, geography information tells the geographical position of agricultural product’s origin and its arrival, object information records all the enterprises engaged in the agricultural product supply chain. The five tasks of the information system are information input, information transfer, information storage, information process and information output. Gathering and recording the information throughout the agricultural product chain, unifying and standardizing the above information is the primary task of information system. The memory function is also essential since the obtained information would not be lost and distorted. The value of the tracking and tracing logistics information system is eventually embodied for those enterprises or individuals along the whole agricultural product supply chain.

5 Application of RFID in TTS 5.1 Function of RFID Radio Frequency Identification (RFID) is commonly known as electronic tag, it is actually a target recognition technology for data interchange in a non-contact duplex communication based on the space coupling of signals such as electromagnetic induction, radio waves and microwaves and so on. Compared with the traditional ticket, label and tracing card, RFID tags have more advantages. It can not only collect and analyze the data from source to destination but also can keep long distance, quick speed, high data security and excellent capability. The operation principle of RFID system is shown in Fig. 3: the effective magnetic space is the basic premise of RFID system. The electronic tag with information enters into the magnetic space while the antenna of the reader emits radio signals at a certain frequency. Meanwhile the electronic tag is activated by the power derived from the faradic current, sends messages stored in the chip through the built-in antenna after decoding. The receiving antenna conveys these signals to the reader. The reader decodes

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Fig. 3. Operation principle of RFID system

the signals, all the decoded signals are conveyed to the information management platform through the application system software for processing and controlling [8]. 5.2 RFID Applied in Production Each RFID tag can be set for each plantation, cultivation, crops and livestock in order to match with the requirements of RFID system. RFID tag records not only the information during plantation and cultivation, such as humidity, light, wind speed, rainfall, but also nutriment or fodder variety and order time. What is more, the different agricultural product and healthy condition can also be added. RFID tags could be placed into collar, card, injection, pill or just on agricultural products’ packages [9]. Owing that all the essential information of agricultural product has been stored to the RFID tag, the circulation speed would be faster, the error rate would be lower. In addition, RFID system provides the basic data such as point of sale system, electronic data interchange system and electronic commerce system for processing enterprises, logistics enterprises, sales agencies, and the initial data for the tracking and tracing system of agricultural products. 5.3 RFID Applied in Processing It is convenient for processing enterprises simultaneously add information on the tag, such as enterprises’ code, processing date, processing batch, and package weight, etc. used in processing [10]. Therefore, the continuity of agricultural product information can be successfully kept along supply chain. After enriching processing corporations’ information, the end consumers could obtain a panoramic view of relevant information even in wholesale market and retail market. 5.4 RFID Applied in Transportation RFID combined with GPS, real-time supervision and tracking service can be available for agricultural product logistics system. For the owners, they can find out the track of

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their goods via the computer network. For the check officers, goods can be examined only through tag so as to speed up the inspection. Meanwhile, whether temperature-shift will do harm to fresh agricultural product or not could be decided by reading the information stored in RFID tag. 5.5 RFID Applied in Storage It is obvious the inventory cycle time can be shorter because the data stored in RFID tag is very useful for warehousing management, e.g. before agricultural products entering warehouse, the data stored in RFID tag can directly be transmitted to computer. The computer will emit the warehousing instruction to the suitable rack and slots. Furthermore, the correct inventory pattern can be chosen depending on the product specification and requirement. 5.6 RFID Applied in Distribution The logistics information system of agricultural product will generate the picking and slotting task as soon as it receives the customer’s order. The operator could pick out the right product immediately according to the information on the RFID tag scanned by hand-reader. Especially some extra information has been enriched during the distribution process in the RFID tag such as distribution vehicle, distribution route, schedule, etc. 5.7 RFID Applied in Sales Not only the vendors but also the consumers can benefit from RFID application in sales. The vendors can supervise the validity of commodities with the help of RFID tag especially for those perishable products. On the other hand, consumers could gain the basic information along the supply chain from the origin to the destination. RFID can help consumers to ensure the agriculture products’ safety. 5.8 RFID Applied in After-Sales On account of the quality granted, the commodities often are recalled. For after-sales, the disabled sectors can be found out immediately through RFID tag. Then some relevant measures can be taken by tracking the whole logistics process of agriculture products. Therefore, the use of RFID in the whole agricultural products logistics system will help to promote the development of seamless exchanging, reading, writing for agricultural products information.

6 Case Study-RFID Applied in 2008 Olympic Games 2008 Beijing Olympic Games is a successful event in the world. Although 2008 Beijing Olympic Games was held in the burning summer, the quality of all kinds of foods is safe and convinced. It is reported that RFID application to the food logistics during

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the Olympic Games help to monitor those foods and ensure the food security. There are no less than 3900 species in 65 categories supervised, and all the crucial foods are labeled with RFID, for instance, rice, flour, oil, meat, fruit, vegetable and dairy product. In addition, a professional database is established for the guarantee on safety. Approximately 17 million sets of foods are provided during the whole Olympic Games. Olympic foods distribution center is established and some measures such as closed transport, entire monitor, and special cars are taken exclusively [11]. RFID label is added on foods from their cultivation to production and processing, i.e. from cultivated land to dining table. Thank for the RFID application, safe and high quality foods can be provided by the Chinese government during 2008 Beijing Olympic Games.

7 Conclusions Application of RFID will boom in the coming years on account of the advantages of goods identity and logistics tracing. With the costs reducing, RFID will surely bring out a revolution in agricultural products logistics and turn to be a new-born economic growth.

Acknowledgments Thanks for the support from national science fund: On Relation Between Mode of Pig Green Supply Chain and Performance around the Poyang Lake(70962002 ).

References 1. Yuan, T.: Research on Constructing Retrospecting System of Agricultural Product Supply Chain. In: Master’s Dissertation of Southwest Jiaotong University (2007) 2. Tang, D.: The Key Technology Research of the System of Track and Trace in the Supply Chain of Agricultural Logistics of Sichuan Province. In: Master’s Dissertation of Sichuan Normal University (2007) 3. Song, J.: The Characteristic of Agricultural Product Logistics and the Related Problems Research. In: Master’s Dissertation of College of Economics & Management Southwest University (2006) 4. Wang, W.: Research on Agriculture Logistics System Evaluation and Optimization. In: Master’s Dissertation of Shandong Normal University (2006) 5. Hou, C., Xia, N.: Applied Research on RFID Technology in China’s Agricultural Products Quality and Safety Traceability System. Chinese Agricultural Science Bulletin 26(3), 296– 298 (2010) 6. Zeng, X.: Study on a EPC Code-based Traceability System for Pork Quality and Safety. In: Master’s Dissertation of Northwest A & F University (2008) 7. Moe, T.: Perspectives on Traceability in Food Manufacture. Trends in Food Science and Technology 9 (1998)

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8. Chang, Q.: RFID Application in Vegetable Supply Chain at Home and Abroad. Radio Frequency Identification Technologies and Applications (5), 18–21 (2008) 9. Li, M., Huang, L.: Application Research of RFID in Agriculture. Anhui Agricultural Sciences 35(20), 6333–6334 (2007) 10. Ling, C., Huang, L.: Application of RFID Technology in Safety Control on Agriculture. Anhui Agricultural Sciences 34(8), 1718–1719 (2006) 11. China Society of Logistics, http://www.cflp.org.cn

Research on Rough Set and Decision Tree Method Application in Evaluation of Soil Fertility Level* Guifen Chen and Li Ma College of Information and Technology Science, Jilin Agricultural University, Chang Chun, Jilin, China [email protected], [email protected] Abstract. Clustering, rough sets and decision tree theory were applied to the evaluation of soil fertility levels, and provided new ideas and methods among the spatial data mining and knowledge discovery. In the experiment, the rough sets - decision tree evaluation model establish by 1400 study samples, the accuracy rate is 92% of the test. The results show :model has good generalization ability; using the clustering method can effectively extract the typical samples and reducing the training sample space; the use of rough sets attribute reduction, can remove redundant attributes, can reduce the size of decision tree decision-making model, reduce the decision-making rules and improving the decision-making accuracy, using the combination of rough set and decision tree decision-making method to infer the level of a large number of unknown samples. Keywords: rough set, decision tree, clustering, data mining, soil evaluation, productivity grade.

1 Introduction In precision agriculture, soil fertility evaluation is the basis for land management, which not only can assess the level of land productivity, and can guide the rational development and utilization. From the data mining point of view, fertility is essentially within the classification prediction. Characteristics of the decision tree method are those: decision tree is a better classification method can handle the non-linear data and describe the data with generating speed and set up an intuitive tree structure [1]. Rough set theory has some advantages in the processing of data and eliminating redundant information and dealing with uncertain information, so widely used in data preprocessing, attribute reduction and so on. Decision rules of rough sets given the right or similar decision-making from the condition, does not exclude the uncertain knowledge, provides a new method based on uncertainty of spatial data mining [2]. In view of rough set and decision tree has a strong complementary nature, this paper introduces rough set theory and decision tree in data mining field, uses original survey data of Nong’an topsoil fertility survey and obtains the inductive *

Foundation project: spark program (2008GA661003), national 863 projects (2006AA10Z271), Jilin Province key project (20060213), Jilin technology development project(2006BAD02A10-6-6).

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 408–414, 2011. © IFIP International Federation for Information Processing 2011

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generalization of the data by analysis, takes clustering samples by study. And then uses the rough set attribute reduction and decision tree method to generate the combination of soil fertility level evaluation rules. This method achieves good results in practice.

2 The Basic Principles of Evaluation Based on Rough Sets and Decision Tree K-means clustering method makes the class as much as a compact cluster, separates as much as possible between classes and meets the needs of the soil data classification. Data of cluster centers, can basically cover the entire sample space, and makes the data more typical. C4.5 algorithm is the most widely used decision tree algorithm, uses adoption rate of information gain to select attributes. Calculate the corresponding information gain ratio, and then select the highest division gain rate as the property of the information gain ratio, can handle continuous numeric attributes. Treatment strategy of missing values for some attribute is assigned to its most common value corresponding training examples, another more complex strategy is to give a probability for each possible value. In the tree formation, in order to prevent the uncontrolled growth of the tree, C4.5 algorithm uses a post-pruning method. The method evolves from one called "rule post-pruning" method. This method uses the training sample set to estimate their own errors before and after pruning, to determine whether the real pruning [3]. Johnson rough set attribute reduction algorithm can reduce attributes to the frequency by property size and weight, access a collection of the most relevant, can calculate the reduction effectively. Specific process is: According to a given decision table to calculate the discernibility matrix. Taking the attributes of largest frequency and weight to reduction sets, and then removes the attributes from resolution function in the same time and return the reduction when the resolution matrix is empty [4]. Therefore, this paper selects the K-means clustering method clustering soil data, extracts optimization samples, then applies the C4.5 decision tree algorithm and Johnson rough sets attribute reduction algorithm method to assist the fertility levels. In the case of large amounts of data, the choice of learning samples is also an important part. This article uses the incremental method selected study samples, using cluster analysis for data processing, removing duplicate sample data, effectively reduce the study sample space and ensure the study sample data is typical. In this article, in order to avoid decision-making performance degradation by using cluster extraction method of learning samples, the author introduce a mechanism to re-select the sample, use of data mining process to provide a typical data to deduce a large data.

3 Applications Based on Rough Sets and Decision Tree 3.1 Experimental Data Acquisition The experimental data is from the Nong'an survey data, provided by Agricultural Technology Promotion Center. Uses ARCGIS9.2 software vector the 1:10 10000 Nong’an soil map, the basic block diagram farmland protection and land-use maps, forms the digital soil map layers, and then matches the fertility survey and quality evaluation of cultivated land base map in 2006 at Nong’an, builds the Nong’an spatial database of sampling points of cultivated land survey and quality assessment.

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Spatial database includes five aspects these are profile and physical chemical properties, site conditions, weather factors, soil nutrients, soil management, including plot latitude and longitude, land area, soil moisture, administrative, light radiation, annual rainfall, soil drought resistance, soil erosion, soil texture, groundwater depth, irrigation, crop rotation suitability, terrain position, soil parent materials, humus layer thickness, salinity, soil ph values, the effective copper, an effective iron, slowly available potassium, available potassium, effective manganese, total nitrogen, available phosphorus, organic matter, cation, effective zinc and level total of 27 properties, 3153 polygon data. According to Nong’an farmland agricultural requirements, the soil is divided into 6 grades 1, 2, 3, 4, 5, 6. 3.2 Extract Based on Clustering Clustering method is used to cluster analysis of experimental data, removing duplication of learning samples, analyzing affects that the number of samples to decisionmaking ability of the decision tree model. The author does data sample based on the principle of gradual, uses K-means clustering algorithm to cluster the original data sample. First The author selects k objects as initial cluster centers from n data objects, and then according to their distance with these cluster centers, the assign remaining pairs of other objects to their most similar clustering; calculates with each new cluster of the cluster center; repeats this process until the standard measure function begin until convergence. In the experiment, clustering sample size are 200, 400, 700, 1000, 1200, 1400, 1800. With the results of the clustering, using ordinary decision tree model to do sample study, samples from the cluster center, through the establishment of decision tree evaluation model to determine the number of learning samples, if the model of

Fig. 1. Sample and model precision

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Table 1. Sample and model precision Sample size (a)

200

400

700

1000

1200

1400

1800

Model accuracy (%)

47.32

67.85

58.93

74.10

74.10

77.68

77.68

high accuracy, tend to converge, then the sample meets the needs, or else need to re-select learning samples. Decision-tree evaluation model test results as shown in Table 1 and Figure 1. From Table 1 and Figure 2 can see that, the curve has a clear inflection point in the 1000 samples, the accuracy of 1400 sample is improved 3.58 percentage points more than 1200 samples, but the 1800 sample evaluation model accuracy is the same evaluate the model with 1400 samples. From these data can see that on the basis of the 1400 samples for each increase of 200 samples, the model precision can be improved very little, indicating that in the 1400-based accuracy rates were stable, is the study the optimal number of samples. 3.3 Data Mining Based on Rough Set and Decision Tree Combination Algorithm for Mining Process. After optimization of the sample clustered a total of

1400 records, contains the soil area, soil moisture, administrative, light radiation, annual rainfall, soil drought resistance, soil erosion, the degree of soil texture, groundwater depth, irrigation, crop rotation suitability, terrain position, into the soil parent material, humus layer, salinity, soil ph values, the effective copper, effective iron, slowly available potassium, available potassium, effective manganese, total nitrogen, available phosphorus, organic matter, cation, effective Zinc total 26 condition attributes and 1 decision attribute as soil fertility levels, the levels of Nong’an is divided into a total of six grades 1,2,3,4,5,6. Rough set attribute reduction algorithm requires that data is discrete data, according to the soil data characteristics, the Entropy / mdl discrete algorithm is carried to do the data processing. Enter the 26 condition attributes to rough set algorithm and form a conditional attribute set C, the fertility level is as the decision attribute D. Using rough set reduction Johnson Reduction Method on attribute set C, get the simple property set. Its reduction properties are soil moisture, light radiation, annual rainfall, soil drought resistance, soil erosion, the degree of soil texture, groundwater depth, crop rotation suitability, terrain position, into the soil parent material, humus layer thickness, salinity, ph values, effective iron, slowly available potassium, available potassium, effective manganese, phosphorus, cation, a total of 19 condition attributes, and removed 7 redundant attributes. After reduction, import the decision tree algorithm C4.5 to do decision-making. The data mining algorithm based on decision tree combining of rough sets are described below: Continuously remove more important attributes from the condition attributes C relative to the decision attribute D, which makes the dependence degree D to it equal the dependence degree D to C, then obtains the attribute reduction set. Then, use the information gain as heuristic information, select attributes that can classify the sample

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Enter the decision-making system S = (U, C, D, (Va), a)

Distinguish matrix calculation, find the corresponding attribute core R, the importance of calculating the properties of other property

If C is empty, remove the attribute, calculated D is equal to its dependence on C of the dependence D

Equal, then joined the R, else removing property

Select R, the largest information gain property, dividing R

Calculated for each attribute information gain, followed by production branch, until the sample property is empty

Pruning, by decision tree Output decision tree Fig. 2. Algorithm flowchart

well, building a branching, and divides the training set according the above, until there are no attributes which may divide again. Afterward use the test set to verify and modify the decision tree model. Algorithm flow shown in Figure 2. Data Mining Results. In 1400 Records, correctly classified data is 1298, the others are not. Decision tree model generated a total of 317 nodes, including leaf nodes 159. Generated part of the decision tree shown in Figure 3. According to the decision tree model, the author uses 100 records on the resulting decision-making model validation, validation results showes that: the decisionmaking accuracy rate of the decision-making model is 92% (Table 4.2), and extracted decision rules 159, some of the decision-making rules are as follows: Rule 1: IF phosphorus> 21.775 AND salt content <= 1 AND light radiation intensity <= 2 AND soil drought resistance <= 3 AND phosphorus <= 22.3 AND soil moisture <= 22 THEN soil fertility grade = 1; Rule 2: IF phosphorus> 21.775 AND salt content <= 1 AND light radiation intensity <= 2 AND soil drought resistance <= 3 AND phosphorus <= 22.3 AND soil moisture> 22 THEN soil fertility grade = 3;

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Suitable for crop rotation Ground water

Topographic Position Topographic Position

Light radiation

Annual rainfall

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Light radiation 2

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cation 3

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Fig. 3. Part of the fertility level decision tree

Rule 3: IF phosphorus> 21.775 AND salt content <= 1 AND light radiation intensity <= 2 AND soil drought resistance <= 3 AND phosphorus> 22.3 AND Humus layer <= 3 AND topographic position <= 18 AND PH Value <= 7.25 THEN soil fertility grade = 1 To compare the feasibility and advance of this experimental methods, the author uses the same training set and test set, respectively run decision tree models tested that with no clustering, no rough set attribute reduction, clustering and attribute reduction and attribute reduction but no clustering, the comparison results as shown in table 2. Table 2. Comparison of the table the results of several methods Number of leaf nodes

Model accuracy rate%

reduction

The number of decision tree nodes 317

159

92

Removed redundant attribute 7

clustering

No reduction

329

165

90

0

No clustering

reduction

539

270

91

7

No clustering

No reduction

543

272

85

0

Whether clustering

Whether reduction

clustering

4 Rsults and Analysis Experimental results show that: (1) The samples using the clustering method taken, by the decision tree model checking, are more typical, can be used as a standard model of learning samples;

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(2) Using the rough set attribute reduction and decision tree method of combining, reduce the tree's branches, removing 7 redundant attributes, the model accuracy rate is 92%, not only improves the mining efficiency, but also improve the accuracy of the model rate. Sun Weiwei’s studies who evaluate the use of decision tree method for assessing soil quality and grade of the same data sets have shown that the decision tree method is better than exponential product method and gray opened a comprehensive evaluation method, often of the right index and France are very close[5]. The results of this study show that decision tree based on rough set model is better than an ordinary decision tree model. (3) Comparison of the results from the correlation method can be seen: in accuracy, clustering methods are slightly higher than the non-clustering methods, reduction methods are slightly higher than the non-reduction methods, clustering and rough sets method combining are higher than all other methods. More important is the preferred method based on clustering of samples in addition to a large number of redundant samples, attributes reduction method based on rough set e excepts some redundant properties, saving time and space, reducing the complexity of the model. In a word, this paper presents a combining method that clustering and rough set theory is better than other methods in time, space and accuracy, and achieved a good result in Land capability classification.

References 1. Xue, Z., Deng, H., Yang, W., et al.: Based on decision tree and chart level superposition accurate agricultural output chart analysis method. Agricultural Engineering Journal 22(8), 140–144 (2006) 2. Wu, C., Xu, K., Hang, Z., et al.: Decision tree’s data mining method based on rough set. Northeast University Journal (natural sciences version) (05), 481–484 (2006) 3. Zhu, M.: Data Ming, pp. 67–76. Chinese Scientific and Technical University Publisher, He Fei (2002) 4. Pawlak, Z.: Rough Sets. Internet Compute. Inform. Sci. 11(5), 341–356 (1982) 5. Sun, W., Hu, Y., Liu, X., et al.: For decisions the soil quality level research. z Journal of South China Agricultural University (Jcr Science edition) 7, 110–118 (2005)

Research on the Method of Geospatial Information Intelligent Search Based on Search Intention Model Jingbo Liu1,2,*, Jian Wang2, and Bingbo Gao2 1

School of Automation, University of Electronic Science and Technology of China 611731, Chengdu, P. R. China 2 Beijing Research Center for Information Technology in Agriculture, 100097, Beijing, P. R. China [email protected]

Abstract. The paper, focusing on the problem that the overall correlation level of the research result is low in the current geospatial data search system, establishes users’ search intention models, researches the intelligent and professional geospatial information search method, and makes use of spatial cognition theory and knowledge engineering methods to make up for the shortcomings of the lack of semantic information in the traditional keyword-based information retrieval. The geospatial information of towns and villages spatial planning field involves multitudinous theme and has complex structure and large scale. There has always been lack of appropriate method to complete the information search and processing activities. This paper takes geospatial data search in towns and villages spatial planning field for example, constructs the professional knowledge base by knowledge engineering, implements semantic reasoning and establishes users’ search intentions model by combining the characteristics of geospatial information. The ultimate goal is to understand users’ search intentions as far as possible and improve the accuracy of geospatial data search. Keywords: geospatial information search, knowledge engineering, search intention model.

1 Introduction With the advent of Internet era, the amount of available geospatial information on the Internet has dramatically grown. Compared with non-spatial information, geospatial information has more complex composition. How to improve the overall correlation level of geospatial information between search results and users’ search intentions is one of the key problems to be solved. Metadata is defined as data about data. For geospatial data, metadata can describe the content, quality, condition and other relevant background information about characteristics. The search models of existing geospatial information search systems are *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 415–424, 2011. © IFIP International Federation for Information Processing 2011

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usually designed based on geospatial information metadata contents. These search systems match the related keywords users enter with spatial metadata database and return the results to users. To a certain degree, keywords can express users’ search intentions, but this method has some shortcomings. The keywords submitted to computers are just physical sign in this method. It seriously dissevers the semantic relationship among the words users type and doesn’t contain any semantic information in search process, leading to satisfactory search results [1]. Since this query mechanism is lack of intelligence and knowledge inference, it is more and more difficult to meet the growing needs. In the field of the towns and villages spatial planning field, planners need to collect and analyze a variety of geospatial information. The geospatial information involves multitudinous theme and has complex structure and large scale. However, there has always been lack of appropriate method to complete the information search and processing activities. After analyzing the shortcomings of the traditional geospatial information search systems, the paper puts forward a semantic-based intelligent search method.

2 Intention Model 2.1 The Composition of Intention Model In the theoretical model of intelligent agents, belief, desire and intention are regarded as the basic psychological elements of intelligent agents. It is generally believed that faith describes subject’s basic view on the environment and the subject uses it to express any future possible situation. Desire is obtained directly from belief, including the subject’s judgement on future situation. Intention is the goal or sub-goal chosen to achieve. In other words, the current goal or sub-goal is the subject’s intention. Intention model is the formal expression of intention, that is to say how to reflect the target models users choose [2]. Geospatial data describes three basic characteristics of various kinds of phenomenon in the real world: spatial characteristic, time characteristic and thematic attribute characteristic. In this study, considering the characteristics of geospatial information, the search intention model consists of four sub-models: topic intention sub-model, measure intention sub-model, space intention sub-model and time intention sub-model. Each sub-model has an input interface used to express users’ different search intentions. Especially, in the topic intention sub-model and measure intention sub-model, the search intention users type will be submitted to inference engine. Then inference engine will reason with the knowledge base built in a knowledge engineering method. The knowledge base consists of measure ontology, planning domain ontology and inference rules. 2.2 Ontology Overview An ontology is a formal, explicit specification of a shared conceptualization [3]. ‘Formal’ refers to the fact that the ontology should be machine readable, which

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excludes natural language. ‘Explicit’ means that the type of concepts used, and the constraints on their use are explicitly defined. ‘Shared’ reflects the notion that an ontology captures consensual knowledge, that is, it is not private to some individual, but accepted by a group. Ontology is suitable for knowledge representation, knowledge sharing and interoperability [4]. 2.3 Introducing the Measure Ontology into the Measure Sub-module This paper introduces measure ontology and inference rules to express users’ measure search intentions. There is no concept of measure inference in traditional geospatial information search system. Users need to use measure knowledge, such as scale and precision, to determine whether the current data could meet their needs. Because of the multiple form of measure, especially the complicated dependent and interactive relationship among the different measure variables, it is difficult for the majority of non-geographic experts to judge the appropriateness of the measure. Adding measure ontology to geospatial information search system not only simplifies users’ maneuverability, but also improves the accuracy and efficiency. As a new constraint variable, measure is effective in narrowing search space and enhancing the search efficiency. The core issue to be solved during introducing measure inference into search process is the inconsistency between the measure variables users type and the measure variables to be searched. Consequently, measure sub-module introduces the measure ontology and inference rules that inference engine can ‘understand’ and realizes intelligent search. 2.4 Introducing the Planning Domain Ontology into the Topic Sub-module The shortage of study on relevant information at the conceptual level, including the lack of information standards and domain ontology, leads to the unsatisfactory results to some degree. Using domain ontology to create semantic tagging for metadata and support semantic-based information search is a method to be mature. The development and application of the planning domain ontology will be used as the basis of the information intelligent process. This study seeks to establish like intelligent information search and process system for space planners. We firstly introduce domain ontology to assist users to express the topic intentions, and then create semantic tagging for metadata by the ontology.

3 The System Design 3.1 Overall Design The system consists of system interface, knowledge base, inference module, search module, metadata database and spatial information database. Figure1 shows the system framework.

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Fig. 1. System framework

The basic process of geospatial information search based on the intention model is as follows. Firstly, with the help of domain experts, we construct the planning domain ontology and the measure ontology. Secondly, we create semantic tagging for the metadata of the metadata database with the two ontologies. When users type search intention through the search interface, the system will reason out relevant data with the help of the ontology and inference rules and return the results to users. 3.2 System Interface User interface module is composed of space module interface, time module interface, topic module interface, measure module interface and results feedback interface. The topic interface and measure interface are the core interfaces of the search system. With the two interfaces, the system can fully understand the users’ search intention to improve its accuracy in an intelligent and professional geospatial information search method. In the space interface, users can specify the spatial location of space objects and the spatial relationship among adjacent space objects. In the time interface, users can specify the time span of geospatial information.

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Information search is the process of interacting with search system dynamically. Users often could not achieve their goals at a time while searching information. They need to option result feedback and interact with search system several times. Consequently, in this paper, the search system gets the related information by the inference engine and search engine, returns the results to users and allows users to select a second time or more. 3.3 Knowledge Base Knowledge base is the collection of facts, rules and concepts. In terms of knowledge storage, the institution to store and manage the knowledge is called knowledge base [5]. Knowledge base system involves a key issue: knowledge representation. Knowledge representation is the key problem in knowledge base system. It should represent knowledge in the way that computers can "understand". In this paper, we realize knowledge representation with ontologies described by OWL [6]. OWL is a language proposed by the W3C consortium to be used in the "semantic-web" environment for the representation of ontologies. This language is based in the previous DAML+OIL language and it is defined using RDF. The inference rules in the knowledge base are written by SWRL [7]. The characteristic of SWRL is that it can be regarded as a part of ontology, and be supported by a lot of reasoning tools. 3.4 Inference Module Inference engine is the core part of the inference module. It is used to drive answer from the knowledge base for the ultimate purpose of formulating new conclusions. Knowledge reasoning is the process that inference engine reasons by the existing knowledge and returns the results to users in a friendly way. 3.5 Geospatial Information Database and Metadata Database Geospatial information database is used to store geospatial information. Geospatial metadata is a summary document providing content, quality, type, creation, and spatial information about a data set. From a data management perspective, metadata is important to maintain an organization's investment in spatial data. Metadata database is regarded as the repository into which all geospatial information metadata documents are stored. In this paper, we create semantic tagging for geospatial information metadata with the above ontologies and inference rules.

4 The Realization of the System 4.1 The Realization of the Knowledge Base The knowledge base includes the planning domain ontology, the measure ontology and the inference rules. Establishing domain ontology is the process of abstracting and refining the objects in the real world and mapping them to the computer world [8].

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When establishing planning domain ontology, we should consider planning requirements, go by background knowledge, research the information space planners need and reference the planning field standards and related metadata specifications. In order to realize sharing, the concept must comply with certain professional standards, or it will lose the significance of establishing ontology. The planning domain ontology is used to help users to express their topic intentions. Intelligent search is mainly reflected in the reasoning process with the help of the inference rules which is written by SWRL. We lay down two kinds of inference rules so that the system can achieve two kinds of reasoning. Firstly, when planners search information in any planning step, they can get various information suggestions of the step. Secondly, planners can get the upper and lower categories of the domain ontology when they select each information category. The measure ontology in this system is composed of the following five categories: scale, spatial resolution, dimension, data accuracy and geometric dimensionality. The scale instances of the measure ontology are composed of common scales, ranging from 1:500 to 1:500 million. The spatial resolution instances consist of common resolutions. Considering the realization of reasoning, the dimension instances only include the length dimensions and the accuracy instances only include the length precisions. The various kinds of measures are not isolated, with some transformational relations. In the search system, we lays down a series of inference rules according to practical application so that users can utilize one or more instances to deduce other corresponding examples by means of inference engine. When users type their search intentions, the system will form a measure variable constraint set according to the users’ queries and return relevant data from database with the constraint set. 4.2 The Realization of the Inference Module Jena inference engine is used as the inference engine in the search system. Jena is a Java framework for building Semantic Web applications. It provides a programmatic environment for RDF, RDFS and OWL, SPARQL and includes a rule-based inference engine. Jena is open source and grown out of work with the HP Labs Semantic Web Programme. In the system, inference engine queries ontologies by SPRAQL (simple Protocol and RDF Query Language). SPRAQL is a query language defined for RDF data model [9]. SPRAQL model contains a collection of triples, called the basic graph pattern. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. SPARQL contains capabilities for querying required and optional graph patterns along with their conjunctions and disjunctions. SPARQL also supports extensible value testing and constraining queries by source RDF graph. The results of SPARQL queries can be results sets or RDF graphs. 4.3 The Realization of the Metadata Database Metadata database is a database of data about data. In this study, the spatial information metadata standard is ISO19139, which is proposed by ISOTC211 group. It defines

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more than 300 metadata elements, being mainly used for geographic information and spatial data. The elements are divided into 14 packages, including identification information, data quality information, description information, space and time reference system information, data content information, and so on. Identification information of the ISO19139 contains the topic element used to describe the phenomenon of space or space target attributes. The planning domain ontology is used to create semantic tagging for the topic element of the metadata so that the search system could support semantic reasoning [10]. ISO19139 metadata standard only contains scale and spatial resolution. It is insufficient to support the above five measure types of measure ontology. Consequently, this study expands the measure of the standard, including scale, spatial resolution, dimension, data accuracy and geometric dimensionality. Finally, we create semantic tagging for geospatial information metadata with the above mentioned measure ontology.

5 The System Evaluation Result In this test, we choose common measure and topic instances whose corresponding data exist in the database, and then judge the relativity of search results manually. Taking the maneuverability into account, this experiment only has two kinds of evaluation results: correlated and uncorrelated. This paper compares the keyword-based search with the ontology-based search (Table 1) and takes recall ratio and precision ratio as the level of the efficiency. Recall ratio is the number of records retrieved in a database search divided by the total number of relevant records in the database. Precision ratio is the number of relevant retrieved records divided by the total number retrieved in a database search. Table 1. Test Question Set Number 1 2 3 4 5 6 7 8 9 10

Module Measure Module Measure Module Measure Module Measure Module Measure Module Topic Module Topic Module Topic Module Topic Module Topic Module

Query Input scale 1:10000 scale 1:1000000 precision 10.0m dimension m resolution 50m population industry climate highway water resources

As it can be seen from Figure 2, the ontology-based semantic search is higher than the search based on keywords at the recall ratio. There is no concept of measure inference in traditional geospatial information search system, leading to a low recall ratio. But, in this search system, when users input measure needs, the search system will

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introduces the measure ontology and inference rules to infer user's measure search intentions and mine more implicit concept. For example, in this test, when users input 1:100000 in scale, the system will return the other four corresponding measure items. Besides, after having introduced the planning domain ontology to assist users to express the topic intentions, the search system can infer the relevant topics based on the topic users input. Therefore, the recall ration is higher than traditional method.

Fig. 2. Recall ratio result

Precision Ratio 100% 90% 80% 70% ontology

60% 50% 40%

keyword

30% 20% 10% 0

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Fig. 3. Precision ratio result

9

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Figure 3 shows that the search system based on the measure ontology is higher than the search system based on keywords at the precision ratio. Adding measure ontology to geospatial information search system not only simplifies user’s maneuverability, but also improves the accuracy. As a new constraint variable, measure is effective in narrowing search space. We can also conclude that the search based on the planning domain ontology is higher than the search based on keywords at the precision ratio in many instances. But, in some cases, the system appears lower precision ratio for the reason that it reasons out too much related concept. Therefore, the search system provides users a second choice to improve the precision ratio. In summary, the method is superior to the traditional method at the precision ratio.

6 Conclusions In this paper, we firstly analyzed the deficiencies of current geospatial information search systems. Then, combining with the spatial information search of the villages and towns spatial planning field, we designed and developed a spatial information search system based on search intention model. Considering the characteristics of geospatial information, the search intention model was composed of topic sub-module, measure sub-module, space sub-module and time sub-module. In topic sub-module and measure sub-module, we established a knowledge base which includes ontologies and inference rules, and then realized the knowledge reasoning. Compared with traditional keyword-based search, this search mechanism can find implicit information and raise the recall ratio and precision ratio to some degree. There are still some shortcomings in the search system. In future work, we will dig users’ search requirements deeply and improve the inference rules. Acknowledgements. This research was supported by National key technology R&D program (2006BAJ05A09) and State science and technology support projects (2008BAB38B04). The first author is grateful to Beijing Research Center for Information Technology in Agriculture for providing him a good research condition.

References 1. Halevy, A.Y.: Theory of answering queries using views. SIGMOD Record 29(4), 40–47 (2000) 2. Rao, A., Georgeff, M.: BDI agents: From theory to practice. In: Proceedings of the 1st International Conference on Multi-Agent Systems (ICMAS 1995), San Fransisco (1995) 3. Stuber, R., Benjamins, V.R., Fensel, D.: Knowledge Engineering, Principles and Methods. Data and Knowledge Engineering 25(1-2), 161–197 (1998) 4. Saias, J., Quaresma, P.: A methodology to create ontology-based information retrieval systems. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 424–434. Springer, Heidelberg (2003) 5. Yu, Y., Zhang, Y.: Knowledge Base System Study Based on Ontology. Journal of Information 22(7) (2003)

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6. Owl web ontology language guide. Technical report, http://www.w3.org/TR/owl-guide/ 7. Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosof, B., Dean, M.: SWRL: A Semantic Web Rule Language Combining OWL and RuleML. W3C MemberSubmission (2004) 8. Noy, N.F., McGuinness, D.L.: Ontology Development 101: A Guide to Creating Your First Ontology. Stanford Medical Informatics, Stanford CA (2001) 9. The Sematics of SPARQL, http://www.inf.unibz.it/krdb/w3c/sparql/ 10. Cho, H., Ishoda, T.: Designing Metadata with Existing Ontologies. IEIC Technical Report (Institute of Electronics, Information and Communication Engineers) (2005)

Research on the Theory and Methods for Similarity Calculation of Rough Formal Concept in Missing-Value Context* Wang Kai, Li Shao-Wen**, Zhang You-Hua, and Liu Chao School of Information and Computer Science, Anhui Agricultural University, Hefei, China [email protected]

Abstract. In this paper, for the low similarity computation accuracy of concept in the field of agriculture ontology mapping, formal concept analysis theory and rough set theory are introduced to similarity computation. Jointly considering attribute hierarchies in concept lattice, the semantic hierarchy of the concepts are weighted differently, and the theory and methods of similarity calculation of rough formal concept in missing-value context is given. Finally, similarity computing model is prospected. Experimental results show the model has a high computational accuracy.

1 Introduction With the explosive growth of knowledge, representation, sharing and exchanging of which has become urgent to solve. Ontology is a shared concept of a clear explanation standardization, which makes it possible to resolve all these issues. It has increasingly become important component of knowledge engineering, knowledge management, information retrieval and semantic Web. Formal concept analysis theory is a mathematical method put by Professor Wille R, which comes from the understanding of concept related to the areas of philosophy, and reflects the hierarchy between the concepts. Rough set theory is a theory of data analysis proposed by Z. Pawlak, which uses the approximate relationship between the upper and lower use of data to describe the uncertainty of information. Now has been widely used in decision analysis, pattern recognition, machine learning and knowledge discovery. Wu Qiang broadens the scope of formal concept in the paper [1], applying rough set to Formal concept analysis area, to definite the concept which can not be studied. Yang wenping discusses the rough approximation of formal concept, proposing to use the rough set method to solve the upper and lower approximation of rough concept, which theoretically proves that the result of this theory is equivalent to the approximate extension of other ones. Kent gives the analysis of the approximate operator model of concept lattice, illustrates the relationship between the approximation operators. *

Fund projects: National High Technology Research and Development Program of China (No. 2006AA10Z249); National Natural Science Foundation of China (No. 30971091, 30800663). ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 425–433, 2011. © IFIP International Federation for Information Processing 2011

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Yu-Ping studies the disorder duality relations between the concept pairs, mainly for the dependence among attributes, modeling for the concept lattice in the tolerance approximation space. Shao [2] synthesizes the basic rough model and reduction concept lattice model, getting the corresponding functional dependency algorithms, by the theory of rough set operations. All the research above mainly focused on the discovery of the common characteristics between rough set model and concept lattice model, and theoretical feasibility, ignoring the characteristics of the concept of ontology structure, lack of the measures of similarity between the concept of ontology in rough environment. The phenomenon of missing values is a widespread problem in knowledge engineering, which mainly related to the expression and processing of uncertain concepts. With the rapid development of the Semantic Web, the number of domain ontology gradually increases, to some extent, causes a sharp increase of uncertain concepts, which seriously affects the sharing and reuse of knowledge between domain ontologies. Ontology mapping is an effective method to solve the problem foregoing, the key of which is to get conceptual similarity. Due to the reasons above-mentioned, this article uses upper and lower approximation of rough set theory, proposes rough formal concept in missing-value context, put forward the improved theory and methods for similarity calculation of rough formal concept in missing-value context.

2 Related Concepts 2.1 Rough Set Theory Rough Set Theory is some kind of mathematical tool dealing with fuzzy and uncertain knowledge. The main idea of this theory is to access decision-making or classification rules, in the premise of maintaining the same classification, by knowledge reduction. Rough Set Theory generally refers to some undefined subset, regularly be approximation defined by two precision sets (upper approximation and lower approximation). Definition 1. For a given knowledge base K= (U, R), U refers to non-empty set of objects; R is a family of equivalence relations based on U. If P ⊆ R , and P is not empty, then the intersection of all equivalence relations in P set is also an equivalence relation, called indiscernibility relations. For each subset X ⊆ U and equivalence relation R, we can get these as follows:

R X = ∪ {Y ∈ U / R | Y ⊆ X }

(1)

R X = ∪ {Y ∈ U / R | Y ∩ X ≠ ∅}

(2)

They are called the upper approximation and lower approximation of relation P respectively. If the upper and lower approximation are not equal, X is called the rough set of R, otherwise, called the defined or precise set of R.

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2.2 Theory of Formal Concept Analysis Formal Concept Analysis are widely used in many area, such as data analysis and rule extraction, the core of which is the concept lattice, that is the concept hierarchy based on binary relation. Concepts exist in the form of relations of posets in the lattice. For each concept, they are composed of extensions and intensions of their own. The relationship of the concept of upper and lower nodes is the one of father and son. The concept hierarchy between concepts can be clearly seen by the Hasse map. The visualization of data could be easily got.

∈ ）

Definition 2. There is a set of L, and a,b,c L, the prerequisites that binary relation ≤ is the poset of L are:1 a ≤ a (reflexivity); 2 a ≤ b and b ≤ a ⇒ a=b anti-symmetry); 3) a ≤ b and b ≤ c ⇒ a ≤ c (transitivity).

）

（

∈

Definition 3. (Infimum and Supremum) There is a subset S L in the poset (L, ≤ ), and then arbitrary element in set L is called the lower bound of subset S. And if it existing a largest element, we call it Infimum; similarly, the definition of Supremum could be got.

（） ∈ ∈

Definition 4. (Lattice) The poset L, ≤ can be called Lattice only if it could Satisfy the following requirements: 1 For any a, b L, a ∧ b and a ∨ b are both exist; 2) Infimum and Supremum are presence for any a, b L.

）

2.3 Missing-Value Context and Rough Formal Concept

（

）

Ordinary formal context K= G, M, I is a triple context, G is a set of objects, M is a set of attributes, and I is a binary relation such that I ⊆ G × M , and it is identified. However, in real life due to lack of information or unpredictable cases happening, knowledge we need can not be obtained in the normal way, which makes it hard to express. Based on the reasons above it is necessary to take specially steps to deal with these problems. First of all, the definition of missing-value context is given. When the relationship between certain object g and property m is uncertain, I ⊆ (g, m) not being judged, it could be called being missing-value [3].

（，，）

Definition 5. (Missing-Value Context) Missing-Value Context T= U A R is a triple context, U={o1,o2, ……on} is a set of objects, A={a1,a2, ……an} is set of Attributes. R is an uncertain relationship between U and A. Better to explain the definition of rough formal concept, formal concept is given firstly.

（） ∈ ， ∈ ；）

Definition 6. (Formal Concept) For a given formal context K= G, M, I), concept (A, B) is called Formal Concept, if it satisfies the conditions that 1 A G B M 2 (A, B I 3 A’=B, B’=A, among which A’ represents the shared attribute sets of objects A, B’ is on behalf of the shared object sets of attributes B.

) ∈；）

(

Definition 7. (Rough Formal Concept) For a given Missing-Value Context T= U, A, R), only if the concept meets the conditions as follows, it could be named Rough Formal Concept: 1) A G, B M; 2) A × B is a rough set based on relation I.

∈ ∈

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3 Traditional Similarity Calculation Model 3.1 Tversky Ratio Model Tversky measured the degree of similarity between concepts by using the shared feature sets of entities. The computational model is as follows:

Sin( m, n) =

f (M ∩ N ) f (M ∩ N ) + α ⋅ f (M − N ) + β ⋅ f ( N − M )

(3)

Among which Sin (m, n) denotes the similarity between concept m and n; M and N are the feature sets of m and n; f is the metric function of feature sets; (M-N) indicates the feature sets which lies in M rather than in N; Similarly, (N-M) indicates the feature sets which lies in N rather than in M; Parameter α , β adjust the cases dealing with the asymmetric feature sets. 3.2 The Similarity Calculation Model Based on Formal Concept Analysis Based on the Tversky Ratio Model, the similarity calculation model to the basis of Formal Concept Analysis is proposed by Souza and Davis, which uses the mathematical operation ∧ and ∨ to calculate the irreducible infimum. | (m ∨ n) | ∧

Sin ( m , n ) =

| ( m ∨ n ) | +α | ( m − n ) | + (1 − α ) | ( n − m ) | ∧

∧

∧

(4)

m ∨ n represents the supremum of formal concept m and n; (m ∨ n) ∧ means the element sets of irreducible infimum of the supremum features; (m-n) ∧ denotes the irreducible infimum element sets which lies in m instead of n; and vice versa. 3.3 The Similarity Calculation Model Based on Information Based on the Tversky Ratio Model, Formica put forward the similarity calculation model that based on information, which makes use of the similar map of concepts in domain knowledge. S in

(( M 1

, N 1 ) , ( M 2 , N 2 )) =

| M 1∩ M 2 |

λ

×ω +[

1

θ

• m ax (

∑

f ( m , n ) ) ] × (1 − ω )

(5)

< m ,n >∈ P

M1 ∩ N1 is the number of the same objects in the pairs of the concepts; λ is the bigger value to be compared with the numerical objects; θ is the larger value to be compared with the numerical attributes;

∑

< m , n >∈P

f (m, n) is the summation of the concepts whose

attributes matches one another in the concept similarity map; ω is the weighting factor adjusting different cases. It is not difficult to draw conclusions from the model above that many scholars just improved the model raised by Tversky. The semantic meaning of similarity model is enriched by pulling in the Rough Set Theory and Formal Concept Analysis. But there

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are still limitations, specifically expressed in two aspects: 1) simply counting the numbers of upper concept nodes, lacking of accuracy measurement, 2) no consideration about the differences between the feature properties of different concept level, only depending on computing the semantic distance between concepts to determine the value of similarity.

4 Rough Formal Concept Similarity Calculation Model 4.1 Equivalence Relation, Irreducible Supremum and Infimum in Formal Concept Analysis Formal concept is formed of objects and attributes, which can determine the equivalence relation of the object G and attribute M by the formal context K= (G, M, I). For every non-empty set of formal concept, there always exists the sole largest sub-concept and smallest parent concept, which is called Supremum and Infimum. If existing the only element not expressed by the largest sub-concept of others, it is named Irreducible Supremum element; Similarly, it is easy to get the definition of Irreducible Infimum element. 4.2 Improved Rough Concept Lattice Similarity Calculation Model On the one hand, by observing the structure of the Hasse map, we know that the included attributes of the upper parent node in concept lattice is the minimal subset attributes of the lower sub-class nodes. And if two nodes have the same feature properties, the conclusion that they must have the same upper parent node can be got. On the other hand, from the taxonomic point of view, the similarity degree between underlying object is higher than the one between upper layer object. Based on the above analysis, the semantic parameters of the upper layer is greater than the one of the lower layer. The improved rough concept lattice similarity calculation model is given below. n

f

R SIM

= (( A 1 _ , B 1 _ ), ( A 2 _ , B 2 _ )) =

|

A1_ ∩ γ

A

2_

|

×α +

∑X W i

i =1 n

∑Y W i =1

i

i

× (1 − α )

(6)

i

Specific parameters are defined as follows: Xi=fi(B1_ ∩ B2_) represents the shared property features of the rough concept lattice in level i; Yi=fi(B1_ ∩ B2_)+fi(B1_ − B2_)+fi(B2_ − B1_) denotes the property features of the rough concept lattice in level i; Wi means the weight of the conceptual elements in level i. A1_ is the lower approximation concept (A1_, B1_)’s object, while (A1_,B1_) is the rough formal concept of (A1, B1), so as the A2_; B1_ is the upper approximation concept (A1_, B1_)’s attribute; Parameter α is the weighting factor adjusting accuracy of the model.

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Table 1. Formal Context of the Domain Ontology Modeling of Tea Pests Dark compo-und eyes

Pest 1 Pest 2 Pest 3 Pest 4 Pest 5

× ×

Easy to control Pest 1 Pest 2 Pest 3 Pest 4 Pest 5

× ×

Light skin color

Short reproductive cycle

× ×

× × × ×

Widely distribute-d

×

Regional

Serious harm of nymph

Severely destruction

× × Lots of feet

Harm for shoots

× ×

×

×

× ×

The weight of the different level is determined by 1/2i-1, known through the literature [4], among which i stands for the number of the level. In order to better explain the model above, a formal context of the domain ontology modeling of tea pests is given below, shown as table 1. Based on the literature[4], the generating algorithm for building concept lattice to the basis of matrix column rank with attribute priority, by which uses the matrix column rank of the concept and the union operation of the concept pairs to generate rough formal concepts having hierarchical structure. The hierarchical concepts corresponding with table1 are just as follows. Layer 1 ：C1

(G , ∅)

Layer 2 ：C2{{ Pest 1, Pest 2, Pest 4, Pest 5}, { Harm for shoots }}, C3{{ Pest 1, Pest 2, Pest 3, Pest 4}, { Short reproductive cycle }}; Layer 3 ：C4{{ Pest 1, Pest 2, Pest 4}, { Harm for shoots, Short reproductive cycle }}, C5{{ Pest 1, Pest 2, Pest 5}, { Easy to control }}； Layer 4 ：C6{{ Pest 1, Pest 2}, { Harm for shoots，Short reproductive cycle, Easy to control，Dark compound eyes }}, C7{{ Pest 3，Pest 4}, { Light skin color, Short reproductive cycle }}； Layer 5 ：C8{{ Pest 5}, { Lots of feet, Harm for shoots, Easy to control, Serious harm of nymph }}, C9{{ Pest 3}, { Light skin color, Severely destruction, Widely

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distributed，Short reproductive cycle }}, C10{{ Pest 4}, { Light skin color, Harm for shoots, Regional, Short reproductive cycle }}； Layer 6 ：C11

(∅, M )

The rough formal concept with the hierarchical structure is generated just as the map 1. The weight of different layers is given in table 2. Using the formula above, the similarity between the concept nodes can be calculated. For example, the similarity value between concept C2 and C6 is got by setting the parameter α =0.25.

1 1 1 1 × 1 + 0 × + 0 × + 0 × 2 2 4 8 fRSIM (C2，C6)= × 0.25+ =0.34 1 1 1 4 (1+3)×1+(0+1)× +(0+1)× +(0+0)× 2 4 8

Fig. 1. Concept lattice with hierarchical structure

Layer 1 Layer 2 Layer 3 Layer 4

Harm for shoots, Short reproductive cycle Easy to control

Weight：1

Light skin color, Dark compound eyes Regional, Severely destruction, Widely distributed, Lots of feet, Serious harm of nymph

Weight：1/4

Fig. 2. Property values of the related levels

Weight：1/2

Weight：1/8

(7)

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C2 C3 C4 C5 C6 C7 C8 C9 C10

C2

C3

C4

C5

C6

C7

C8

C9

C10

1 0.00 0.67 0.00 0.40 0.00 0.40 0.00 0.40

0.19 1 0.67 0.00 0.40 0.67 0.00 0.40 0.40

0.69 0.69 1 0.00 0.67 0.50 0.33 0.33 0.67

0.19 0.13 0.17 1 0.40 0.00 0.40 0.00 0.00

0.34 0.49 0.90 0.35 1 0.33 0.50 0.25 0.50

0.06 0.92 0.52 0.00 0.33 1 0.00 0.67 0.67

0.63 0.00 0.36 0.52 0.57 0.00 1 0.00 0.25

0.00 0.46 0.40 0.00 0.36 0.95 0.00 1 0.50

0.4 0.4 0.9 0.0 0.8 0.8 0.3 0.4 1

Fig. 3.Values of the similarity between concepts

By using the similarity way, all the objects and attributes which are non-empty could be calculated. In order to analyze these data we get, the result of the improved model and the one of the Souza model are put together to make comparition shown as the table 3. The table is divided into two parts by the diagonal of the number one. The value of the upper triangular is the result of the improved similarity of rough formal concepts. The value of the lower triangular is the result of the similarity of the Souza model.

5 Model Analysis For the reason that precision and recall always changes with the mutative thresholds, they can not be used to analyze the result of the similarity accurately. Based on the values in table 3, the concept node in the high-level such as C2 and its child node C4 are chosen to analyze the relationship between other concept nodes. Compared to the Souza model, the conclusion that the improved model is better could be got at two aspects. On the one hand, the result of accuracy is improved. The values of the improved model increase to the different degrees, for jointly considering attribute hierarchies in concept lattice and the relations between objects and attributes. On the other hand, irrelevant concept pairs decrease. Due to the concepts in the domain area have certain similar characteristics, all the values of the concept pairs could not be zero. The improved model cuts down the number of the pairs with zero effectively, enhancing the measurement accuracy between concept pairs.

6 Conclusions and Future Work The paper puts forward the theory and methods for similarity calculation of rough formal concept in missing-value content, in which formal concept analysis theory and rough set theory are introduced to similarity computation. Jointly considering attribute hierarchies in concept lattice, the semantic hierarchies of the concepts are weighted

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differently. Experimental results show the model has a high computational accuracy. The model above proposes a practical theory and methods to merge domain ontology, helping to raising the accuracy of ontology integration.

References 1. Wu, Q., Liu, Z.-t.: The Rough Concept in FCA. Journal of Chinese Computer Systems 26, 1563–1565 (2005) 2. Shao, M.: Set approximations in fuzzy formal concept analysis. In: Fuzzy Sets and Systems, Beijing, pp. 2627–2640 (2007) 3. Xie, Z.-p, Liu, Z.-t: Concept Analysis and Knowledge Acquisition in Missing-Context. Computer Science, Beijing, 36–39 (2000) 4. Mao, H., Dou, L.-l.: An Algorithm of Concept Lattice Based on Matrix Column Rank with Attribute Priority. Journal of Hebei University (Natural Science Edition), Shi jiazhuang 29, 130–132 (2009)

Research on Traceability System of Food Safety Based on PDF417 Two-Dimensional Bar Code Shipu Xu1, Muhua Liu2, Jingyin Zhao1, Tao Yuan1, and Yunsheng Wang1,* 1 Technology & Engineering Research Center for Digital Agriculture, Shanghai Academy of Agricultural Sciences, Shanghai 201106, China 2 Engineering College, Jiangxi Agricultural University, Nanchang, Jiangxi 330045, China [email protected]

Abstract. In this paper, through the research on two-dimensional bar codes of food source tracking; a set of traceability tracking system was designed based on the two bar coding algorithm and decoding algorithms. This system could generate PDF417 code based on food production, logistics and transport, supermarket and other storage information. Consumers could identify PDF417 code through a client based on B/S architecture so that a simple process of food traceability has been completed. Keywords: Food source tracking, Food safety, B/S, Two-dimensional bar code.

1 Introduction In recent years, food safety is always a focus of governments, food companies, news media and the consumers, such as foreign BSE, GMF, domestic Melamine scandal, Sudan, etc. In 2009, the Food Safety Law adopted on Two Sessions shows the importance of food safety. As more serious and emergent of food safety incidents were appeared, the concept of food traceability was mentioned once again. Food traceability is a process, in which the supply chains of participants take use of information technology to carry out the information sharing or to track food supply chains. Once a problem was discovered in some part of the food supply chains, we could find out the root of the problem using the information technology from downstream to upstream. There would be another relative word—traceability. It means that the system can provide backtracking mechanism at all possible steps, such as food growing period, breeding, processing, transportation and sale which involved in the process of products providing. So the consumers could decide what to buy according to this information related to the origin and transport. The application of backtracking mechanism was more mature and widespread in the EU and Japan than our country. Currently, there are two popular traceability technologies in the world, including isotopes tracing and barcode technology. Isotope tracing technique is to distinguish various types of food and their possible sources, depending on isotopic composition in *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 434–440, 2011. © IFIP International Federation for Information Processing 2011

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vivo of which is affected by the factors of climate, environment, and type of biological metabolism [1]. The basic principle and foundation of this technique is based on the natural isotope fractionation effects. AS the information carrier, the bar code technology is a traceable method from downstream to upstream. This method is widely used in the world, and it also reached a certain high degree of maturity. Different types of bar code offered different origins of the principle. In China, traceability of food safety technology develops late. There was only the concept of food traceability in the late 1970s, but it has a fairly rapid development. During the Tenth Five-Year Plan period, independent innovation capacity original innovation of food safety technology and recreation capacity of digestion and absorption are greatly enhanced through the implementation of food safety science and technology projects. In recent years, due to the repeated food safety problems, the government intensifies the monitoring of food safety but the work is just beginning. As the feature of decentralized management of food security monitoring, the uniform national food safety traceability system has not been established. The stage of research and simulation is presented in the sector, local department and company. At present, there is a relatively standard of food tracking and traceability technology which is introduced by the ANCC (Article Numbering Country Centre); but it has not carried out specific research on a particular aspect of the traceability technology. During Spring Festival of China in 2010, the two-dimensional bar code has a good use for security identification. There has been 80 years since the bar code was invented. With the advance of technology, development of bar code is maturing and people’s lives are more convenient. However, there is a separatist party because of the different characteristics in areas. Currently, bar code has more limited application which is only used in goods, logistics, library management, medical treatment and so on. Bar codes are different in different aspects. The same series of information cannot be shared between each other. The two-dimensional bar code which is emerging in these years can solve this problem well.

2 Coding of PDF417 As shown in Figure 1, each of PDF417 code includes 3~90 layers and each layer is composed by Start/End, Left/Right Row Indicator Code words and 1~30 symbol characters. Each symbol character is consisted of 17 modules, including the four black bars and four white bars [3]. As the number of layers and each symbolic in a layer is uncertain, the PDF417 can suit different needs. As shown in Figure 1, the PDF417 code is composed by a symbol for each character which has four black bars (The black part in Figure 2, b1~b4) and four white bars (The white part in Figure 2, s1~s4). From the Start, each bars contains 1~6 modules from left to right. In a symbol character, there are a total of 17 modules in every bar. The PDF417 symbol set is formed by the three clusters. Each cluster contains 929 PDF417 codeword in different bars. In each cluster, each symbol corresponds to a unique code word characters which is ranging from 0 to 928.In PDF417, the symbol characters are used in cluster No.0, No.3 and No.6.PDF417 uses only one cluster on each layer of the symbol characters [5]. The same clusters have a repeat in every 3

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layers. Layer 1 uses the symbol character of the 0 cluster, Layer 2 uses the symbol character of the 3 cluster, Layer 3 uses the symbol character of the 6 cluster, and Layer 4 uses the symbol character of the 0 cluster, and so on.

Fig. 1. The Schematic of PDF417. It contains start symbol and terminator, left and right level indicator

Fig. 2. The schematic of symbol characters. In this chart, it contains a symbol for each character which has four black bars and four white bars.

The most important process which is taken by the mix of main text is translating data (character or ASCⅡ) into a symbol in coding with two-dimensional bar code. The corresponding relationship of data and text mode was stored in a database. Text code will be obtained through the corresponding to each character from a database. This text can generate PDF417 code word. The combination formula is C = 30 × H + L(C means code words and H and L means high and low characters in the corresponding character value respectively)[2]. First, the algorithm flow chart of generated codeword is shown in Figure 3. As shown in Figure 3, it can calculate the required error-correcting code word according to the generated code and choose error correction level. The error correction code words generated according to Reed_Solomn algorithm, including the following three steps [4]. First, data polynomial symbol will be established. Its expression is d(x) = dn-1xn-1 + dn-2xn-2 + …+ d1x + d0.The coefficients of this polynomial contains a data code word which has a area code word, including the length of code word symbols, data code words and filled code words. The alignment of each data code words (di,i = 0,…n-1) in the bar code is shown in Table 1. Second, error-correcting code word generating polynomial will be established. The polynomial with k-generator for the codeword is: g(x) = (x-3) (x-32)…(x-3k) = xk + gkk-1 + … + g1x + g0. 1x

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Fig. 3. The algorithm flow chart of generated codeword Table 1. The location diagram of data, row identifier, and error correction code words

Third, for a given set of data code words and a selected level of error correction, error correction code words will be generated. Finally, it arranged in a matrix on which the request was encoded in accordance with the dn-1, dn-2... d0, Ck-1, Ck-2...C1, C0 order. As shown in Table 1, the dn-1=n means the number of data code word. The output encoding can be a text file. Each row in the file corresponds to each line of bar code, and each line is content from Table 1.With the calculated code word, it will be transformed into the corresponding 01 sequence for white and black bars through a piece. Such as 41111315, it will be compiled out of the 11110101111011111.

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Fig. 4. The flow chart of generating a PDF417 image. There are four statuses in this chart. At last, a picture could be output.

After completing these three steps, it will query symbol characters by the data word, Left/Right row indicator, error-correcting code word and each cluster of a line, and drawing a bar code in accordance with the code symbol character. It can be modeled on the one-dimensional bar code. In the design of the traceability of food safety system, the process of encoding is to convert the readable information to a bar code character which is used to draw, and then the additional and related information can be added. The process of coding is shown in Figure 4. The original information which contains text, characters, numbers, images and fingerprints is converted to PDF417 bar code string. The code string is 929 for the domain of integers. After searching the database, it will be converted into symbol characters, and finally a two-dimensional bar code graphics will be drawn through the relevant procedures.

3 Designing of System There are four design principles of this system: 1, Convenient and practical. The first principle of this platform design is convenient, simple to operate, and the system is easy to learn and use, thus it reduces the threshold for using this platform. To some extent, it avoids a difficult situation when promoting this system by the lack of technology.2 Science and reliability. The food safety traceability system makes full use of one-off data from research and market. It could ensured that the information provide is scientific, reliable to meet the needs of all users.3 Three-dimensional, cross and completely. The system uses the information dissemination chain with multiple, three-dimensional and cross.4, High security, high compatibility, easy to maintain and scalable. It ensures security of data backup and recovery automatically by using advanced technology. With a cross-platform technology, all the function modules of this system could be run normally in a variety of operating environment. Thus it could be easy to maintain and manage in later period, easy to upgrade, expand and improve. As a widely used system, the system’s designing goals and requirements, combination of advancement and practicability, and scalability need to be balanced. It can be considered from the following aspects of the system’s logical designing.

① Make clear the construction goals and the user’s needs of this system. In fact, the system can determine the goal of building phases. Only when the goal was clearly defined, we can determine the content and size of system construction. And to formulate a reasonable and ordered solution is the key of success or failure of the system.

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② In the choosing of ‘advanced’ and ‘practicality’, it does not blindly pursue the advancement in technology. It just tries its best to meet the needs of the user when the technology can be achieved. ③ Considering scalability. With the development of society, the progress of information technology and user needs can be changed; the system scalability is referred to a very high level, which requires the structure of rational planning system; and it should be set aside the interface with other systems. Based on the above considerations, the map of the system logic design and framework is shown in Figure 5.

Fig. 5. The map of system logic design and framework. There are three types of user in this system which contains administrator, special users and guest.

According to Figure 5, the logic design and framework map from the following three aspects to illustrate.

① The security and accuracy of core database data. In order to ensure this, the authority of a person who stored such information into production base, transport and supermarket is set to only increase data, but cannot modified and deleted the information. The authority permission is set to place the database on management who has the highest permission. In order to prevent the invasion of hackers and viruses, the interface of information input was designed to C/S structure which is the only way to guarantee data’s security and accuracy. ② A major function of the system is bar code generation module based on C/S structure. The appropriate encoding algorithm of PDF417 two-dimensional bar code was selected in this system. It needs a special note that the PDF417 bar code’s image should be saved as a picture into database in the background.

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③ When PDF417 bar code is identified, there are two types of user groups. One of them is identified as the typical consumer. The convenience and fast of use is what they need mostly; so we choose the better algorithm of PDF417 bar code decoding which reflected in the error correction capability and time. Another type is identified as the user who has a special need, such as members with information at all levels and the secession makers in other departments. They need to draw more information on this PDF417 bar code and develop appropriate policies and regulations; so these users should require higher accuracy of the information. If necessary, such users can connect directly to the database.

4 Conclusion This paper analyzed the current situation of food safety traceability at home and abroad; and the framework map and logic design of the system was released in this paper. The traceability of food safety in the promotion of the tracking system will be restricted by a number of hardware and the narrow use. In future, if the hardware was more mature, this system can be ported to the portable devices, such as mobile phones, PDA and other mobile devices. Under the mature 3G network, the consumer is convenient to use the system.

References 1. MacKay, D.J.C.: Information Theory, Inference, and Learning Algorithms. Cambridge University Press, Cambridge (2003) 2. Pradeep Kumar, N., Rajavel, A.R.: DNA Barcodes can distinguish species of Indian mosquitoes. J. Med. Entomol. 77, 1–7 (2007) 3. Hayes, D.J., Shogren, J.F., Shin, S.Y., Kliebenstein, J.B.: Valuing Food Safety in Experimental Auction Markets. American Journal of Agricultural Economics 77(1), 35–46 (1995) 4. Itkin, S., Martell, J.: A PDF417 Primer. Symbol Technologies, Bohemia (2002) 5. Johnston, R.B., Yap, A.K.C.: Electronic Data Interchange using Two Dimensional Bar Code. HICSS(4) 14(5), 83–91 (1998)

Research and Application of Cultivation-SimulationOptimization Decision Making System for Rapeseed (Brassica napus L.) Hongxin Cao1, Chunlei Zhang2, Baojun Zhang3, Suolao Zhao3, Daokuo Ge1, Baoqing Wang3, Chuanbao Zhu4, David B. Hannaway5, Dawei Zhu1, Juanuan Zhu3, Jinying Sun6, Yan Liu1, Yongxia Liu1, and Xiufang Wei1 1

Institute of Agricultural Resources and Environment Research/Engineering Research Center for Digital Agriculture, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, Jiangsu 2 Institute of Oil Crops Research, Chinese Academy of Agricultural Sciences, Wuhan 430062, Hubei 3 Northwest Sci-Tech University of Agriculture and Forestry, Yangling 712100, Shaanxi, China 4 Liaocheng Municipal Agric Technology Extension Station, Liaocheng, Shandong 252500 5 College of Agricultural Sciences, Oregon State University, Corvallis, Oregon 97331, USA 6 Agricultural Technological Extensive Station of Luntai County, Luntai, 841600, Xinjiang, P.R. China [email protected]

Abstract. The objectives of this study were to develop Rapeseed Cultivation Simulation-Optimization-Decision Making System (Rape-CSODS), and validate it in order to design rapeseed planting, regulate and control its growth and development, and fulfill its high yield, good quality, high benefits, and standard production eventually. The models in Rape-CSODS were developed based on field experiments with 3 cultivars, 2 plant types, 6 sowing dates, and 4 sites from 2002 to 2008 in the middle and lower valley of Yangtze river in China and relative data from references of rapeseed research, employed ideas of R/WCSODS (Rice/Wheat Cultivation Simulation-Optimization-Decision Making System), then Rape-CSODS were developed through integrating rapeseed growth models, optimization models, and expert knowledge. In that the rapeseed growth models mainly included phenophase, leaf age, population dynamic, and organ forming etc., and the optimization models for rapeseed cultivation mainly included the optimum biomass, the optimum LAI, the optimum shoot and ramification number dynamics models, and the optimum decision making models for nitrogen and water etc. As a case, the system has been applied in Shandong Province in this study, and the results showed that the scheme get by this systems can increase green leaf number before over-winter, 1.1; 1000-weight, 0.2g; and rapeseed yield, 676.5 kg ha-1 at mature, comparing with rapeseed production in the same conditions. The Rape-CSODS with general adaptability, clear yield aim, better mechanism, and dynamic characteristic can get the optimum management sheet pre-planting, micro-adjustment sheet during growth for rapeseed under different climates, soil, cultivars, and yielding levels. Keywords: Rapeseed, Cultivation, Growth Models, Optimization Models, Decision Making System, Research and Application. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 441–456, 2011. © IFIP International Federation for Information Processing 2011

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1 Introduction Rapeseed is one of four main oil crops in the world, whose plant area is about 14.1million ha in general. In the same time, it is also main oil crops in China, and its plant area is 4.0-5.0 million ha. The crop simulation and the 3S (GPS, GIS, and RS) technology are the core and foundation of digital cultivation technique systems (Cao et al., 2005), and the crop simulation technology mainly included the crop growth models, the optimum models in cultivation, and the simulation-optimization-decision making. In that the optimum models in rapeseed cultivation were mainly the models of the optimum season, population dynamic, seed rate, fertilizer rate, and soil water etc., which set a basis and goals for the simulation-optimization-decision making in rapeseed, and had an important significant in promoting digital cultivation and realizing objective dynamic quantitative and optimum management. In the world, the research of rapeseed models can be divided into two stages obviously, i.e. the empirical models as main (70-80’s of 20 century), and the (partially) mechanism models as main (since 90’s of 20 century). In the later stage, the rapeseed growth and development and ecological system models such as EPR95 (Kiniry et al.,1983), DAR95 (Peterson et al.,1995), LINTUL-BRASNAP (Habekotté, 1997), CERES-rape (Gabrielle et al.,1998,1999), APSIM-Canola (Robertson et al.,1997), and CECOL (Husson et al.,1997) etc. were developed, and they can simulate rapeseed growth and development in real time. However, in China, the research of rapeseed simulation model were not more. Liao et al. (2002a, 2002b) studied characteristics of dry matter accumulation, distribution, and transfer of winter rapeseed, and developed the knowledge-based expert system for rapeseed production. Liu et al. (2003, 2004) set up simulation model of rapeseed phenophase etc. Zhu et al. (2004, 2005, 2007) studied dynamic knowledge model and decision support system for rapeseed cultivation. Cao et al. (2006, 2009) carried out the studies of simulation models of phenophase, leaf age, dry matter, leaf area, and ramification numbers. Tang et al. (2006, 2007a, 2007b) studied dynamic simulation on photosynthesis and dry matter accumulation, shoot dry matter partitioning, and yield formation of rapeseed, and developed a growth model-based decision support system for rapeseed management. However, few studies have been done on research and application of cultivation-simulationoptimization decision making system for rapeseed. The objectives of this paper were to establish Rapeseed Cultivation SimulationOptimization-Decision Making System (Rape-CSODS) through combining the rapeseed growth models, the optimization models for rapeseed cultivation, and expert knowledge for plant diseases and insect pest of rapeseed, apply it in rapeseed production in Liaocheng of Shandong province of China, and validate it in order to design rapeseed planting, regulate and control its growth and development, and to fulfill its high yield, good quality, high benefits, and standard production eventually. In that the models in Rape-CSODS were developed based on field experiments in Yangtz river middle and lower valley of China.

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2 Materials and Metheods 2.1 Materials “Zhongshuang 9” (V1, conventional) and “Zhongyouza 2” (V2, hybrid) (breed by Institute of Oil Crops Research, China Academy of Agricultural Sciences, CAAS), and “Ningyou16” (V3, conventional) and “Ningyou18” (V4, conventional) (breed by Institute of Economic Crops Research, Jiangsu Academy of Agricultural Sciences) ,and “Qinyou 2” (V5, hybrid) (breed by Center of Sci-Tech & Education Research of Shaaxi Province) were used in the experiments. 2.2 Methods 2.2.1 Referring to References Its aims were to apprehend and master the rapeseed growth and development rules and principles of growth regulation and control for rapeseed whole, and obtain initial parameters on varieties, soil, and climates etc. by referring to the references in papers and books on rapeseed physiology, ecology, and cultivation etc. 2.2.2 Development of Rape-CSODS The Rape-CSODS were established through combining the rapeseed growth models, the optimization models for rapeseed cultivation, and expert knowledge for plant diseases and insect pest of rapeseed using Visual Basic ver. 6.0 and VFP ver. 6.0. 2.2.3 Validation of Rape-CSODS The scheme get by this systems, Rape-CSODS, were compared with rapeseed production in the same weather, soil, cultivars. 2.2.4 Experiments In order to decide the parameters of the models and verify the models, the field experiments with different rapeseed cultivar types in multi-sites and multi-years were carried out. Experiment 1: Two rapeseed cultivars, V1 and V2, were grown in the field from 2002 to 2005 on Red loam soil near Wuhan (soil test in pre-planting results indicated the following: total nitrogen, 1.2 g kg-1; organic matter, 20.7 g kg-1; pH, 7.79; and volume weight, 1.50g/cm3, 30 5′N), Hubei Province. The six planting dates were 15 September, 22 September, 29 September, 6 October, 13 October, and 20 October, respectively. The experiment included 12 treatments, 3 replications, and 36 subplots arranged random with 8.00- by 2.50-m. Fertilizing and other field managements in subplots were the same. Fertilizer contained 180 kg N ha-1, 120 kg P2O5 ha-1, 180 kg K2O ha-1, and 15 kg boron ha-1. Experiment 2: The rapeseed cultivar, V3, was grown in the field from 2006 to 2007 on Yellow umber soil with higher fertility in pre-planting in soil in Nanjing (32 3′N), Jiangsu Province. 2 treatments (direct seeding, and transplant), 3 replications, and 6 subplots arranged random with 3.00- by 2.50-m were set to the experiments. Fertilizing and other field managements in plots were the same.

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°0

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Experiment 3: The rapeseed cultivars, V3 and V4, were grown in the field from 2007 to 2008 on Yellow umber soil with higher fertility in pre-planting in soil in Nanjing (32 3′N), Jiangsu Province. The experiment included 2 cultivars and 2 nitrogen levels (Fertilizer: 0.018kg N·m-2; 0.012kg P2O5·m-2; 0.018kg K2O·m-2; and 0.0015kg borax·m-2; CK: no fertilizer), 4 treatments, 3 replications, and 12 subplots arranged random with 40.0-cm row spacing, 17-20 cm plant spacing in 7.00- by 4.30m area. Fertilizing and other field managements in plots were the same. The phenophase, LAI, the total shoot numbers, dry matter, leaf number, leaf photosynthesis, plant characters, and meteorological and soil data etc. were observed during rapeseed growth or after harvest.

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3 Cultivtion-Simulation-Optimization Decision Making System for Rapeseed (Rape-CSODS) 3.1 Structure and Functions of Rape-CSODS The system was developed based on the principles on eco-physiology for rapeseed, balance of soil nutrition and moisture, agricultural system science and agricultural meteorology, the optimization of rapeseed cultivation, and so on, and included rapeseed growth and optimization models, databases, system platform, and function (Fig.1). In that there were the simulation models for growth and development in rapeseed (see Appendix below), the optimization models of rapeseed cultivation (see Appendix below), and generating models for weather data (see Appendix below), there were mainly the parameters for weather, soil, and cultivars, and expert knowledge for managements for nutrition and water in soil and controlling for diseases, insect pests, and weeds in rapeseed in databases, there were the interface for decision making, databases, 4 sub-systems with parameter adjusting, decision making in normal year, decision making in current year, and analyzing for cultivar suitability in the system platform, and there were mainly determining the model parameter, the optimization cases and for rapeseed cultivation in normal year, the revising scheme for rapeseed cultivation in current year, suggestion for suitable plant areas for rapeseed cultivars, printing sheet for decision making, and so on. The interface for decision making was developed using Visual Basic ver.6.0, which included main menu and first level sub-menu with linking every sub-systems (Fig.2 to Fig.5) with independent main interfaces and main menu, the background databases were designed using VFP ver. 6.0, and the output of the decision making results can link with Excel.1998-2007. 3.2 Running Environment of System 3.2.1 Hardware The system required personal computer with the model Pentium IV or above, disk driver, and laser or stylus printer. 3.2.2 Software The system required WIN98 or above operation system, Visual Basic ver. 6.0, VFP ver. 6.0 or above, and Excel.1997 or above.

Analysis of effects of climate changes

Interface for Rape-CSODS

Optimum season Optimum LAI Optimum shoot numbers Optimum soil water Optimum decision making for fertilizing Optimum yield

Weather generator

Deciding the optimum planting date The optimum seed rate or plant density The optimum LAI dynamic The optimum shoot number dynamic The optimum dry matter dynamic The optimum cases of fertilizing The optimum cases of water management Control objects and methods of pest for diseases, insects, and weeds Printing decision making Tab. Fast pre-planting

Input and adjustment of Parameters

Function layer

System layer

Model layer

Prediction and regulation in seedling period Prediction and regulation in middling period Prediction and regulation during later period

Parameter adjustment

Models of phenophase Leaf number Photosynthesis and biomass LAI Shoot numbers Organ formation Soil water Soil nutrition Yielding formation

Analysis of proper plant districts of new varieties

Fig. 1. Chart of the structure and functions of Rape-CSODS

Expert knowledge

Analysis of variety adaptability

and

Decision making in current year

and

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Analysis of effects of climate changes on rape production

Decision making in normal year

Database for parameters of varieties, climates, and soil etc.

Diseases models knowledge of rapeseed Pest insect models knowledge of rapeseed

Database layer

Principles of rapeseed physiology and ecology, soil water and nutrition balance

Principles of system sciences and meteorology of agriculture

Principles of optimization of rape cultivation

Principles of plant pathology and insect ecology

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Fig. 2. The system main interface

Fig. 4. Selection and input for cultivars

Fig. 3. Browse of meteorological data in local

Fig. 5. The optimum fertilizer balance-sheet in the normal year

4 Application of System 4.1 Basic Situations of Site Rape-CSODS was applied in Liaocheng of Shandong province in 2006-2007, and the demonstration area was at 7.73 ha located in Houlou Village, Jiazhai, Chiping County with planting pattern of direct seeding between rows of pre-crop corn. The optimization scheme in normal year were gained by Rape-CSODS using the meteorological data in normal year in Liaocheng (Table 1), the average soil nutrition data in the demonstration area (soil texture, sandy loam; soil organic matter, 9.5 g·kg1; total nitrogen, 0.83 g·kg-1; P2O5, 18.5 mg·kg-1; K2O, 102 mg·kg-1), and the cultivar, “Qinyou 2” through combining the scheme with the local expert knowledge on Rapeseed. The managements of the demonstration area were carried in accordance with the optimization scheme from Rape-CSODS, and the control rapeseed field was used in the conventional managements under the same conditions such as soil, weather, and cultivar.

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Table 1. Meteorological data in the normal year in Liaocheng

Months

Monthly mean

℃

temperature ( )

Monthly mean

Monthly mean

maximum

minimum

℃

℃

temperature ( ) temperature ( )

Monthly sun

Monthly rainfall

Monthly rainy

time (h)

(mm)

days (D)

1

-2.0

3.4

-6.6

162.0

4.3

2.5

2

0.9

6.1

-4.2

161.0

7.1

2.8

3

7.1

13.1

1.6

198.0

14.6

4.3

4

14.6

20.5

8.5

231.0

23.8

5.7

5

20.0

26.7

14.4

263.0

45.8

5.4

6

25.2

31.7

19.5

246.0

69.3

7.8

7

26.7

31.4

22.5

204.0

185.5

12.7

8

25.4

30.3

21.5

216.0

123.3

10.5

9

20.6

26.2

15.7

214.0

55.2

6.8

10

14.4

20.6

9.5

200.0

36.7

5.1

11

6.6

12.4

2.0

170.0

13.4

5.2

12

0.2

5.2

-4.2

157.0

6.1

2.4

4.2 Comparison of Results 4.2.1 The Seedling Situations in Rapeseed before Over-Winter The various indices of seedling status in rapeseed before over-winter in demonstration area were larger than that of the control, e.g. the green leaf numbers per plant, leaf area per plant, and dry matter per plant increased 1.1, 221 cm2, and 1.5g, respectively (Table 2). Table 2. Seedling status in rapeseed before over-winter in 2006

Items

Green leaf numbers per plant

Diameter of rootstalk (cm)

Leaf area per plant (cm2)

LAI

Fresh weight per plant (g)

Dry weight per plant (g)

Demonstration area(D)

10.6

1.4

1961

1.82

162.3

15.8

Control area(CK)

9.5

1.3

1740

1.73

151.4

14.3

±(D-CK)

1.1

0.1

221

0.09

10.9

1.5

The investigation date was on Dec. 20, 2006.

4.2.2 The Yield and Yield Components Under the same conditions, 1000-grain weight and yield in demonstration area was larger than that of the control, e.g. the 1000-grain weight increased 0.2g, and the yield raised 676.5kg ha-1, respectively (Table 3).

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Plant

(104/

height

ha)

(cm)

Demonstration area (D)

17.1

Control area (CK)

±(D-CK)

Items

Length of

available

Length

available ramification of main

Pod number

ramification numbers on anthotaxy on main

Pod

Seed

1000-

number number weight

Yields (kg/ ha

per plant per pod

(g)

76

336.2

18.1

3.4

2829.0

49.3

71

258.5

17.9

3.2

2152.5

6.2

5

77.7

0.2

0.2

676.5

(cm)

main stem

(cm)

anthotaxy

1.65

38.4

8.3

55.5

17.1

1.55

35.1

6.9

0

0.10

3.3

1.4

5 Discussion and Conclusions 5.1 Disscussion At present, the development direction of rapeseed production is the high yield, good quality and end-uses, therefore, it is a basic guarantee of health development of rapeseed production to realize regionalization of distribution, large regional precision planting, standardization of production, and informationalization of managements. Regionalization of distribution Analysis of cultivar adaptability can be made by Rape-CSODS, which will help to know possible proper plant regions of cultivars, and develop potential extensive values for it. Of course, in order to fulfill this purpose, it is need to integrate with GIS. Large scale precision planting In the large scale planting, it is need to decide corresponding plant sheets under different weather, soil, and cultivar conditions, and the purposes can be realize using Rape-CSODS quickly expediently easily. In addition, the decision making with precision and digital can be done by Rape-CSODS, which will also help to realize precision planting, and it is a precision planting technology with low-cost. Standardization of production The cultivation practices with standardization can be integrated in pre-sowing plant sheet, which is a benefit references for planter. As long as you done according to it, standardization of production can be fulfilled. Informationalization of managements Informationalization of crop managements is one of crucial contents for modern agriculture. The Rape-CSODS and its application in rapeseed production set a basis for informationalization management of rapeseed production. With wide use of internet, informationalization management can be more feasible in agriculture, therefore, it is need to be integrated with internet.

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5.2 Conclusions Rape-CSODS contained simulation models for growth and development in rapeseed, the optimization models of rapeseed cultivation, and expert knowledge databases for managements for nutrition and water in soil and controlling for diseases, insect pests, and weeds in rapeseed, in that the rapeseed growth models mainly included phenophase, leaf age, population dynamic, and organ forming etc., and the optimization models for rapeseed cultivation mainly included the optimum biomass, the optimum LAI, the optimum shoot and ramification number dynamics models, and the optimum decision making models for nitrogen and water etc. The Rape-CSODS was a system with general adaptability, clear yield aim, better mechanism, and dynamic characteristic. The system has been applied in Shandong Province in this study, and the results showed that the scheme get by Rape-CSODS can increase green leaf number before over-winter, 1.1; 1000-weight, 0.2g; and rapeseed yield, 676.5kg ha-1 at mature, comparing with the control in the same conditions. The Rape-CSODS was a result of applying crop simulation technology in rapeseed cultivation, managements, and informational management system for rapeseed cultivation, which overcome lag and non-dynamic disadvantages of results from 2-3 year field experiment data on cultivation conventionally, and had a huge market and wide application foreground.

Acknowledgement The authors would like to express their appreciation to funding of national projects in the tenth five-year plan (2001BA507A-09-04), 948 projects of Agricultural Ministry of China(2004- 30), the national star project (2008GA690171), Jiangsu Province projects(06-G-169), and Jiangsu Government Scholarship for Overseas Studies; to Professor Qi Cunkou, and Associated Professor Chen Xinjun of the Institute of Economic Crops Research, JAAS for providing test materials.

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Liu, T.M., Hu, L.Y., Zhao, Z.H., Cao, C.G., Cao, W.X., Yan, M.C.: A mechanistic of phasic and phenological development in rape. I. Description of the model. Chin. J. of Oil Crop. Sci. 26(1), 27–30 (2004) (in Chinese with English abstract) Petersen, C.T., Svendsen, H., Hansen, S., Jensen, H.E., Nieksen, N.E.: Parameter assessment for simulation of biomass production and nitrogen uptake in winter rape. Europe J. of Agron (4), 77–89 (1995) Robertson, M.J., Holland, J.F., Kirkegaard, J.A., Smith, C.J.: Simulating Growth And Development of Canola in Australia. In: Proceedings of the 10th International Rapeseed Congress (1999), http://www.regional.org.au/au/gcirc/2/143.htm Tang, L., Cao, W.X., Zhu, Y.: Development of growth model-based decision support system for rapeseed management. Transactions of the CSAE 22(11), 160–164 (2006) (in Chinese with English abstract) Tang, L., Zhu, Y., Ju, C.H., Cao, W.X.: Dynamic simulation on shoot dry matter partitioning and yield formation of rapeseed. Chin. J. of Applied Eco. 18(3), 526–530 (2007a) (in Chinese with English abstract) Tang, L., Zhu, Y., Sun, X.F., Cao, W.X.: Dynamic Simulation Model for Photosynthesis and Dry Matter Accumulation in Rapeseed. Acta Agaon Sin. 33(2), 189–195 (2007b) (in Chinese with English abstract) Wang, Y.R.: Population Physiology of Crop High-yielding, 1st edn. Sci. and Tech. literature Pub. House, Beijing (1991) (in Chinese) Zhu, Y., Shen, W.X., Cao, W.X., Tian, Y.C.: Dynamic knowledge model and decision support system for rapeseed cultivation management. Transactions of the CSAE 20(6), 141–144 (2004) (in Chinese with English abstract) Zhu, Y., Cao, W.X., Shen, W.X., Tian, Y.C.: A dynamic knowledge model for fertilization management in rapeseed. Chin. J. Eco. 24(2), 209–213 (2005) (in Chinese with English abstract) Zhu, Y., Cao, W.X., Tian, Y.C., Yao, X.: A dynamic knowledge model for designing suitable scheme of rapeseed sowing and transplanting. Chin. J. of Applied Eco. 18(2), 322–326 (2007) (in Chinese with English abstract)

Appendix Simulation Models for Growth and Development in Rapeseed Phenology The basic models of rapeseed phenology were developed in the thesis through employing ideal of “Rice Clock Models” (Gao et al., 1992; Cao et al., 2005, 2006, 2009, 2010). dPj/dt = 1/DSj = ekj·(Tebj) pj· (Teuj) qj · (Pej) Gj · f(ECi) Tebj = (Ti – Tbj) / (Toj- Tbj ), when TiToj, Ti =Toj Teuj = (Tuj –Ti) / (Tuj -Toj), when Ti >Tuj, Ti= Tuj Pej = (Pi – Pbj) / (Poj- Pbj ), when PiPoj, Pi =Poj

，

where dPj/dt is the development rate at the jth stages, DSj is the days at the jth stages, Tebj and Teuj are the effective factors for temperature, respectively, pj and qj are the genotypic coefficient of temperature effects, Pej is the effective factor of photoperiod, Gj is the genotypic coefficient of photoperiod effects, and f(ECi) is the effective

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function of agronomic practice factors for rapeseed, Ti is the daily mean temperature ( ) in the jth stage, Tbj Toj and Tuj are lower, optimum, and upper limit temperature ( ) demanded in the jth stage for rapeseed, respectively, and Pbj Poj are the critical and optimum day length (h) demanded in jth stage for rapeseed, respectively. Vernalization models can be described as following through employing ideals of “wheat clock models”:

、

℃ ℃

、

dV/dt = 1 / Ds2 = e k2 ·(Ve ) C If a cultivar was winter or semi-winter rapeseed, the expression of Ve was: V 4 , 4 V 5 9 1.0, 5 V 10 V 20 V , 10 V 20 10 0, V 4 or V 20 However, if it was spring rapeseed, the expression of Ve was:

V

V , 0 V 5 5 1.0, 5 V 20 30 V , 20 V 30 10 0, V 0 or V 30

where K2 and C are the parameters of vernalization, Ve is the factor of rapeseed vernalization effect, Vti is the daily mean temperature in vernalization phase. It will finish vernalization phase when Ve equal to some extent accumulation days; the vernalization days of the winter rapeseed were 30 to 40 days, the semi-winter rapeseed with 20 to 30 days, and the spring rapeseed with 15 to 20 days. Leaf Age The growth rate of rapeseed leaf were different in different varieties, development stages, temperature, and nutrition conditions etc., when nutrition condition was optimum, the models of rapeseed leaf age were (Gao et al., 1992; Cao et al., 2005, 2006, 2009, 2010): dLj/dt = f (Lj) = 1/DLj = DLoj ·(Tt/To)La/Lb 0, when T T T, when T DLoj = e LK

T T

where dLj/dt is the development rate of the jth leaf age, f (Lj) is the basic development function, DLj is the development days demanded from emergence to the jth leaf age, DLoj is the development days demanded from emergence to the jth leaf age under the optimum conditions, Tt and To are the daily mean temperature( of the tth day, and the optimum temperature for rapeseed leaf age development, respectively, and La Lb LK are the parameters of leaf age models, respectively.

、、

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Dry Matter It was results of accumulation and partitioning in various rapeseed organ of assimilate of photosynthesis, and the accumulation of dry matter in whole rapeseed growth season was a process with “S-curve” changes. Therefore, simulation models of rapeseed dry matter dynamic were (Gao et al., 1992; Cao et al., 2005, 2006, 2009, 2010): W(t)= W(t-1) +

△W(t)

β P R, before elongation β P R , after elongation to early anthesis W t 1 0.2 β P R , after early anthesis 1 0.2 0.4 where W(t) and W(t-1) are the dry matter (g) of population in the tth, and the (t-1)th day after emergence, respectively, W(t) is the net increment (g) of dry matter of population in the tth day after emergence, PCG is the canopy photosynthesis rate of rapeseed (g co2·m2·h-1), R is the respiration rate (g co2·m2·h-1), β is the coefficient transforming carbohydrate into dry matter in rapeseed, 0.2 and 0.4 are ratio of photosynthesis of green stem and pod in whole photosynthesis in the same periods.

△

LAI In terms of principles of dry matter partitioning for rapeseed, the simulation models of LAI before early anthesis were (Gao et al., 1992; Cao et al., 2005, 2006, 2009, 2010): LAI(t)=LAI(t-1)+ΔLAI(t) ΔLAI(t)= LAII(t)- LAID(t) LAII(t)=CP(t) ·ΔW(t) ·RLA(t) where LAI(t) and LAI(t-1) are LAI in the tth, and the (t-1)th day after emergence, respectively, ΔLAI(t) is the daily net increment of LAI in the tth day after emergence, LAII(t) and LAID(t) are the total daily increment and decrement in the tth day after emergence, respectively, CP(t), ΔW(t), and RLA(t) were partitioning coefficient of dry matter in leaf, daily accumulation rate of dry matter in plant (g m-2 d-1), and ratio of leaf area to dry matter (m2 g-1), respectively. It’s decreasing after early anthesis was a function of development index: L AIX LAI(t)=

﹒ ﹒

﹒

1 + d1 × D VI (t) t DVI(t)= ∑ DPDi/No i =1 where LAIX is the largest LAI of rapeseed in whole growth period, DVI(t) is the development index in tth day after anthesis with ranges between 0 and 1, No is development physiological days from early anthesis to mature, and d1 is a parameter of models. 2

Total Shoot and Ramification Numbers The increasing models of shoot and ramification numbers from elongation to early anthesis were (Gao et al., 1992; Cao et al., 2005, 2009, 2010):

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△

TILN(t) = TILN(t-1) + TILN(t) TILN(t) = KTLNI(t) TILN(t-1) DPD(t) PTILN(t) PTILN(t) = 1- TILN(t-1) / TILNM KTLNI(t) = KTLNO ·TLNF(t)

△

The decreasing models of shoot and ramification numbers from early anthesis to mature were: TILN(t) = TILN(t-1) - TILN(t) TILN(t) = KTLNI(t) ·TILN(t-1) · DPD(t) · PTILN(t) PTILN(t) = 1- TILN(t-1) / TILNM KTLNI(t) = KTLNO· TLNF(t)

△

△

where TLNF(t) is the effective factor of the total leaf numbers in the tth day after emergence, TILN(t) and TILN(t) are the total shoot and ramification numbers, and its daily increment (number m-2) in the tth day after emergence, respectively, TILN(t-1) is an initial value of the total shoot and ramification numbers in the (t-1)th day after emergence(number m-2), TILNM is the largest numbers of total shoot numbers (number m-2), PTILN(t) is the ratio of total shoot and ramification numbers in the tth day after emergence to TILNM, DPD(t) was the development physiological days in the tth day after emergence, and KTLNI(t) is increasing coefficient of shoot and ramification numbers in the tth day after emergence, which was decided by nitrogen, light, temperature, and soil moisture etc.

△

Optimization Models in Rapeseed Optimum Season There was an overyear in growth and development of winter rapeseed, so an important basis of deciding the optimum planting date was to get the happy seedling under diffrent varieties and cropping systems. The green leaf numbers per plant preoveryear can be as the happy seedling criteria based on studies of Institute of Oil Crops Research, CAAS. At first, the optimum planting date was calculated by leaf age models above, then, the other optimum phenology were calculated by phenophase models above (Gao et al., 1992; Cao et al., 2005, 2009, 2010). Optimum Leaf Age The models of optimum leaf age of rapeseed were expressed as below under the optimum nutrition conditions, and the leaf development affected only by temperature (Gao et al., 1992; Cao et al., 2005, 2009, 2010): dLoj/dt = f (Loj) f (Loj) = 1/ DLoj = DLoj ·(Tt/To)a/b where dLoj/dt is the development rate in the jth leaf age, f (Loj) is a basic development function of leaf age, DLj is the development days demanded from emergence to the jth leaf age, DLoj is the development days demanded from emergence to the jth leaf age under the optimum conditions, and Tt and To are daily mean temperature in the tth day, and the optimum temperature for development of leaf age ( ), respectively.

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Optimum LAI The optimum LAI in different phenophase (LAIO) of rapeseed can be expressed as follows (Gao et al., 1992; Cao et al., 2005, 2009, 2010):

（）

LAIO = - 1/Ej ln(B ·Ib/ I0) where I0 is the horizontal nature light intensity above the population in the jth phenology in the local, which equals to product of daily mean solar radiation by coefficient (C), B·Ib is the light intensity of lower part of population in the jth phenology, Ej is the extinction coefficient of population in the jth phenology, Ib is the compensating light intensity of lower part of population in the jth phenophase.

（）

Optimum Dry Matter The optimum dry matter at different phenology and yielding levels equals to dry matter in the optimum LAI pre-elongation, that in the optimum LAI, and green stem area from elongation to pre-anthesis, and that in the optimum LAI, green stem area, and PAI, and its basic models can be expressed as follows (Gao et al., 1992; Cao et al., 2005, 2009, 2010): Wo(t)= Wo (t-1) +

△W (t) o

β P R , before elongation R , after elongation to early anthesis W t 0.2 β P R , after early anthesis 1 0.2 0.4 where Wo(t) and Wo(t-1) are the optimum dry matter of rapeseed population in the tth day, and (t-1)th day after emergence (g), respectively, Wo(t) is the net increment of the optimum dry matter of rapeseed population in the tth day after emergence (g), PCGO is the optimum canopy photosynthesis rate (g co2·m2·h-1), 0.2 and 0.4 are ratio of photosynthesis of green stem and pod in whole photosynthesis in the same periods, and the other signs are the same above. β P 1

△

Optimum Shoot and Ramification Numbers The models of the optimum shoot and ramification numbers of rapeseed population (NFO) (104×ha-1) can be expressed as follows (Gao et al., 1992; Ling 2000; Cao et al., 2005, 2009, 2010): BSO = NMF / NSV NSV = LN / 4 + k + 1 NFO = NMF + NS·BSO N

0, N 9, 12, 20,

8 or N 9 N 11 N 13 N

16 10 12 16

where BSO is the optimum plant numbers (104×ha-1), NMF is the optimum main stem and primary shoot numbers (104×ha-1), relying on yielding levels, NSV is the valid shoot and ramification numbers per pant, and NF and NS are the optimum primary, and secondary ramification numbers per plant, respectively.

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Optimum Decision Making of Ecological and Balance Sheet for Fertilizing Based on the principles for balance sheet for fertilizing, the nutrition of fertilizer in rapeseed can be calculated as follows (Gao et al., 1992; Cao et al., 2005, 2009, 2010): FNQ = (FAQ - FSQ)/EF where FAQ is the nutrition rate demanded in whole rapeseed growth, FSQ is the nutrition rate supplied by soil, the effects of the ecological factors such as the soil nutrition contents, soil organic matter, pH, and soil temperature etc on FSQ can be considered, and EF is the use efficiency of fertilizer in the rapeseed growth season. Optimum Decision Making of Soil Moisture Based on the principles for soil moisture balance, the models of profit and loss of soil water in rapeseed growth season can be expressed as follows (Gao et al., 1992; Cao et al., 2005, 2009, 2010): DSW(t)= ISW(t) - OSW(t) OSW(t) = ETA(t) + RU(t) + C(t) ISW(t) = W0(t) + P(t) where DSW(t) is profit and loss of soil water in the tth day (mm), ISW(t) and OSW(t) are capture, and loss of soil water in the tth day (mm) in current or normal year, respectively, ETA(t) RU(t) and C(t) are actual evaporation-transpiration, runoff, and intercept in the tth day(mm), respectively, W0(t) is initial soil moisture in the tth day (mm), and P(t) is rainfall in the tth day(mm).

、

Generating Models for Weather Data According to Gao et al. (1992a), the daily day length, DLj, and daily radiation, QDj, can be calculated below.

×

×

SL = 23.5 sin (6.2832 (284 + j) / 365) D6 = sin ((45 - (LT - SL - 0.565) / 2) 3.1416 / 180) D7 = sin ((45 + (LT - SL + 0.565) / 2) 3.1416 / 180) D8 = cos (LT 3.1416 / 180) cos (SL 3.1416 / 180)

×

D10

2 tan

DL

D10/15

×

/

× ×

×

/

2

× ω× × × × × × × × × ×

× ω× × ×

C1 = 3.1416 / (1 + (3.191 cos ( j) + 0.695 sin( j)) / 100) C2 = sin (LT 3.1416 / 180) sin (SL 3.1416 / 180) D10 (3.1416 / 180) C3 = D8 sin (D10 3.1416 / 180) QAX = (1.94 1440) / C1 (C2 + C3) QDj = QAX (0.146 + 0.559 (ST[j]/10) / DLj)/23.8846 where SL is solar latitude, j is day number, LT is local latitude, (taking = 6.28 / 365), and ST[j] is sun time on jth day.

ω

ω is solar constant

Residue Dynamics of Phoxim in Pericarp, Sarcocarp and Kernel of Apple Yunxia Luan1,2, Hua Ping1,2, and Ligang Pan1,2,* 1

Beijing Research Center for Agrifood Testing and Farmland Monitoring, 100097 Beijing, China 2 Beijing Research Center for Information Technology in Agriculture, 100097 Beijing, China Tel.: +86 10 51503031; Fax: +86 10 51503406 [email protected], [email protected]

Abstract. The temporal and spatial variations of phoxim residue in apples were studied. The insecticide phoxim was applied by spraying according to the recommended dosage. The phoxim residues in different parts of apple were extracted with acetonitrile, purified by primary secondary amine (PSA) and C18 filler, and then determined by gel permeation chromatography-gas chromatograph mass spectrometer (GPC-GC/MS). It was disclosed that the half-life of phoxim in apples was 1.64 days. The contents of the phoxim residue in different parts of apple could be ranked as follows : pericarp > entire apple > sarcocarp> kernel, and the ratio of residue in pericarp and the entire apple ranged from 6.241 to 9.262 in groups with different specific surface area. There were significant differences in the contents of residues between the pericarp and entire apple, which provided the evidence in the theory instruction and design basis to the sampling method of pesticide residue based on pericarp. Keywords: Apple; Phoxim; Sampling method; Residue dynamics; GPC– GC/MS.

1 Introduction Agricultural product quality and safety has been given higher requirements in recent years. Currently, the issues caused by excessive pesticide residues in edible agricultural products attracted the social attention to the safety and monitoring of vegetable production processes. Pesticides are used to control insects, weeds, and disease throughout the world [1]. Although their use leads to increased crop yields and improvements to the food quality, pesticide residue levels in food are of increasing concern to the public. Due to the abuse and misuse of pesticide, pesticide residues severely influence people’s health. Organophosphorus (OP) pesticides are the most widely used agricultural pesticide [2]. More than 70% of that are hyper-toxic or high-toxic, and normally are forbidden to use in planting fruits and vegetables [3]. OP insecticides are routinely applied to fruit crops (e.g., apple, peach, and pear) that are subject to consumption as single servings [4, 5]. Consumption of individual *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 457–464, 2011. © IFIP International Federation for Information Processing 2011

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fruits containing high OP insecticide residues may result in high exposure over a short period of time. Residue testing for the presence of pesticides, however, is routinely performed on composites of 5 to 10 individual or unit samples rather than samples of individual fruit [6-8]. The use of data derived from composite samples may result in the underestimation of residues present in individual fruit because dilution may occur during compositing and, therefore, may not accurately represent exposure in single serving commodities [9, 10]. Variability factors have been established to indicate how much residue concentrations measured in individual samples vary from levels observed in composite samples [11-13]. Variability factors have been calculated by dividing the maximum pesticide concentration observed in an individual or unit sample by the mean composite level [13]. In recent years, studies to establish residue levels observed in individual samples relative to composite samples have been performed in a variety of crops (e.g., apples, carrots, oranges, potatoes, and tomatoes) [14-17]. Residue data in unit samples of individual commodities are required to establish variability factors for comparison with default values currently being used. Recently, variability also has been estimated using the 97.5th percentile concentration data for individual samples, rather than maximum levels, where sufficient data exist. Sampling and sample preparation are critical steps in analysis, and cannot be treated separately from sample treatment; rather the processes must be conducted together. However, limited data have been reported that directly compare the sampling procedures against one another. Even less information is published regarding the most appropriate part of the material to use in conjunction with a sampling procedure for pesticides detection, which is particularly important to the portable rapid detector of contamination in fruits and vegetables. The present study addressed how well pesticide residue levels measured in composite samples represent concentrations in different parts of apples, in order to improve sampling method of single serving foods. A within-laboratory trial was performed to ensure that insecticides were applied at known rates, times, and following label practices. By sampling from the selected apples, individual apples were not mixed with others during spray and distribution. The temporal and spatial variations of phoxim residue in apples could be established.

2 Materials and Methods 2.1 Chemicals and Reagents Phoxim standard was purchased from National Institute of Metrology (China). The dispersive solid phase extraction sorbents of primary secondary amine (PSA) and C18 were purchased from Agela Technologies Inc. High-purity solvents cyclohexane, acetone, and acetonitrile were all purchased from Fisher Chemicals (Pittsburg, PA, USA). Analytical reagents magnesium sulfate (MgSO4) and sodium chloride (NaCl) were purchased from Beijing Chemical Reagents Company (China). 2.2 Apparatus The gel permeation chromatography (GPC) equipped with gas chromatography mass spectrometry system GC/MS was used. The GPC consists of two LC-10ADvp

（

）

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pumps, a SIL-10ADvp auto-sampler, a Shodex CLNpak EV-200AC column (2mm i.d.×150 mm) and CTO-10ASvp column oven, a SPD-10Avp UV detector, two FCV12AH flow channel selection valves (RV.A, RV.B) and a SCL 10Avp system controller. GC/MS machine is a Shimadzu GC/MS-QP2010 instrumentation equipped with a PTV-2010 large-volume injection device. GC/MS data analysis was triggered by a contact closure start signal from the HPLC controller. Data acquisition was performed using a C-R8A plus data processor. All these parts are the products of Shimadzu (Kyoto, Japan), except the Shodex CLNpak EV-200AC column (Shoko Co., Tokyo, Japan). Acetone/ cyclohexane mixing solvent (3/7, v/v) was used as the mobile phase of GPC, and the flow rate was set at 0.1 ml/min. The mobile phase was degassed using DGU-14A degasser (Shimadzu), and the GPC column was kept at 40 in the column oven.

℃

2.3 Sample Preparation All samples were purchased at local markets in Beijing. Samples were extensively collected to achieve good sample homogeneity. Phoxim was applied using an air blast sprayer and application rates guaranteed to contain 50% phoxim emulsifiable with the dilution multiple of 1:500. Apples were cored and sliced into 5 segments [pericarp(P), sarcocarp1(S1,S2,S3,S4) and kernel] separately, using a corer/slicer retailed and peeling machine for domestic use. The first and alternate slices of each apple were taken, chopped manually using a knife, and placed in a plastic bag for storage at - 25 until extraction and analysis. The remaining segments of individual apples were retained in separate bags for preparation of composite samples. Composites were constructed from the apples prepared from each group by randomly selecting 8 apples for each treat, regardless of the style and weight of each apple. Composite samples were prepared by thoroughly mixing the chopped apple pieces from the bags containing the retained portions of the individual apples. Composite samples were frozen until extraction and analysis. To prepare each sample, 10 g of a previously homogenized food material was transferred into a suitable glass vessel. Then, 10ml acetonitrile was added to each sample using an adjustable-volume solvent dispenser. The glass vessels were capped before vortex mixing for 1 min at maximum speed. Once the initial sample mixing was completed, 1 g NaCl and 4 g anhydrous MgSO4 were added and mixed immediately on a Vortex mixer for 1 min. It was important to note that this step must be taken immediately after the initial mixing step to prevent the formation of MgSO4 conglomerates. To separate phases, samples were centrifuged for 10 min at 1570×g. Using an adjustable repeating pipette, 1.0 ml aliquot of upper acetonitrile layer was transferred into a 1.5 ml flip-top microcentrifuge vial containing 150 mg anhydrous MgSO4 and 50 mg PSA sorbent. The vial was tightly capped and shaken on a vortex mixer for 1 min before extraction. Then the mixed extraction solution was centrifuged for 5 min to separate solids from solution. The solution was filtered through a 0.45μm syringe filter for GPC-GC/MS analysis.

℃

2.4 Determination of Phoxim in Apple by GPC-GC/MS Analysis was performed by GPC-GC/MS. The analytical GC/MS equipped with a PTV-2010 large-volume injection device was carried out using deactivated silica

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tubing (5m×0.53mm i.d.) , a Rtx-5MS pre-column (5m×0.25mm) and a Rtx-5MS column (30m×0.25mm×0.25μm film thickness; coated with 5% phenyl and 95% methylpolysiloxane; Restek Corporation, Bellefonte, Palo Alto, USA). The temperature of the PTV injector was set at 120 for the initial 5 min of sampling time, and then increased to 250 at 100 /min. The oven temperature was maintained at 80 for 5 min, subsequently increased to 280 at a rate of 8 /min, and then held constant for 10 min. The quadrupole mass spectrometer was operated in the electron impact ion (EI) mode. Ion source temperature and interface temperature were set at 200 and 250 , respectively. The mass spectrometer was operated in an ionizing energy of 70 eV. Injection volumes were 1μl for all analyses. Helium (99.999%) was used as the carrier gas.

℃

℃

2.5

℃

℃ ℃

℃

℃

℃

Quality Assurance/Quality Control

Apples from market without phoxim pesticide were obtained, which were prepared the same as the rinsed and peeled apples, and used to prepare blank apple matrix for quality assurance testing. With each set of samples (8-10 samples per set) extracted and analyzed, two aliquots of blank apple matrix were prepared for separate extraction followed by extraction, cleanup, and analysis as for all other samples. Background levels of some analyses were periodically detected in the blank matrix samples and were used for background subtraction in the determination of recovery from spiked matrix only; residue concentrations in samples were not blank corrected. No traces of the phoxim were observed in any of the analyzed reagent blanks.

3 Results and Discussion 3.1 Degradation Dynamics of Phoxim in Apple The phoxim pesticide was found to be beyond the detection limit in all the samples analyzed in the present study. In general, phoxim was found at elevated levels in entire and different parts of apples prepared from treatment relative to those control without sprayed by phoxim. Concentrations of phoxim were detected from the day applied to 28 days after treatment (Fig. 1). The degradation rate in the three days after treatment was 24.36%, 48.73% and 54.18%, respectively. The rate of degradation changed slowly between the 5th day to the 10th day, while the concentration of phoxim suddenly dropped after 11 days. Phoxim residues were mainly found in pericarp and the outer laying sarcocarp. However, low levels of phoxim was also detected in the interior sarcocarp and kernel. Residue levels ranged from 0.0057 mg/kg to 0.1100 mg/kg in the entire apples, 0.0061 mg/kg to 0.4469 mg/kg in pericarp and reduced level in sarcocarp adjacent to pericarp (Tab. 1). Phoxim was not detected in any solvent blanks that were processed along with samples, indicating that background levels of this compound were not due to lab contamination. Successive decline was observed during storage period (Fig. 1). It should be noted that the relative increase level of phoxim in sarcocarp and kernel after applied 10 days earlier (Tab.1). In apples analyzed after 28 days treatment, no residues were found in

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sarcocarp and kernel any more. It was assumed that the reduction of phoxim during the storage might be caused by its relatively high persistence to hydrolysis, and that the residues detected in kernel might be related to extensive vascular system around kernel. Table 1. Ratio of residue in pericarp and the entire apple in apples with different SSA Days after treatment

Total apple

Residues(mg/kg) pericarp sarcocarp

kernel

0

0.1100

0.4469

0.0013

ND

1

0.0832

0.3644

0.0015

0.0009

2

0.0564

0.3572

0.0019

0.0011

3

0.0504

0.3250

0.0025

ND

5

0.0485

0.3071

0.0013

0.0008

7

0.0449

0.3057

0.0033

0.0009

9

0.0410

0.2810

0.0030

ND

11

0.0391

0.2567

0.0012

ND

13

0.0164

0.1173

0.0009

ND

15

0.0087

0.0433

0.0008

ND

20 28

0.0069 0.0057

0.0219 0.0061

0.0006 ND

ND ND

0.5000 ) g k / g m ( n o i t a r t n e c n o c

0.4500

Total Pericarp Sarcocarp

0.4000 0.3500 0.3000 0.2500 0.2000 0.1500 0.1000 0.0500 0.0000 0d 1d 2d

3d 5d 7d

9d 11d 13d 15d 20d 28d

Time

Fig. 1. Degradation of phoxime in different parts of apple

3.2 Analysis of Phoxim in Different Parts of Apple In order to investigate the phoxim level in different parts of apples, segments from different layer of pericarp (P), sarcocarp (S1, S2, S3, S4) and kernel of individual

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apples were separated by using the peeling machine to avoid errors caused by manual operation (Fig.2). The residues observed in samples collected from different part of apples were shown in Figure 3.

Fig. 2. Different sampling parts of apple P: pericarp of apples, S1: sarcocarp close to pericarp, S2: the second layer of sarcocarp, S3: the third of sarcocarp, S4: the sarcocarp near kernel, K: kernel or core of apples

The concentration of the total apple and pericarp was consistent with the result above with the crude sampling method. The maximum phoxim concentration was observed in pericarp. The phoxim residue order in different positions of apple was: pericarp >total apple> the sarcocarp close to pericarp(S1)> kernel > the sarcocarp near kernel (S4) (Fig. 3). Contrary to expectations, no phoxim residue was detected in S2 and S3, which was the main edible part of apples. Therefore, 85% of the pesticide residues existed on the surface of the apple, and the degradation rate of pesticide residues in the entire apple was consistent with pericarp. 0.45 0.4 ) g k0.35 / g m 0.3 ( n o0.25 i t a r 0.2 t n e0.15 c n o 0.1 c 0.05 0 T

P

S1

S2

S3

S4

K

Fig. 3. Residues of phoxime in apple 6 days after treatment

3.3 Relationship between Residues and Specific Surface Area Specific surface area (SSA) is a material property of solids which measures the total surface area per unit of mass, solid or bulk volume, or cross-sectional area. It is a

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derived scientific value that can be used to determine the type and properties of a material. It is defined either by surface area divided by mass (with units of m²/kg). The most representative sampling part of apple was the pericarp when phoxim applied by spray. Sample of pericarp instead of the whole homogenate would be efficient in pesticide residues analysis. Therefore, the ratio of residue in pericarp and the entire apple in apples was investigated in Fuji apples with different shapes and specific surface areas (Tab.2). Apples with higher weight had lower SSA, while the pesticide residue in pericarp was higher than the apples with lower SSA. The ratio of phoxim in pericarp and the entire apple ranged from 6.241 to 9.262 in groups with different SSA. Table 2. Ratio of residue in pericarp and the entire apple in apples with different SSA Weight Weight (g)

Surface Area cm2

（）

（cm / g） 2

Cp/Ct

169.605+4.666

170.750+6.583

1.007

6.241

189.425+4.748

180.093+2.512

0.950

7.963

209.032+4.015

190.377+2.926

0.911

9.262

SSA

3.4 Discussion The problem of pesticide residue has become a chief obstacle to the apply industry internationalization in the world. At present, samples tested for the pesticide residue stem mainly from the entire apple, because the results from the sample have been generalized. However, pesticide residue of apples mainly exists in the periearp, a large number of pulp mixed into analysis sample would dilute the pesticide residues in pericarp, which would reduce the accuracy of detection, especially in the rapid detection. Therefore, the careful research to pesticide residue of apple periearp provides the theory instruction and the design basis to the reasonable and efficient sampling method. Acknowledgments. This work was supported by the public foundation from Beijing Municipal Rural Affair Committee (20080901), National High Technology Research and Development Program 863 (2010AA10Z403, 2007AA10Z202) and Beijing Municipal Science and Technology Commission Program (Z09090501040901).

References 1. Wen, Y.X., Li, J.K., Zhang, X.M., Xu, J.: Studies on sensitivity and detection limit of phytoesterase on organophosphate pesticides. Food Science 27(09), 186–188 (2006); Mith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981) 2. Jensen, A.F., Petersen, A., Granby, K.: Cumulative risk assessment of the intake of organophosphorus and carbamate pesticides in the Danish diet. Food Addit. Contam. 20, 776–785 (2003)

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3. Mol, H.G.J., van Dam, R.C.J., Steijger, M.: Determination of polar organophosphorus pesticides in vegetables and fruits using liquid chromatography with tandem mass spectrometry: selection of extraction solvent. J. Chromatogr. A 1015, 119–127 (2003) 4. Zhou, H.Y., Gao, Z.X., Sun, S.M., Wang, H.Y.: Development and application of on-site fast detection techniques for food safety. Journal of Instrumental Analysis 27(7), 788–794 (2008) 5. Souza, M.S., de Potas, G.M., de D’Angelo, A.M.P.: Organophosphorous and organochlorine pesticides affect human placental phosphoinositides metabolism and PI-4 kinase activity. J. Biochem. Mol. Toxicol. 18, 30–36 (2004) 6. Giddings, J.M., Biever, R.C., Racke, K.D.: Fate of chlorpyrifos in outdoor pond microcosms and effects on growth and survival of bluegill sunfish. EnViron. Toxicol. Chem. 16, 2353–2362 (1997) 7. Andersson, A.: Comparison of pesticide residues in composite samples and in individual units: the Swedish approach to sampling. Food Addit. Contam. 17, 547–550 (2000) 8. Harris, C.A.: How the variability issue was uncovered: the history of the UK residue variability findings. Food Addit. Contam. 17, 491–495 (2000) 9. Pennycook, F.R., Diamand, E.M., Watterson, A., Howard, C.V.: Modeling of the dietary pesticide exposures of young children. Int. J. Occup. EnViron. Health 10, 304–309 (2004) 10. WHO (World Health Organization): Joint FAO/WHO consultation on food consumption and exposure assessment to chemicals in food, Geneva, Switzerland (February 10-14, 1997) 11. Suhre, F.B.: Pesticide residues and acute risk assessmentsthe US EPA approach. Food Addit. Contam. 17, 569–573 (2000) 12. Fernández-Cruz, M.L., Villarroya, M., Llanos, S., Alonso-Prados, J.L., Garcia-Baudin, J.M.: Field-incurred fenitirothion residues in kakis: comparison of individual fruits, composite samples, and peeled and cooked fruits. J. Agric. Food Chem. 52, 586–863 (2004) 13. Ambrus, A.: Within and between field variability of residue data and sampling implications. Food Addit. Contam. 17, 519–537 (2000) 14. Boulaid, M., Aguilera, A., Camacho, F., Soussi, M., Valverde, A.: Effect of household processing and unit-to-unit variability of pyrifenox, pyridaben, and tralomethrin residues in tomatoes. J. Agric. Food Chem. 53, 4054–4058 (2005) 15. Ambrus, A., Soboleva, E.: Contribution of sampling to the variability of pesticide residue data. J. AOAC Int. 87, 1368–1379 (2004) 16. Lentza-Rizos, C., Tsioumplekou, M.: Residues of aldicarb in oranges: a unit-to-unit variability study. Food Addit. Contam. 18, 886–897 (2001) 17. Lentza-Rizos, C., Balokas, A.: Residue levels of chlorpropham in individual tubers and composite samples of postharvest-treated potatoes. J. Agric. Food Chem. 49, 710–714 (2001)

Risk Analysis of Aedes triseriatus in China Jingyuan Liu1, Xiaoguang Ma1,*, Zhihong Li2, Xiaoying Wu1, and Nan Sun3 1

Institute of Health Quarantine,Chinese Academy of Inspection and Quarantine, Beijing, P. R. China 2 College of agriculture and biotechnology,China Agricultural University, Beijing, P. R. China 3 Shanghai Entry-Exit Inspection and Quarantine Bureau, P. R. China [email protected]

Abstract. Aedes triseriatus(Say), the eastern treehole mosquito (Diptera: Culicidae), is an important medical vector transmitting the LaCrosse encephalitis(LAC) and West Nile Virus, distributed in north America, which ever caused the LAC epidemic in east and central America. With the medical-vectors intercepted data of Sino-North America trade at port, it was confirmed that it is possible for A. triseriatus to invade China by cargoes, imported timbers, containers, etc. The life cycle of A. triseriatus is closely related to the temperature and humidity. Three models were used. CLIMEX model was used to analyze the possible colonized area of A. triseriatus in China, the epidemic risk of LAC encephalitis transmitted by A. triseriatus was analyzed by MaxEnt model, and ArcGIS9.2 was used to analyze the model results. The purpose of this research is to establish a superposition model on the risk analysis of medical-vectors. The result shows that the eastern treehole mosquito could colonize in China. The high risk areas of LAC encephalitis are in Yunnan, Sichuan, Guizhou, Chongqing, Hubei, Anhui, Jiangsu, Zhejiang, Jiangxi, Hunan, Guangxi, Guangdong, Fujian, Hainan, Taiwan; South of Shanxi, Henan, Shandong; Southeast of Liaoning provinces; The medium risk areas covers most of Shanxi, Shaanxi, Hebei, Tianjin, Jilin, Heilongjiang; South of Xizang, Gansu, Ningxia, Qinghai; North of Sichuan, Liaoning, Shandong, Henan, Middle of Neimeng and parts of southwest Xinjiang provinces. The low risk area includes north of Xizang, Ningxia; South of Qinghai; Middle of Gansu, Neimeng, North of Ningxia, parts of southwest Xinjiang provinces. Keywords: Medical vector; Risk Analysis; CLIMEX model; MaxEnt model; ArcGIS software; Model superposition.

1 Introduction The relevant laboratory study shows that conditions, A. triseriatus is a vector of several viruses such as yellow fever, eastern encephalitis, Venezuelan encephalitis and western encephalitis. It is also the vector of canine heartworm disease. A. triseriatus propagates in water, particularly accumulated water in treeholes and tires, and can also survive in artificial vessels where there is accumulated water, so the mosquito *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 465–472, 2011. © IFIP International Federation for Information Processing 2011

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can survive in any place where accumulated water is available [2]. With increase in trade between China and North America, A. triseriatus is likely to invade China through cargoes, imported timbers and containers. It is a medical vector which may poses a serious threat to China. Therefore, it is imperative to predict the potential distribution scope of the mosquito in China with a view to preventing its invasion. A. triseriatus will colonization in an area is closely related to climatic factors such as temperature, humidity and rainfall, so the software CLIMEX based on the phenologic and climatic conditions is selected to predict its potential distribution in China. The software MaxEnt based on the maximum entropy algorithm is used to predict the possible spread areas of LAC encephalitis transmitted by A. triseriatus. This paper provides s a risk analysis of A. triseriatus based on a superposition model.

2 Materials and Methods 2.1 Materials 2.1.1 Analysis of Limiting Factors for Distribution of Aedes triseriatus The life cycle of A. triseriatus includes four stages: egg, larva, pupa, and adult. Adult A. triseriatus mosquitoes spawn on water surface and has one or two generations each year, depending on environmental conditions, especially rainfalls [2]. In Michigan of the US, the eggs hatch into larvae in March to April. There are four larval instars, and fourth instar larvae will moult into pupae. In northern states of the US, adult mosquitoes start appearing from mid-July to September. The eggs will enter a state of diapause under appropriate conditions, and in the southern US, this insect overwinters in mature diapause larvae. During diapause, larvae can move freely to seek foods, but non-diapause larvae move slowly and eat less. In Toronto, Canada, first instar larvae appear in mid-March, with water temperature at 4.1-9.7 , second instar larvae start appearing in mid-April, and larvae start pupating in mid-May. Adults appear in June or July. The second-generation larvae appear in mid-July to mid-August, the second-generation population over-winters in larval stage before there life cycle ends [1]. Adult male mosquitoes appear earlier than female mosquitoes, and female mosquitoes start seeking foods 3 days after appearing. This mosquito prefers to bite mammals and is sensitive to light. The relevant study indicates that the photoperiod is a major factor that induces its overwintering diapause and a low-temperature environment can strengthen photoperiod induction [3].The state of diapauses will end under long-lasting sunlight [4]. The photoperiod for inducing diapuase varies in different latitudes [5]. A. triseriatus has strong resistance to low temperature. In 1972, M Jail studied the influence of different gradients of temperature on its growth under laboratory conditions. The study pointed out that the temperature for the larvae of this mosquito to start growing normally is 9 . In water below 4 or above 40 , all A. triseriatus will die within 3 days. Under 6 , 8 and 38 , the larvae can grow to second instars, but will not continue growing. When the water temperature reaches 40 , the larval mortality rate is still very high, with only 31% larvae survived. Obviously, high

℃

℃

℃ ℃

℃ ℃

℃

℃

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temperature limits its growth. In 2007, the study by Williams et al. in Canada stated that A. triseriatus might survive in southern Canada, and under natural conditions, it could spawn in water lower than 10 and first instar larvae were found in water of 0.7 . Obviously, temperature is a main factor controlling the southern and northern boundary in distribution of A. triseriatus. The eggs, larvae and pupae of A. triseriatus live in water and will not survive under overly dry environment. Strong rainfall will wash away A. triseriatus on water surface. Therefore, ambient humidity and rainfall limit its distribution to a certain extent.

℃

℃

2.1.2 Model The version number of the CLIMEX model is 2.0, and that of the MaxEnt model is 2.3, which can be downloaded directly via network (source website: http://www.cs.princeton.edu/~schapire/max-ent/). The GIS analysis software is ESRI ArcGIS9.2. 2.1.3 Data The CLIMEX model has 2414 global meteorological stations including 86 Chinese stations, and meteorological data can be purchased from the Earth System Scientific Data Sharing Network (http: //www.geodata.cn/Portal/) according to the research needs to add Chinese meteorological stations. The environmental data necessity for the MaxEnt model is world eco-environmental data provided by Desktop GARP platform, including data such as elevation, climate and type of vegetation etc. The maps ArcGIS analysis come from the National Fundamental Geographic Information System (http://nfgis.nsdi.gov. cn/). 2.2 Methods The data input in the CLIMEX model are biological data of eastern treehole mosquito including climatic factors such as temperature, humidity, illumination and stress. The model calculates the ecoclimatic index EI which reflects the probability of colonization of a species in a region. We can predict the probability of colonization of a species in a new habitat by adjusting the parameters so that the distribution of EI values maximally matches the present distribution of A. triseriatus. The inverse distance weight interpolation method of ArcGIS can carry out interpolation analysis of EI values to demonstrate more intuitionistically the potential distribution of A. triseriatus in China. The MaxEnt model can calculate the survival requirements of LAC virus using environmental data based on its known distribution and the eco-environmental data. Known distribution data was listed as the input data according the published references. 80% of the input data was used to build the requested model, and the left 20% was used to modify it. Run the model to achieve the potential spreading scope of LAC in a new habitat. Space analysis of ArcGIS software was used to analyze the possible colonization of A. triseriatus if it is spread into China and the potential distribution scope of LAC virus transmitted by it in China, obtained the risk analysis result for A. triseriatus in China.

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3 Result 3.1 Potential Distribution Result of A. triseriatus in China The preliminary parameters acquired according to biological data were adjusted match the existing distribution. The adjusted parameters are shown in the following table. Table 1. CLIMEX parameters of Aedes triseriatus Parameter description Lower temperature threshold DV0

Value 6

Lower optimum temperature DV1

15

Upper optimum temperature DV2

28

Upper temperature threshold DV3

38

Lower soil moisture threshold SM0

0.2

Lower optimal soil moisture SM1

0.7

Upper optimal soil moisture SM2

1.2

Upper soil moisture threshold SM3 Cold stress temperature threshold TTCS Cold stress accumulates rate THCS Dry stress threshold SMDS Dry stress rate HDS

2 5 -0.00001 0.1 -0.00001

According to the adjusted parameters, the potential distribution of A. triseriatus in China was predicted. The interpolation result of ArcGIS was compared with the actual distribution in North America to carry out classification. The result shows that this Aedes mosquito is widely distributed in China, covering almost regions in our country. According to its distribution in North America, the EI values are divided into four risk levels: high, moderate, low and no risk. Among them, the EI value is above 30 and marked in red in the areas such as Yunnan, Guizhou, Chongqing, Guangxi, Guangdong, Hong Kong, Hainan, Hunan, Hubei, Jiangxi, Fujian, Zhejiang, Anhui, Jiangsu, Taiwan, east Liaoning, southeast Shandong, south Henan, south Shaanxi, southeast Sichuan and south Tibet. This indicates that there is a high risk of colonization of A. triseriatus in the above areas. The EI value is during 9-30 and marked in orange in the areas such as Heilongjiang, Jilin, Hebei, Shanxi, Liaoning, northeast Shandong, north Henan, north Shanxi, south Gansu, south Ningxia, northwest Sichuan, south Tibet, northeast Inner Mongolia, southeast Qinghai and northwest Xinjiang. This shows that there is a possibility of colonization of A. triseriatus in the above areas. The EI value is during 1-9 and marked in yellow in the areas such as North Tibet, central Qinghai, central Gansu, central Inner Mongolia, north Ningxia and northeast Xinjiang. This indicates that A. triseriatus can survive in the above areas and there is a risk of its colonization.

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Fig. 1. Potential distribution of Aedes triseriatus in China

3.2 Potential Distribution of LaCrosse Virus in China MaxEnt uses suitable values with the scope of 0-100 as the indices to judge the probability of colonization, and according to the distribution of LAC virus in North America, the suitable values are divided into four risk levels: high, moderate, low and no risk. The result shows that there are widespread suitable distribution areas for this LAC virus in east China. Its highly suitable distribution areas include Heilongjiang, Jilin, Liaoning, Hebei, Beijing, Tianjin, Shandong, Henan, Jiangsu, Anhui, Zhejiang, Hubei, Jiangxi, Fujian, Guangdong, Hunan, south Shanxi, central and south Shaanxi, east Sichuan south Tibet. Its moderate suitable distribution areas include north Heilongjiang, Guangxi and east Guizhou. Its low suitable distribution areas mainly concentrate in Guizhou, Yunnan, south Gansu, north Shaanxi, north Shanxi, north Hebei, east Inner Mongolia and north Xinjiang.

Fig. 2. Potential distribution of LaCrosse encephalitis in China

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3.3 Result of Risk Analysis for Spread of Aedes triseriatus to China As the solely transmission of LAC virus and impossibility spreading without A. triseriatus , the vector insect and the host jointly contribute to the epidemic of LAC virus and decide the risk. Furthermore, during the risk superposition process, the vector’s weight result is greater than the virus’s weight result. The superposition matrix is shown in the following figure. Table 2. Risk Matrix Virus/Vector

High 4

Moderate 3

Low 2

No risk1

High 4

High 44

Moderate 34

Low 24

No risk 1

Moderate 3

High 43

Moderate 33

Low 23

No risk 1

Low 2

High 42

Moderate 32

Low 22

No risk 1

No risk 1

High 41

Moderate 31

Low 21

No risk 1

According to the matrix analysis, the risks are divided into 4 grades and 13 levels by the vector weight superposition theory. The ArcGIS spatial analysis module is used to realize and demonstrate the above superimposition theory and gain the risk of spread of Aedes triseriatus into China (as shown in Fig. 3).

Fig. 3. Potential risk of Aedes triseriatus in China

The result is displayed in four color series: red, yellow, green and colorless, which presents the four risk levels high, moderate, low and no risk of A. triseriatus invasion respectively. Each color series is divided into four sublevels from dark to light, reflecting a high-to-low risk tendency in each level. The red areas with risk level of 44, 43, 42 and 41 represent high-risk areas. The risk level is 44 in deep red areas are highest risk level in the high-risk areas, such as east Liaoning, south Shandong, south Henan, Jiangsu, Anhui, Hubei, Hunan, Zhejiang, Jiangxi, Fujian, Guangdong, Guangxi, Chongqing, east Sichuan, Hainan and west Taiwan. The areas with the risk

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levels of 43, 42 and 41 mainly concentrate in Yunnan, central Sichuan, Guizhou and southeast Tibet. The yellow areas 34, 33, 32 and 31 represent moderate-risk areas, and the risk level is 34 in the areas with a comparatively high risk, such as south Shaanxi, south Shanxi, north Hunan, north Shandong, southeast Hebei, Beijing, Tianjin, northwest Liaoning, Jilin, Heilongjiang and southeast Tibet. The areas with risk level of 33, 32 and 31 concentrate in northeast Inner Mongolia, north Hebei, north Shanxi, north Shaanxi, south Gansu, northwest Sichuan, south Tibet and northwest Xinjiang. The green areas with risk level of 24, 23, 22 and 21 represent low-risk areas, and the risk level is 24 in areas with a comparatively high risk level, such as east Inner Mongolia, Beijing, Shanxi, Hebei and Shandong. The areas with risk level of 23, 22 and 21 mainly concentrate in north Tibet, south Qinghai, central Gansu, north Ningxia, central Inner Mongolia, north Shanxi and northeast Xinjiang. In the impossible surviving area of vector A. triseriatus, the risk superimposition result is 14, 13, 12 and 11 which can be regarded as at no risk because the vector cannot survive and the pathogen cannot be transmitted.

4 Discussion In 2005, Craig R Williams et al. used the CLIMEX model which is based on the biological climate theory to predict the potential distribution of culex tritaeniorhynchus which transmitted Japanese encephalitis virus (JEV) in Australia and New Zealand. The predicted distribution result of the mosquito is highly consistent with its actual distribution. This shows that the CLIMEX model can play a good role in predicting the potential distribution of organism vectors. However, the study did not carry out further analysis of the JEV carried by culex tritaeniorhynchus. Kurt D. Reed et al. had predicted the latent distribution areas of Blastomyces dermatitidis using the MaxEnt model. The study assessed the risk of LAC virus mainly transmitted by Aedes triseriatus using the MaxEnt model. The above two kinds of risk simulation results underwent superposition analysis based on the geographic information system and formed a new model for biological risk analysis of vectors. Once A. triseriatus spreads to China, it will not only annoy people but also pose a threat to their health. In addition, this mosquito can be carried easily, and with increase in trade between China and North America, A. triseriatus is likely to be carried by trading tools such as ship, container. Therefore, it is necessary to formulate the relevant risk management measures according to the risk analysis result to protect people’s health.

References [1] Williams, D.D., Mackay, S.E., Verdonschot, R.C.M., et al.: Natural and manipulated populations of the treehole mosquito, Ochlerotatus triseriatus, at its northernmost range limit in southern Ontario, Canada. Journal of Vector Ecology 32(2), 328–335 (2007) [2] Penrod, J.R.: Aedes triseriatus and treeholes: trophic interactions and factors influencing larval growth. Michigan states University, Michigan (2000)

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[3] Clay, M.E., Venard, C.E.: Diapuase in Aedes triseriatus (Diptera: Culicidae) Larvae Terminated by Molting Hormones. Annals of the Entomological Society of America 64(4), 968–970 (1971) [4] Baker, F.C.: The effect of photoperiodism on resting, treehole, mosquito larvae. Can. Entomol. 67, 149–153 (1935) [5] Donald, A.S., George, B.C.: Egg diapause in Aedes Triseriatus (Diptera:Culicidae): geographic variation in photoperiodic response and factors influencing diapause termination. J. Med. Entomol. 20(6), 601–607 (1983) [6] Jalil, M.: Effect of temperature on larval growth of Aedes triseriatus. Journal of Economic Entomology 65(2), 625–626 (1972) [7] Song, H.M., Zhang, Q.F., Han, X.M.: CLIMEX: Professional biological software for predicting potential distribution of species. Entomological Knowledge 41(4), 379–386 (2004) [8] Sutherst, R.W., Maywald, G.F.: A computerised system for matching climates in ecology. Agriculture, Ecosystems & Environment 13(3-4), 281–299 (1985) [9] Sutherst, R.W.: Implications of global change and climate variability for vector-borne diseases: generic approaches to impact assessments. International Journal for Parasitology 28, 935–945 (1998) [10] Sutherst, R.W.: Prediction of species geographical ranges. Journal of Biogeography 30, 805–816 (2003) [11] Sutherst, R.W., Maywald, G.F., Russell, B.L.: Estimating vulnerability under global change modular-modelling of pests. Agriculture, Ecosystems & Environment 82, 303– 319 (2000) [12] Sutherst, R.W., Maywald, G.F., Bottomley, W., et al.: CLIMEX version2 User Guide. Hearne Scientific Software [13] Barker, C.M., Paulson, S.L., Cantrell, S., et al.: Habitat preferences and phenology of Ochlerotatus triseriatus and Aedes albopictus (Diptera: Culicidae) in southwestern Virginia. Journal of Medical Entomology 40(4), 403–410 (2003) [14] Yu, P., Wei, R., Wang, Z.L., Yang, Y.J.: Review and monitoring measures of west nile virus vectors. Chinese Journal of Vector Biology and Control 16(4), 324–326 (2005) [15] Wu, L., Xie, W.: West Nile Encephalitis. Chinese Journal for Clinicians 34(3), 15–17 (2006)

Risk Assessment of Reclaimed Water Utilization in Basin Based on GIS Yanxia Zheng1,2,3, Shaoyuan Feng3, Na Jiang3, and Qingyi Meng4 1

College of Resources and Environmental Sciences, China Agricultural University, Beijing 100083, P. R. China 2 Institute of Resources and Environmental Sciences, Shijiazhuang University, Hebei 050035, P. R. China 3 Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, P. R. China 4 Beijing Hydraulic Research Institute, Beijing 100044, P. R. China [email protected]

Abstract. The reclaimed utilization of urban wastewater is an indispensable way for solving the crisis of urban water resource. In order to meet the needs of reclaimed water pollution control the risk assessment model for reclaimed water utilization in basin are established. GIS was employed to derive all the data and evaluate model in order to get spatial variance and distribution of the risk levels on different scales within study area. The evaluation results can supply the pollution control with target areas. Based on the analysis of reclaimed water data of Beiyun river basin, the paper sets up an index system of risk assessment of reclaimed water. In addition, it applies the mathematics theory to making a comprehensive assessment on the utilizing risk of reclaimed water in the research object, which provides a scientific foundation for risk management of reclaimed water in theory and practice.

，

Keywords: Reclaimed water, Risk assessment, GIS, Beiyun river.

1 Introduction There is an increasing trend to require more efficient use of water resources, both in urban and rural environments. Wastewater reclamation has been the subject of a number of studies. Types of wastewaters used for recycling include treated and untreated sewage effluent[1-3], storm water runoff [2,4], domestic greywater[5], and industrial wastewater[2,6]. During the last decade, municipal wastewater reuse has emerged as an important means of supplementing water supplies in a large number of regions throughout the world. In many instances, reuse is also promoted as a means of limiting wastewater discharges to water environments. Wastewater reclamation is used in many ways. Effluents are reused for irrigation purposes in many countries around the world on all of the populated continents. Wastewaters can often contain significant concentrations of organic and inorganic nutrients for example nitrogen and phosphate. There is potential for these nutrients present in recycled water to be used as a fertilizer source when the water is recycled as an irrigation source for D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 473–478, 2011. © IFIP International Federation for Information Processing 2011

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agriculture[7,8]. There have been a number of risk factors identified for using reused waters for purposes such as agricultural irrigation. Some risk factors are short term and vary in severity depending on the potential for human, animal or environmental contact, while others have longer term impacts which increase with continued use of recycled water. So health and environmental risks associated with water reuse have been addressed. The application of risk assessment to protect human health has grown over the last 60 years, but it is only during the last years that reclaimed water risk assessment has become more researched. The aim of reclaimed water risk assessment is the estimation of risk of adverse effects to communities of species in locations that are potentially exposed to pollutants. Health and environmental risks associated with water reuse are now dealt with in a thorough manner by guidelines used in many countries. Guidelines pertaining to chemical contaminants are typically limited to bulk parameters such as chemical oxygen demand (COD), biochemical oxygen demand (BOD), pH and total suspended solids (TSS). In many situations these simple parameters provide suitable surrogate indications of the likely presence of chemical species of concern. However, for more highly treated reuse waters, they are of limited sensitivity. Furthermore, for some applications, an accurate assurance of specific chemical concentrations is important. Today, the assessment of whether a substance can pose a potential risk to the organisms present in the environment is based on hazard quotients. Many uncertainties come into the question when trying to calculate the hazard quotients. Theoretically, the exposure is calculated from measured values in the different environmental compartments. However, in most of the cases, this data is unavailable or insufficient due to the complexities inherent to environments. Due to this fact, models have become very useful tools in predicting concentrations of certain contaminants in specific compartments. In many risk assessments, the actual concentrations in the environment are not measurable so risk assessors must use models to predict what these concentrations will likely be. In China, there is very little analytical information available concerning the occurrence and fate of risk assessment during reuse applications. The paper is cooperation in Beijing in Beiyun river. For the recycled water for landscape water is evaluated. Establish the recycled water utilization assessment parameter. The purpose of this paper is to provide quantitative basis of risk assessments.

2 Study Area Description The study was carried out along the Beiyun River, located in the Haihe Basin, one of main basins present in China. With an area of about 4348 km2, the basin is situated in Beijing, with mean precipitation of 532.5mm (1980-2000) a year. The precipitation is concentrated in certain months of the year causing large flow fluctuations of the watercourses and therefore, leading to important differences in the dilution of the compounds present in waste water discharges. Fig. 1 shows the location of the Beiyun River Basin within Beijing. Beiyun river has completed some city sewage treatment plant, with design processing ability of 254 million ton per day, actual treating capacity is 214.4 million ton per day.

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river border

Fig. 1. Beiyun River inside Beijing

3 Results and Discussion 3.1 Distribution of Reclaimed Water Pollution intensity calculation of treatment facilities Pollution discharge =water discharge× Pollutant emission concentration. All data are from the pollution sources census data. According to the discharge of reclaimed water distribution layout of different districts is finished based on GIS. NH4+-N

CODcr

（：）

Fig. 2. CODcr and NH4+-N pollution load in different districts based on GIS unit t/a . Data shows CODcr discharge of treatment facilities is 3.44 thousand t/a NH4+-N discharge of treatment facilities is 0.53 thousand t/a. According to the administrative division, The area of biggest emissions is Chaoyang district, Percentage of 64.1%. The second discharge area is Haidin district, Percentage of 21.2%.

，

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Table 1. Comparative analysis between water quality of reclaimed water and surface water environment quality standards mg/l

( )

Ⅲ

Ⅳ

NO

Analyze item

1

Temperature( )

_

_

2

PH

6-9

3

DO≥

4

Ⅴ

primary (A)

Remarks

_

_

_

6-9

6-9

6-9

_

5

3

2

_

_

CODMn≤

6

10

15

_

_

5

CODCr≤

20

30

40

50

over

6

BOD5≤

4

6

10

10

over

7

NH4-N≤

1.0

1.5

2.0

5(8)

over

8

TP≤

0.2 (0.05)

0.3 (0.1)

0.4 (0.2)

0.5-1

over

9

TN≤

1.0

1.5

2.0

15

over

10

Cu≤

1.0

1.0

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（six valence）≤

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Risk Assessment of Reclaimed Water Utilization in Basin Based on GIS

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3.2 Assessment of Reclaimed Water In China, water quality standard in the reclaimed water use are different due to reclaimed ways. For example: reclaimed in agriculture perform “Standards for irrigation water puality” (GB 5084-2005); using water in fisheries perform “Water quality standard for fisheries” (GB 11607-89); using in scenic environment, and include water function range, perform “Environmental quality standards for surface water” (GB 3838-2002), and over water function range, perform reuse of“ urban recycling water—Water quality standard for scenic environment use” (GB/T 189212002); using in industry cooling perform “Code for desing of wastewater reclamation and reuse” (GB/T 50335-2002); using in city cooling perform reuse of recycling water for urban water quality standard for urban miscellaneous water consumption GB/T 18920-2002 . This paper process water quality assessment depend on primary(A) of discharge in sewage plant. Because Beiyun river includes in water function range, perform “Environmental quality standards for surface water” GB 3838-2002 . Comparison between water quality with the primary discharge standard and “Environmental quality standards for surface water” (GB 3838-2002): poor V species, main overpollutant are CODCr NH4+-N and TP.

》(

)

（

）

、

4 Conclusions Discharge of treatment facilities is bigger in Beiyun river. The area of biggest emissions is Tongzhou district, Percentage of 64.1%. The second discharge area is Changping district, Percentage of 21.2%. So it is necessary to improve sewage plant effluent standards to improve the quality of water environment of Beijing rivers. Overall, the irrigation water can be directly used in the secondary water level of treatment facilities. The use of recycled water for the irrigation of crops has benefits in using a resource that would otherwise be discarded and wasted. Using recycled water also reduces the pressures on the environment by reducing the use of environmental waters. Reclaimed water must pass through different degree of deep processing and the corresponding standards for direct using, and the depth of water storage with reclaimed water to avoid entering drinking water range. Ongoing research will improve the use of reclaimed water as well as increasing public confidence.

Acknowledgments The authors are grateful for support from the Restorative Project of Urban Water Environment (2008ZX07209-002 and 2008ZX07209-004) and Community Foundation of Ministry of Water Resources (200901083).

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References 1. Shereif, M.M., Easa, M.E.S., El-Samra, M.I., Mancy, K.H.: A demonstration of wastewater treatment forreuse applications in fish production and irrigation in Suez. Egypt. Water Sci. Technol. 32, 137–144 (1995) 2. Asano, T., Maeda, M., Takaki, M.: Wastewater reclamation and reuse in Japan: overview and implementation examples. Water Sci. Technol. 34, 219–226 (1996) 3. Haarhoff, J., Van der Merwe, B.: Twenty-five years of wastewater reclamation in Windhoek. Nambia. Water Sci. Technol. 33, 25–35 (1996) 4. Dillon, P.J., Pavelic, P., Gerges, N.Z., Armstrong, D., Emmett, A.J.: Artificial recharge of groundwater. In: Proceeding of the Second International Symposium Artificial Recharge of Ground Water, pp. 426–435 (1994) 5. Anderson, J.M.: Current water recycling initiatives in Australia: scenarios for the 21st century. Water Sci.Technol. 33, 37–43 (1996) 6. Guillaume, P., Xanthoulis, D.: Irrigation of vegetable crops as a means of recycling wastewater: applied to Hesbaye Frost. Water Sci. Technol. 33, 317–326 (1996) 7. Meli, S., Maurizio, M., Belligno, A., Bufo, S.A., Mazzatura, A., Scopa, A.: Influence of irrigation with lagooned urban wastewater on chemical and microbial soil parameters in a citrus orchard under Mediterranean condition. Sci. Total Environ. 285, 69–77 (2002) 8. Ramirez-Fuentes, E., Lucho-Constantino, C., Escamilla-Silva, E., Dendooven, L.: Characteristics, and carbon and nitrogen dynamics in soil irrigated with wastewater for different lengths of time. Bioresour. Technol. 85, 179–187 (2002)

Root Architecture Modeling and Visualization in Wheat Liang Tang, Feng Tan, Haiyan Jiang, Xiaojun Lei, Weixing Cao, and Yan Zhu* Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing, 210095, PR China [email protected]

Abstract. This paper aims to develop a root morphological model in wheat (Triticum aestivum L.), and the realistic visualization of root growth under different soil conditions in wheat. Based on the topology of the wheat root system, historical literature and experimental data, a root morphological model in wheat was developed preliminarily using thermal time as a driving factor, including sub-models of root emergence, root growth rate, and root axis curvature. Based on 3D visualization technology, a three-dimensional visualization model of the root axis in wheat was developed by using the platform of VC++.net and OpenGL library, including sub-models of geometry, texture mapping, and light rendering. Integrated the established root morphological model and the visualization model, the three-dimensional visualization of a root system in wheat under different soil conditions was realized. This study lays a foundation for further development of a visualization system for the whole wheat plant. Keywords: Wheat; Root; Architecture; Model; Simulation; Visualization.

1 Introduction Plant root systems play an important role in the development of new wheat germplasm with improved drought tolerance, nutrient and water uptake efficiencies and lodging resistance[1]. Root system development and structure are particularly difficult to study because of the difficulty of observing and quantifying the architecture of actual roots and the complexity and plasticity of roots as geometric objects [2]. Simulation models and computer visualization are useful tools to study root architecture. Several studies dedicated to the visualization of root system architecture have emerged during these last years, using different approaches. One approach is of fractal description [3-5], which uses statistical relationships between typical dimensions (e.g., length between branches, branching angles and diameters) observed locally throughout the root system. This approach, however, is essentially static because it does not rely on morphogenetic processes, thus rendering the models nearly unable to simulate architectures bearing developmental information. The approach *

Corresponding author.

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based on development [2, 6-8] has been used more often. It formalizes and combines in a mathematical framework the main developmental rules involved in the dynamics of root system architecture. L systems [9-10] is another approach which has been used for root architecture modeling and visualization [11-12]. This approach links with a physiological model to simulate both plant function and structure, but it has not often been applied to the plant root. Greenlab is a functional-structural model of plant growth for environmental applications, examples have been applied to root system modeling [13, 14] and performing well, however visualization technologies like random processing and collision detection were not studied in detail. Wheat (Triticum aestivum L.) is the most important grain crop in the world, more than half of world’s population relies on wheat as its primary food staple. Existing studies on wheat root growth modeling and visualization emphasize either developmental processes or visualization technology. Thus, the objectives of this paper are to develop a 3D root morphological model in wheat based on historical literature and experimental data and realistic root growth visualization in wheat under different conditions by using 3D geometrical modeling techniques.

2 Structure of Wheat Root System Two root types are distinguished in wheat: the seminal roots (also called primary roots), which develop at the scutellar and epiblast nodes of the embryonic hypocotyl of the germinating caryopsis; adventitious roots, which subsequently emerge from the coleoptilar nodes at the base of the apical culm and tillers [1]. These two categories of roots function in a complementary manner, and thus the root system must be considered as a whole. The number of primary roots in a cereal is usually five to seven, but may reach ten. Lateral roots (also called branch root) emerge on the primary roots and adventitious roots when roots grow until a certain period. First order and second order lateral roots also can grow on the primary roots and adventitious roots at a certain interval. Thus, the wheat root system is comprised of primary roots, adventitious roots and different orders of lateral roots in the soil (Fig. 1). A binary tree structure was used to analyze the structure of wheat root system. The root system can be divided into different types of root axis, each root axis has several nodes, from each node can emerge two sub-trees. The root tip has no children nodes. These nodes include root growth information including root hierarchy and branch information (Fig. 2).

3 Morphological Model Descriptions The root morphological model simulates the dynamic growth processes of the three dimensional structure of a root. The basic information needed to model the morphology of a single root is the number of roots, the times of root (primary roots, adventitious roots, and lateral roots) emergence, the growth rate of the root axis, and the growth direction of the root axis. Each root was thought of a collection of axial segments which change direction according to the morphological model.

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T1

T2

T3 1. Primary root; 2. Branching interval; 3. First order lateral root; 4. Secondary order lateral root; 5. Adventitious root Fig. 1. Root architecture in wheat

Initial node

ĉ Ċ

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3

2

5

4

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ċ

6 9

8 10 11

Č č Ď Child node

Fig. 2. Hierarchical structure of root nodes in wheat

3.1 Root Emergence Primary root emergence. The primary roots emerge when wheat seed germinates, two or three pairs of primary roots emerge from below simultaneously, and the roots emerging on each node display radial symmetry [15]. The number of primary roots (N) is significantly related to thermal time (TT, oCd), as following: N=2.64·ln(TT)-6.28 .

(1)

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Adventitious roots emergence. The growth of leaves, tillers and adventitious roots has a synchronous relationship: when leaf n+3 has emerged, a root emerges on leaf node n [16]. Integrating a previous study on the leaf phyllochron [17], the relationship between emergence of the adventitious root and thermal time was described by equation (2).

⎧102 + PHYLL× (b + 1) TTa(b+2) = ⎨ ⎩102 + PHYLL× (a + b + 3)

a=0 1 ≤ a ≤ SN

(2)

where a is the tiller position where the adventitious root grows, and b is the node position on the tiller where the root grows. TTa(b+2) is the thermal timewhen a root emerges on node b of tiller a (oCd), PHYLL is phyllochron, 102 is the thermal time from sowing to emergence (oCd), SN is the number of effective tillers. Lateral roots emergence. According to historical experimental data and the literature [18], the branching interval of main and first order lateral roots were 0.2 mm oCd-1, and secondary order lateral roots were 0.05mm oCd-1. Branching can happen in the intervals. Branching probabilities were calculated by the interval of the adjacent two lateral roots. The lateral root radius after branching was determined by a law which adopted by SimRoot [19], the section area before root branching was calculated by the section areas after root branching. d=α

×(d

2 2 1 +d2 )

(3)

where d is root diameter before branching (mm); d1 and d2 are the lateral root diameters after branching (mm); α is weighting coefficient, based on experimental data, taking an α value of 0.6 in this study. 3.2 Root Growth Rate Different types of roots have different growth rates, the growth rate of a primary root can be several centimeters per day, while the growth rate of secondary order lateral root can be several millimeters per day. Roots grow rapidly when thermal time is less than 120 oCd. Above 120 oCd, root growth rate decreases. The growth rate of a single root was calculated as following: When thermal time was less than 120 oCd, the growth rate (V(TT)) of primary, adventitious and first order lateral root were: V(TT)=-7×10-6 TT 2+0.0375 TT -17 .

(4)

The growth rate of secondary branch of root: V(TT)=-1×10-6 TT 2+0.0564 TT -47 .

(5)

o

When thermal time was greater than 120 Cd, the growth rate of primary and adventitious root was: V(TT)=2.1ln(TT)+5.2 . The growth rate of first order lateral root was:

(6)

Root Architecture Modeling and Visualization in Wheat

V(TT)=5.4ln(TT)-20 .

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(7)

The growth rate of secondary order lateral root was: V(TT)=5.2ln(TT)-21 .

(8)

3.3 Growth Direction of Root Growth direction of primary and adventitious root axis. The method developed by Clausnitzer and Hopmans [20] was adopted in this study for the calculation of the growth direction of the root axis. The growth direction (D) is computed from three directional components: the initial direction (D-1) of the root at the previous time step, geotropism (DG), mechanical constraint (DM) which may be considered as isotropic (random vector). The three vectors determine the final growth direction: D = D-1+ DG + DM .

(9)

The final position of root axis is rotated to an orthonormal coordinates, and can be denoted as following: X2=X1+L·cosα

(10a)

Y2=Y1+L·cosβ

(10b)

Z2=Z1+L·cosγ

(10c)

(X1, Y1, Z1) is initial growth position of the root axis in a growth cycle, (X2, Y2, Z2) is the final growth position of the root axis in a growth cycle, L is the lenth of the root axis, α, β, γ are decided by equation (9). Growth direction of lateral root axis. The 3D structure of a primary root is rotated to an orthonormal coordinates (Fig.3a, b). This amount of rotation is calculated to find the position of the root in 3D space. Then the growth direction of a new lateral root on the primary root was calculated. Finally, the orthonormal coordinates was rotated back to its original orthonormal coordinates. Fig. 3a shows that the final growth direction of a new lateral root P2P3 was determined by the vectors P0P1 and P1P2. The three points P0, P1 and P2 were rotated to XOY of an orthonormal coordinates, the corresponding points were P0’, P1’ and P2’, the growth direction of a new lateral root P2’P3’ was denoted by a vector v, which can be divided to each coordinate, as ( v1, v2, v3) (Fig.3b), thus,

v1 = v ⋅ sin γ ⋅ cos δ

(11a)

v2 = v ⋅ cos γ

(11b)

v3 = v ⋅ sin γ ⋅ sin δ

(11c)

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v = v1 + v2 + v3 2

2

2

(11d)

v =length of vector v, v / v =unit vector. The required rotation is calculated by using the definition of an orthonormal axial system x, y, z:

a1 = ( x1 − x0 ) / v ⎫ ⎪ b1 = ( y1 − y0 ) / v ⎬ → Unit vector x=(a1, b1, c1) c1 = (c1 − c0 ) / v ⎪⎭

(12)

Similarly, Y=(a2, b2, c2), Z=(a3, b3, c3), X=(a4, b4, c4), Now we have the unit vectors X, Y and Z with their components, which form a matrix R:

⎛ x ⎞ = ⎛ a4b4c4 ⎞ ⎟ ⎜ ⎟ ⎜ ⎜ y ⎟ = ⎜ a2b2c2 ⎟ = R ⎜ z ⎟=⎜ a b c ⎟ ⎝ ⎠ ⎝ 3 3 3⎠

(13)

Reverse rotation and translation will bring the structure back to its original position. Vector v, representing the new link P2P3, will thereby be multiplied by the transposed matrix R. y

P2

P3c

P3

J

P1

P2c

z

x

P1c

P0

a. The determination of lateral root growth direction

G

P0c

b. Structure of lateral root in orthonormal coordinates

Initial node Length of nodes interval

The node of lateral root emergence

c. Root axis curve formation Fig. 3. Growth direction of lateral root in wheat

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Root axis curve formation. The root axis was comprised of numerous root segments. The root axis grows 1 oCd as a step. When the root axis grows a certain length, from the root axis emerges a node which produces a lateral root according the rule explained in 3.1.1. The curve of the root axis was formed in this manner of root growth, as showed in Fig.3c. 3.4 Water Stress and Nitrogen Deficit Effect on Root Architecture Water stress. Wheat root architecture is influenced by soil water content. The relative growth rate was used simulate the root growth process under water stress [21].

⎡ w(t )( w(t ) − wi ) ⎤ dR = YR (t ) ⎢ ⎥ + α (t ) R (t ) wi < w(t ) < w0 Rdt ⎣ w0 ( w0 − wi ) ⎦

(14)

where R(t) is the root weight, w(t) is the actual soil water content, Wi is the minimum soil water content, w0 is the critical soil water content, YR(t) is a genetic factor of the root in different developmental stages, and α(t) is a function coefficient of the root. Nitrogen deficit. Low soil nitrogen content can stimulate root growth, and high soil nitrogen content would restrict root growth. According to the CERES model [22], Nitrogen deficit factor (FN) which is a value within 0-1 can be calculated as following: FN= 1-(TCNP -TANC)/(TCNP-TMNC) .

(15)

where TCNP, TANC and TMNC are critical, actual and minimum soil nitrogen content, respectively. 3.5 Random Processing To avoid rigid root curves while using the visualization model, the root morphological parameters were randomized to enhance the effect of “shake”. Based on the morphological feature parameters outputted from the wheat root morphological model, it was necessary to add a random variable between the high-low limit to realize realistic effect. The random variable was limited to a certain range, and the accuracy rating was evaluated by average value of the parameters. According to central limit theorem and large number theorem, this model constructs random variable as described in equation (15).

N r = ( N max − N a ) r1 / 2.58 − ( N a − N min ) r2 / 2.58

(16)

where Nr is random variable; Na is an average of a morphological feature parameter; Nmax is the maximum morphological feature parameter; Nmin is the minimum morphological feature parameter; r1 and r2 are the random variables subject to the normal distribution based on N[0,1].

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4 Visualization Model In order to make the visualization of roots looks more realistic, a visualization model was developed by using 3D dimensional graphic technology on the platform of VC++.net and OpenGL library, including a root axis geometric sub-model, and a texture and illumination sub-model. 4.1 Root Axis Geometric Sub-model A root system is comprised of a number of root axes, the root axes can be thought of as numerous connections of truncated cones. A truncated cone has four parameters: radius of the upper (R1) and lower (R2) circles, the height (L), and the direction of root axis [17] (Fig. 4a). The connection of the truncated cones should be followed by two conditions: the forepart of the truncated cone is larger than the posterior; the primary and adventitious root is thicker than lateral roots. When two truncated cones are connected, a gap appears, as showed in Fig.5a, which affects the visualization effects. A polygon grid algorithm [23] was adopted to eliminate these gaps (Fig. 5b).

R2

L

R Fig. 4. The basic model of root segment

a

b

Fig. 5. Comparison of the effect of truncated cones connection

4.2 Illumination and Texture Sub-model Roots are embedded in soil and grow in a dark environment, however, root structure can be looked clearly under illumination. The illumination model in the OpenGL library was adopted for simulating the illumination effect. The root surface was drawn by a texture model, which was constructed by using photos of a root at different stages with textures and integrated with the geometric model. The format of texture picture was 32*32 bmp.

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5 Visualization of Wheat Root --Example Applications Based on the root topological structure, linking root morphological model and visualization model, visualization of wheat root was realized by using 3D geometrical modeling techniques. Fig. 6 shows that the time-course simulation of root growth under different growth stages in wheat. The root system was from simple to complex, and the number of roots varied greatly. An experiment was conducted to compare the visualization output in this study with field observations. The experiment was conducted at the Nanjing Agricultural University Experimental Station in Nanjing (32°02′ N, 118°50′ E), China in 2009. The sowing date was 11 Nov. The cultivar Huaimai 25 was evaluated. Fig. 7 shows the comparison between observed and simulated single root architecture. The observed root structure scanned by the WinRhizo system (Regent Instrument Inc., Canada) indicated that the observed secondary order lateral roots were less than the simulation because of mechanical damaged from manual sampling. Fig. 8 illustrates a reasonably good comparison between the visualization of root architecture and the observed root architecture at the seedling stage.

1. 6 days

2. 15 days

3. 25 days

4. 35 days

Fig. 6. Visualization of root system growth under different growth stages in wheat

a

b

Fig. 7. Comparisons between an observed (a) and simulated (b) single root architecture

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a

b

Fig. 8. Comparisons between observed (a) and simulated (b) root system architecture at seedling stage

6 Discussion and Conclusions This paper described a root morphological model for simulating root growth and development under different conditions and a visualization model for the realistic visualization of root architecture in wheat. We aimed at the actual characteristics of root architecture in wheat. It is different from previous studies [6, 19], which aimed at a synthesis, generic root architectural model and its ability to simulate a diversity of root system architectures. Some of methods applied in those models were adopted in this study. Other studies on visualization of root architecture used different approaches, such as fractal description [2-4], development rule [5-7], L-systems [10-11], and Greenlab [12, 13]. These studies on wheat root either paid attention to the development or briefly described the visualization technology [5], or only observed and simulated the root morphology at the seedling stage [12]. Based on the topology of the root system in wheat, historical literature and experimental data, a root morphological model of wheat was established preliminarily using thermal time as a driving factor, including a root emergence sub-model, a root growth rate, and a root axis curve sub-model. Based on 3D visualization technology, a three-dimensional visualization model of a root axis in wheat was developed by using the platform of VC++.net and OpenGL library, including the sub-models of geometry, texture mapping, and light rendering. Integrating the established root morphological model and the visualization model, the three-dimensional visualization of root system growth in wheat under different conditions was realized. Roots growth and development is very complicated with a high degree of plasticity as influenced by many different factors, such as soil chemical and physical properties, climate elements, and crop genetics. Further study is needed to focus on soil, crop genotypes, and environmental conditions.

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Acknowledgments. We are thankful that the study was supported by the National Natural Science Foundation of China (30800136) and the National Basic Research Programme of China (2009CB118608). We also would like to thank Professor Albert Weiss at School of Natural Resources, University of Nebraska-Lincoln, for revising the English of this manuscript.

References 1. Manske, G.G.B., Vlek, P.L.G.: Root architecture-wheat as a model plant. In: Waisel, Y., Eshel, A., Kafkafi, U. (eds.) Plant Roots: The Hidden Half, pp. 249–259. Marcel Dekker, Inc., New York (1996) 2. Lynch, J.P., Nielsen, K.L.: Simulation of root system architecture. In: Waisel, Y., Eshel, A., Kafkafi, U. (eds.) Plant Roots: The Hidden Half, pp. 247–258. Marcel Dekker Inc., New York (1996) 3. Fitter, A.H., Stickland, T.R.: Fractal characterization of root system architecture. Funct. Ecol. 6, 632–635 (1992) 4. Ozier, L.H., Lecompte, F., Sillon, J.F.: Fractal analysis of the root architecture of Gliricidia sepium for the spatial prediction of root branching, size, and mass: model development and evaluation in agroforestry. Plant Soil 209, 167–180 (1999) 5. Van Noordwijk, M., Spek, L.Y., De Willigen, P.: Proximal root diameters as predictors of total root system size for fractal branching models. I. Theory Plant Soil 164, 107–118 (1994) 6. Diggle, A.J.: ROOTMAP – a model in three-dimensional coordinates of the growth and structure of fibrous root systems. Plant Soil, 105169–105178 (1998) 7. Pages, L., Vercambre, G., Drouet, J.L., Lecompte, F., Collet, C., Bot, J.L.: Root Typ: a generic model to depict and analyse the root system architecture. Plant Soil 258, 103–119 (2004) 8. Clausnitzer, V., Hopmans, J.W.: Simultaneous modeling of transient three-dimensional root growth and soil water flow. Plant Soil 164, 299–314 (1994) 9. Lindenmayer, A.: Mathematical models of cellular interaction in development, Parts I and II. J. Theor. Biol. 18, 280–315 (1968) 10. Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, New York (1990) 11. Shibusawa, S.: Hierarchical modeling of a branching growth root system based on L-system. Acta Hort. 319, 659–664 (1992) 12. Daniel, L., Andrea, S.: Root growth simulation using L-system. In: Handlovičová, A., Frolkovič, P., Mikula, K., Ševčovič, D. (eds.) ALGORITMY 2009 18th Conference on Scientific Computing Vysoké Tatry, pp. 313–320. Slovak University of Technology, Bratislava (2009) 13. Zhang, W.P., Guo, Y., Li, B.G.: Development and application of three-dimensional growth model of root system in wheat seedling. Scientia Agricultura Sinica 39(11), 2261–2269 (2006) ( in Chinese) 14. Zhang, W.P., Li, B.G.: Three-dimensional model simulating development and growth of cotton root system. Journal of System Simulation 18(1), 283–286 (2006) ( in Chinese) 15. Ma, Y.X.: Wheat Roots. China Agriculture Press, Beijing (1999) ( in Chinese) 16. Klepper, B.: Root and shoot development in winter wheat. Agron. J. 76, 117–122 (1984) 17. Yan, M.C., Cao, W.X., Luo, W.H., Wang, S.H.: A simulation model of above-ground organ formation in wheat. Acta Agronomica Sinica 27(2), 222–229 (2001) ( in Chinese)

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18. Zhang, B.G., De Reffye, P., Liu, L., Kang, M.Z., Li, B.G.: Analysis and modeling of the root system architecture of winter wheat seedlings. In: Hu, B.G., Marc, J. (eds.) Plant Growth Modeling and Applications: Proceedings-PMA03: 2003 International Symposium on Plant Growth Modeling, Simulation, Visualization and Their Applications, Beijing, China, October 13-16, pp. 321–328. Tsinghua University Press/Springer, Beijing/ Heidelberg (2003) 19. Lynch, J.P., Nielsen, K.: SimRoot: Modeling and visualization of root systems. Plant Soil 188, 139–151 (1997) 20. Chen, J.H., Lieth, D.: A two-dimentional, dynamic model for root growth distribution of potted plants. J. Amer. Soc. Hort. Sci. 118, 181–187 (1993) 21. Mech, R., Prusinkiewicz, P.: Visual models of plants interacting with their environment. In: Fujii, J. (ed.) Computer Graphics Proceedings. Annual Conference Series, pp. 397–410. ACM SIGGRAPH, New York (1996) 22. Godwin, D.C., Singh, U.: Nitrogen balance and crop response to nitrogen in upland and lowland cropping system. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (eds.) Understanding Options for Agricultural Production, pp. 55–77. Kluwer Academic Publishers, Dordrecht (1998) 23. Lluch, J., Vivo, R., Monserrat, C.: Modelling tree structures using a single polygonal mesh. Graph. Models 66, 89–101 (2004)

Sensors in Smart Phone Chunmei Pei1, Huiling Guo2, Xiuqing Yang1, Yangqiu Wang1, Xiaojing Zhang1, and Hairong Ye1 1

Beijing Vocational College of Electronic Science and Technology, Beijing, China 2 China University of Mining Technology ( Beijing ), Beijing, China [email protected]

Abstract. The technological innovation in electronics makes nowadays mobile phone more than a simple communication tool: it becomes a portable electronic device with integrated functions, such as listening to music, watching movies, taking photos, etc. To achieve these, many kinds of advanced sensor are used. In this paper, several applications of sensor in smart phone are introduced including Image Sensor, Fingerprint Identification Sensor, Photo-electric Sensor, Acceleration Sensor, etc. The trend of the mobile phone, 3I (Intellignet, Interaction and Internet), will have all kinds of sensor more and more popular in mobile phones to change our life. Keywords: Communication Terminal, Sensor, Smart Phone, 3I.

1 Introduction The development of technology, as seen in the portable devices, is making people's work and life more convenient. Digital cameras, MP3, MP4, Pocket PC and other small but powerful electronic devices are seen everywhere. Mobile phone, with annual sale of more than 200 million worldwide, is one of the most popular ones and almost indispensable to our lives. Starting from being a simple communication tool, mobile phone started to integrate more basic functions such as text messaging, multimedia, etc. To become more intelligent and smarter, a variety of sensors are adopted in mobile phones.

2 Definition and Classification of the Sensors 2.1 Definition of the Sensors According to National standard GB7665-87, sensors are defined as: "Devices which can feel the information to be measured, and convert the information into usable signal in accordance with some rules. Sensors are usually composed of sensitive components and conversion devices." Sensor is a detection device based on certain rules, which can measure the information and transform the signal to another form to meet the requirement of information transmission, processing, storing, displaying, recording and controlling. It is the primary step for automatic detection and control. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 491–495, 2011. © IFIP International Federation for Information Processing 2011

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2.2 Classification of Sensors Sensors can be classified in several ways, by conversion method (based on their basic physical or chemical effect), by usage, by type of output signal, by material, or by manufacturing techniques. By working theory, it can be classified into physical sensors and chemical sensors. By usage, it can be classified into positioning sensors, liquid level sensors, power sensors, speed sensors, thermal sensors, acceleration sensors, radiation sensors, vibration sensors, humidity sensors, magnetic sensors, gas sensors, vacuum sensors and biosensors. By output signal, it can be classified into analog sensors, digital sensors, and switch sensors. By the manufacturing process, it can be classified into integrated sensors, thin film sensors, thick film sensors, and ceramic sensors.

3 The Application of Some Typical Sensors in Smart Phones 3.1 Image Sensor As the photography feature has become standard in many mobile phones, image sensor has been widely used in mobile phones after its application in regular cameras. Taking pictures and shooting videos with mobile phones are part of a lot of people’s daily lives. The image processing technology has given the image sensors a broader room for application. 3.1.1 Business Card Recognition We use the phone to take a picture of the business card. The image is processed by software to identify the information in it. After some types of processing, the name, age, telephone, company address on the card are automatically inserted into the mobile telephone directory under one entry. This is a typical high-end feature of the business mobile phones. 3.1.2 Facial Recognition The user can take a picture to his/her face, and register it in the phone. Image processing software can identify the user's facial features, and establish the criteria to authenticate the user in the future. With this feature, the user does not need to remember complex password when accessing secured mobile phones. The only thing to do is simply to take a photo for himself/herself, and register it in the phone. In this way, the mobile device is more secured and the usage becomes simple. 3.2 Fingerprint Sensor As the demand increases for security of personal data on consumer products, and as the fingerprint sensors have achieved substantial improvement in the size, cost and

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accuracy, and so forth, fingerprint recognition technology has been applied on more and more diverse mobile devices. Many latest high-end business phones have such functions. Currently there are two solid-state fingerprint sensors in the market, contact sensors (touch sensors), and slip sensors (slide sensors). Contact sensors require the finger to push on the fingerprint sensor area. Slide sensors require your fingers to move at the sensor surface. The sensor will capture a specific set of data, then quickly analyze the data, and certifies the user. The improvement in sensing technology and algorithms, the cost-sensitive market, and the inherent requirements of portability, have made slip sensors more popular. Fingerprint sensor was firstly used in the mobile phone for identification, which is equivalent to the password identification. Only when the owner of the fingerprint is verified, can he/she be able to use the phone. With the development of semiconductor and software technologies, mobile phones are becoming mobile terminals that can access personal and corporate data at anytime, anywhere. It is critical to ensure the security of user access to prevent unauthorized access. For example, in e-commerce, the fingerprint recognition software can relieve the user from multiple complicated password operations. The process is simpler but the level of security is higher than traditional method. Fingerprint recognition can also be widely used in many other areas. For example, in the future the police can use the mobile phone to obtain the fingerprint, instead of ID card or driver's license, of a suspect in a district of high crime rate. The fingerprint information can be sent to the central database via Internet from the mobile phone. Then the ID of the suspect can be obtained. 3.3 Optical Sensor The photoelectric sensors in mobile phones are mainly used to smartly regulate the mobile phone by detecting the brightness of the environment, to save energy and make the mobile user-friendly. When the environment is too dark, it can automatically reduce the backlight brightness protect the eyes. When the environment is too bright under the sun, it can automatically increase the screen brightness, so the display can be clearer. When the mobile phone is placed near the ear during a phone call, it can automatically turn off the screen and backlight, to extend the battery life. The touch screen is disabled at the same time, to prevent unintentional hang up of the call by miss touching on the screen. By using this type sensor, the mobile phone can even be designed to control the ring volume by detecting the light intensity of the environment. When the mobile phone is put in the pockets or purse, the ring tone is loud. When the mobile phone is taken out of the pocket, as the brightness of the environment changes, it can turn down the volume. This feature is very useful. It not only can avoid missing phone call because of weak ring tone, but also can avoid disturbing others. The battery life is also saved. 3.4 Accelerometer Sensor There are two types of acceleration, static and dynamic. When static accelerometer is tilted to an angle, gravity will produce a vector in the sensing field. By measuring this

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vector, the tilt angle of the mobile phone can be calculated. So moving the mobile phone back and forth, up and down, can control some functions. The so-called dynamic acceleration can detect the moving speed, collision and so on. Accelerometer can be used to detect the angle. When we move the phone up and down, the acceleration sensor can send the corresponding information to CPU. And the software can control the menu selection, flipping, image switching and other operations. When the phone is rotated for 90 degrees, it can automatically switch between vertical display and horizontal display. In racing, skiing and other games, accelerometer can even replace the arrow keys, to turn, accelerate, and break the vehicle by tilting the phone at different angles, thus making the game more entertaining. Acceleration sensor can also be used as odograph, to detect and record the number of steps when walking or running, to calculate the distance. Combining with personal characteristics, such as height, weight and other information, the software can calculate the energy consumption, which can reflect the exercising result. In this way phone can also be used as fitness equipment. Mobile phones with acceleration sensor can also be used as emergency alarm. When the user experiences a severe crash, or falls, the acceleration sensor can immediately send the related information to the CPU. If the user does not move within a period of time after the accident (such as 1 minute), the software then determines that the user is injured, and will immediately alarm. If the phone has GPS module, it can also automatically start the GPS, and send the location information to the emergency department. So the victim can get first aid treatment. This will greatly benefit the growing population of senior people. Acceleration sensor, combination with magnetic sensor, can provide more powerful functions. "Perspective Space" technology is achieved by combining these two types of sensors. They detect the horizontal displacement, as well as vertical displacement, and the directions. When the user is carrying such mobile phone traversing in a building, a lot of information of lateral displacement, vertical displacement and the direction is transmitted to the CPU. With image rendering technology, a perspective of the building can be shown to us. This technology can also be the supplement to GPS navigation. In a special location, such as tunnels and tall buildings, where GPS does not work, acceleration sensor and magnetic sensor can work together to collect multi-directional displacement information and continue the navigation in the absence of GPS signals.

4 Conclusion With the rapid improvement of the technology, mobile phone is far more than just a simple communication tool. It has become a complex mobile data terminal with a variety of functions. The mobile phone will definitely become 3I (Intelligent, Interaction, Internet). To be intelligent and interaction, the mobile phone should provide a lot of practical functions, and automatically change settings and response to different environment, to achieve the user’s requirement in the simplest way. This requirement gives the sensor a great opportunity to show its functions. It is believed that a variety of sensors will spread in a large area in the mobile phone and eventually change our life.

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References 1. Yan, H.: Studying and Using the Techinque of Sensors in Production. J. Metrology & Measurement Technique, Beijing (2006) 2. Ran, G.: Application of Sensors. M. Xidian University Press, Xi’an (2002)

Simulation Analyze the Dice and Shape of the Dicer Based on ADAMS Yingsa Huang, Jianping Hu, Deyong Yang, Xiuping Shao, and Fa Liu Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education&Jiangsu Province, Jiangsu University, Zhenjiang 212013, China [email protected]

Abstract. The Fruits and Vegetables diversacut dicer is composed of impeller roller, circular knivess and crosscut knives. The mismatch of the rotationl speed ratio and the unsuitable central position arrangement between the impeller and the crosscut will directly affect the quality of the diced section which is apt to appear marked arc, incline and so forth. Therefore, In this paper, using the virtual prototype technology to build the 3D model of fruits and vegetables diversacut dicer, and import it into ADAMS (dynamic analysis software of mechanical system) to simulation analyze the different shape of diced section when changing the rotationl speed ratio from 0.11 to 0.22 and the central horizontal spacing between the impeller and the crosscut vary from 260mm to 310mm. The results indicated that the mismatch between the impeller speed and the crosscut speed is the main factor which causes the incline surface during dicing process. If the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, and the rotationl speed ratio set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21, the optimal diced section can be acquired so as to provide a basis for improving the diced quality. Keywords: Dice, Diced section, Shape, ADAMS, Simulation.

1 Introduction In recent years, to satisfy the requirement for large number of fresh-cut fruits and vegetables in the food processing industry in our country, a new Fruits and Vegetables diversacut dicer was designed absorbed the traditional handiwork technolgies and the advanced technology from the foreign countries. This dicer can solve the problem of unable for automatical feed, automatic cut and smooth discharge which existed in the traditional dicer very well. Additionally, it enhanced work efficiency, reduced working strength and satisfied the scale-production request in fruit and vegetable processing industry[1,2]. But there still existing many shortage, for example the mismatch of the rotationl speed ratio and the unsuitable central position arrangement between the impeller and the crosscut directly affect the quality of the diced section which is apt to appear marked arc, incline and so forth. This paper applied virtual prototyping technology, carried out kinematic simulation and analysis on the dicer to D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 496–504, 2011. © IFIP International Federation for Information Processing 2011

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Analyze the main factors which influenced the quality of the diced section, so as to provide evidence for improving the quality of diced section.

2 Structures and Work Principles of the Diversacut Dicer The diversacut dicer is composed of impeller roller, circular knives and crosscut knives,which is shown in Figure 1. The dicer adopted diversacut technology, including feeding by rotary propeller, slicing by centrifugal cutting method, cutting strips by circular knives and finally cut by crosscut knives. The main parameters of the dicer was set that the inner diameter of impeller roller is R1=200mm and 6 paddles in the impeller the radius of the circular knives is R2=45mm and the radius of the track of the crosscut knifepoint is R3=110mm. The amount of the circular and the crosscut knives can be adjusted according to the cutting specifications.

1. Impeller roller 2. Paddle 3. Circular knives 4. Crosscut knives Fig. 1. The 3D model of the dicer machinery

3 Virtual Prototype Modeling According to the designed parameters of the diversacut dicer, the 3D solid model of the machine part was established in UG4.0 and the assembly was completed, thereby established the virtual prototyping parameterized model[3,4]. Then imported the model into ADAMS using of the Model Data exchange interface which provided by UG4.0 and the dynamic analysis software of mechanical system ADAMS. After the successfully transformation of the model, the constraints was added in ADAMS including Fixed pair joint between the slicer and the impeller roller, Fixed pair joint between the slicer and the crosscut knives holder, Fixed pair joint between the crosscut knives and the crosscut knives spindle, Revolute pair joint between the crosscut knives holder and the crosscut knives spindle, Translation pair joint between the slicer and the ground. Then added motions on the model including a rotational motion and a Translational motion[3-5]. Then moved and rotated the model in order that the bottom of the slicer is at the origin and the slicer is along the Y-axis.

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4 Simulation Analyzing the Shape of Diced Section 4.1 Simulating Condition Setting The rotational velocity of the impeller roller was set at 120r/min while the rotationl speed ratio between the impeller roller and the crosscut knives which is indicated by was set from 0.11 to 0.22 and the specification of the diced product was 20mm×20mm×20mm. In order that the deformation of the fruits and vegetables material during the cutting process is lesser and the simulating result conforms with the actual result, the structural arrangement of the machine should be compact, what is to say is that the triangular region between the impeller roller, the circular knives and crosscut knives is small, as shown in figure 2. The arrangement of the impeller roller and the crosscut knives is shown in Table 1.

Fig. 2. The structural arrangement of the machine Table 1. The arrangement of the impeller roller and the crosscut knives horizontal spacing Δx /(mm)

vertically spacing Δy /(mm)

260.00

188.75

265.00

183.19

270.00

174.03

275.00

167.67

280.00

162.26

285.00

161.36

290.00

161.98

295.00

159.86

300.00

159.86

305.00

159.86

310.00

154.68

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4.2 Analysis of the Simulation Results The cutting experiment indicated that the irregularity of the diced section was caused during the process of dicing by the crosscut knives. Especially, the rotationl speed ratio and the central position arrangement between the impeller roller and the crosscut knives greatly affect the quality of the diced section. In the simulation analysis, the reverse method that the crosscut knives moves upward along the slice blade while rotating was adopted. The coordinate curve of the crosscut knifepoint can be got, which is the shape curve of the diced section, then provided the basis for optimizing the arrangement and determine the transmission of the dicer[4-7].

Fig. 3. The shape curve of the diced section when Δx =260mm

Fig. 4. The shape curve of the diced section when Δx =265mm

Fig. 5. The shape curve of the diced section when Δx =270mm

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Fig. 6. The shape curve of the diced section when Δx =275mm

When the arrangement of the impeller roller and the crosscut knives was set that the horizontal spacing Δx =260mm 310mm, the rotationl speed ratio between the

～

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impeller roller and the crosscut knives was set that n1 n3 = 0.11 0.22 , Figure 3 to Figure 13 respectively shown the simulation result of the shape curve of the diced section. As can be seen from the Figure 3,4,5,6, when the horizontal spacing between the impeller roller and the crosscut knives is 260mm 275mm, choosing the rotationl speed ratio from 0.11 to 0.22, the diced section appears incline surface,and the larger of the ratio,the more obviously of the incline.

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Fig. 7. The shape curve of the diced section when Δx =280mm

Fig. 8. The shape curve of the diced section when Δx =285mm

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Fig. 9. The shape curve of the diced section when Δx =290mm

Fig. 10. The shape curve of the diced section when Δx =295mm

Fig. 11. The shape curve of the diced section when Δx =300mm

As can be seen from the Figure 7 to Figure 11, when the horizontal spacing between the impeller roller and the crosscut knives is 280mm 300mm, choosing the rotationl speed ratio from 0.11 to 0.22, the ideal diced section can be acquired, and it appears marked arc surface. As can be seen from the Figure 12,13, when the horizontal spacing between the impeller roller and the crosscut knives is 305mm 310mm, choosing the rotationl speed ratio from 0.11 to 0.22, the diced section appears incline surface,and the larger of the ratio,the less obviously of the incline.

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Fig. 12. The shape curve of the diced section when Δx =305mm

Fig. 13. The shape curve of the diced section when Δx =310mm

4.3 Irregularity Degree Analyzation of the Diced Section In this paper, the quality of the diced section was measured by the Irregularity degree that the vertical distance between the section peak and the bottom ‘h’, as shown in Figure 14. Through analyzing the data of the simulation results,the Irregularity degree of the diced section under different rotationl speed ratio and central position arrangement between the impeller and the crosscut was got, which is shown in Figure 15.

Fig. 14. The Irregularity degree of the diced section

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8

260 265 270 275 280 285 290 295 300 305 310

7 6 5 4 3 2

0.22

0.21

0.2

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0.18

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0.16

0.15

0.14

0.13

0

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1 0.11

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rotationl speed ratio between the impeller and the crosscut

Fig. 15. the Irregularity degree of the diced section under different rotationl speed ratio and central position arrangement between the impeller and the crosscut

As can be seen from the Figure 15, if the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, when the rotationl speed ratio set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21, the irregularity degree is the least, and the quality of the diced section is the best.

5 Conclusion 1. Through kinematics simulation of the cutting mechanism, come to a conclusion that when the horizontal spacing between the impeller roller and the crosscut knives is 260mm 275mm and 305mm 310mm, the rotationl speed ratio adjust from 0.11 to 0.22, the ideal diced section can’t be acquired. What’s more, the diced section appears obviously incline surface. 2. When the horizontal spacing between the impeller roller and the crosscut knives is 280mm 300mm, the rotationl speed ratio adjust from 0.11 to 0.22, the ideal diced section can be acquired, the diced section appears marked arc surface. The simulating result provided a basis for the central position arrangement between the impeller roller and the crosscut knives.

～

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3. Through the Irregularity degree analyzation of the diced section, the ideal parameter combination that if the central horizontal spacing between the impeller and the crosscut set at 280mm, 285mm, 290mm, 295mm, 300mm, the rotationl speed ratio should be set at 0.11, 0.13, 0.16, 0.18(0.19), 0.21 was acquired. In this parameters, the quality of the diced section is best, as provided a basis for optimized the working parameter of the dicer next.

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Acknowledgements This work was financially supported by Tackling key Problems of Science and Technology of Jiangsu Province (BE2008385).

References 1. Bi, W.: Studies on Slicing Techniques and Material Characteristic of Lotus Root. D. Jiangsu University (2006) 2. Wei, E.: The Simulation and Optimization on the New Slicing Machine of Lotus Root Based on Virtual Prototype Technology. Jiangsu University (2008) 3. Kong, Z.: Design of Cotton Transplanter Based on Virtual Prototype. Shandong University of Technology (2006) 4. Li, W., Li, J., Zhang, J., et al.: Optimization Design and Simulations of the apple-pickingrobot arm. Journal of Beijing University of Technology 35(6), 721–726 (2009) 5. Wang, C., Tan, L.: Study on a Virtual Prototype Based Hay Highly Compressing Process. Transactions of the Chinese Society for Agricutural Machinery 36(3), 99–101 (2005) 6. Wang, W., Dou, W., Wang, C., et al.: Parameter Analysis of the Planting Process of 2ZT-2 Beet Transplanter. Transactions of the Chinese Society for Agricutural Machinery 40(1), 69–73 (2009) 7. Zhang, Y., Ou, Y., Mou, X.: Virtual Test on the Finger-Chain type Sugarcane-Lifter Based on ADAMS 25(7), 88–93 (2009)

Simulation and Design of Mixing Mechanism in Fertilizer Automated Proportioning Equipment Based on Pro/E and CFD Liming Chen and Liming Xu* College of Engineering, China Agricultural University, 17 Tsinghua East Road, Beijing, 100083, P. R. China [email protected], [email protected]

Abstract. Precision agriculture is the developing trend of modern agriculture, and the rational utilization of fertilizer is one of the key technologies in the precision agriculture, which needs to fertilize variably according to the crop needs and soil fertility conditions. Thus a fertilizer automated proportioning equipment is developed to Proportion the three fertilizers: Nitrogen fertilizer, Phosphorus fertilizer and Kalium fertilizer. The fertilizer will take greater effect after mixing sufficiently, so this paper mainly researched the mixing mechanism. The simulation and analysis of the velocity field and flow field of the fertilizer for the two paddles worked, the spiral-type paddle and multiple- fan-type paddle, are conducted respectively using Pro/E Pro/Engineer and CFD(Computational Fluid Dynamics) fluid dynamics analysis software. During simulation, the paddle models are meshed using the Gambit software. Then the multiple- fan-type paddle is determined to be the more suitable one through the FLUENT software.

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Keywords: Precision agriculture, Fertilizer Proportioning, Mixing mechanism, Multiple- fan-type paddle, CFD fluid dynamics analysis.

1 Introduction Agriculure is the foundation of our country's economy, but the rough-type form of agriculture is no longer able to meet the development requirements of modern agriculture. Therefore, the development of precision agriculture has become an inevitable trend. Fertilizer, as one of the Production materials in agriculture can increase the crop yield. But for a long time, the extrude Problem of fertilization is illogical fertilizer Proportion and low utilization ratio, high fertilizer input, especially the phosphorus fertilizer input, which results the nutrient input imbalance and the increasing fertilizer input cost . From 1978 to1995, the national fertilizer consumption has increased by 97%, but grain output has increased by only 36% while the crop yield increased by 1%, and the fertilizer amount applied increased by almost 3%. The average fertilizer utilization ratio is less than the developed countries by more than 10%. The utilization ratio *

Corresponding author.

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of Nitrogen is 30%, Phosphorus is from 10% to 25%, Kalium is from 40% to 50%. The increasing fertilizer consumption with low utilization ratio results the decreasing fertilizer returns, serious soil pollutions and groundwater, food contamination. If the fertilizer utilization ratio is enhanced by 10%, 100Mt fertilizers will be saved, which comes up to saving about 10 billion RMB Therefore, the reasonable fertilizer amount according to the crop need can enhance the fertilizer utilization ratio and reduce the environmental pollution. The precision variable-fertilizing technology determines the usage amounts for different kinds of fertilizers according to the soil fertility condition and the crop need. The Proportioned fertilizers should be mixed sufficiently so as to achieve the best effect. Thus it is necessary to design and simulate a mixing mechanism for different kinds of fertilizers. The key component of the Proportioning mechanism is the stirring paddle, which is closely related to the mixing time and the mixing effect for different kinds of fertilizers. For mechanical mixing the paddle is the only source of momentum and has close relationship with the fluid flow in the mixing tank. Thus it is very important to design the paddle. This paper mainly designs and simulates two types of paddles in order to choose a more suitable one. Many researchers have achieved a lot on the variable fertilization technology. The “SOILECTION” fertilization system has been developed by America Ag-chen Equipment Company. It could be applied to solid and liquid fertilizers. This system applied air-seeder and no-tillage seeder can change the used amounts of seeds and fertilizers, even it can change the Proportion of three kinds of fertilizers or seeds. The “SOILECTION” has the advantages of simple, easy to control, high precision and reliability, but it is expensive (Wuyun Zhao et al., 2007). The ST820 variable ratio fertilizer machine with the plant function has been developed by Case Company, it could use the IHAFS software to get the prescription map with computer and then generate prescription file which will be stored in the PCMCIA card. The PCMCIA card could be inserted into the variable ratio controller to Provide preparations for the fertilizer machines to fertilize automatically (Jun Zhao, 2004). The fertilizer machines and equipments for plant Protection in France have the highest automation level among all the agricultural machineries. The French AMASAT variable ratio fertilizer control system has been applied to various types of the centrifugal fertilization machines (Xiaohui Zhang et al., 2002). At present, there are two variable ratio fertilizer machines at home, 2F-VRT1 variable ratio fertilizer machine and 1G-VRT1 rotary variable fertilizer machine. The two machines control the fertilizing amount mainly according to the preferences set by users or the prescription map calculated by the upper computer. The machines can receive the GPS location information and the operating speed signals while they are working in real time, then the rotation speeds of the fertilizer-driven systems are adjusted automatically to achieve the purpose of variable ratio fertilization. The machines also support the manual and automatic control modes, and they can broadcast fertilizers evenly on the soil surface. The two machines are applicable to the fertilization work before sowing seeds, the variable fertilization work for the striking root fertilizer of the winter wheat and the variable fertilization work for forages (Wang Xiu et al., 2008).

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In addition, Israel and China Agricultural Research Institution have developed the liquid fertilizer automated Proportioning machine, which can finish the irrigation work. The one developed by China Agricultural Research Institution mainly control the fertilizer Proportion by detecting the PH value and electrolytic conductivity of the Proportioned fertilizer liquid. In the Proportioning Process, it needs to adjust the flow of each kind of fertilizer manually. The machine is mainly used for the greenhouses. There are many researches on the variable ratio fertilization, but the researches are mainly focused on the liquid fertilizer or just for one kind solid fertilizer. Thus, there are few researches focused on the mixing mechanism for the fertilizer automated Proportioning equipment. In China, the solid fertilizers are used mostly currently, therefore, it is very important to develop a mixing mechanism used in the variable ratio fertilizer machine for several kinds of fertilizers.

2 The Variable Ratio Fertilizer Mechanism for Precision Agriculture An automated matching mechanism for three kinds of fertilizer (N, P, K) is developed in this paper, Fig. 1 shows the working diagram .The mechanism consists of three tanks for three fertilizers. The fertilizers are fertilized by external fluted roller feeds according to the crop needs and soil fertility conditions. The fertilizer feed are driven by step motors to discharge certain amount of fertilizers. The three kinds of fertilizers exhausted get into the mixing tank, in which the fertilizers are stirred and mixed before being fertilized into the soil by another team of fertilizer feeds. The three kinds of fertilizers discharged into the mixing tank must be sufficiently mixed before being fertilized into the soil. Therefore it is needed to develop a paddle to mix the fertilizers sufficiently. The design of paddles should be done according to the form of installation, and the work block diagram of stirring a mechanism is given in the Fig. 1. N

P

fertilizer feed team 1

K

fertilizer feed team 2

M ixing tank

fertilizer feed team 3

（ P addle ）

Fig. 1. The work block diagram

Fig.1 shows that the three kinds of fertilizers are distributed in three areas in the horizontal direction respectively. To meet the installation reliability of the whole mechanism, the paddle can be installed in the form of vertical installation or horizontal installation. But the vertical installation needs the blade diameters to be long, while the

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blade number of the paddles is to be decreased, so the mixing effect is reduced. Thus the paddle is installed in the form of horizontal installation. Due to the low power, large flow and short mixing time of axial paddles, the multi-axial paddle is applied. In this paper, the velocity fields and flow fields of the spiral paddle and the multiple-fan-type paddle are compared and analyzed to determine the suitable one. In this paper, PRO/E Pro/Engineer software is used to design the paddles and finish the motion simulation. CFD (Computational Fluid Dynamics) is used to compare and analyze the velocity fields and flow fields to determine the more suitable paddle. Fig. 2 shows the design flow block.

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Fig. 2. Design flow chart of the stirring paddle

3 Paddle Design 3.1 Design the Spiral Paddle In order to gather the fertilizers on both sides to the center, the spiral paddle (Fig. 3) moves in two-opposition-way, and the fertilizers is decentralized when the rotation direction of the spiral paddle changes

Fig. 3. The structure chart of the spiral paddle

The motion form, motion trace and velocity distribution of the spiral paddle were analyzed through motion simulation in this paper. The lines with deep color in Fig. 4 are path lines of certain point on the paddle. Fig.4 shows that when the spiral paddle rotates reversely, the path line is outward, so it indicates that the spiral paddle can achieve the effect of decentralizing fertilizers. When the spiral paddle rotates forward, the path line is inwards, so it indicates that the spiral paddle can achieve the effect of gathering fertilizers.

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Fig. 4. The path lines of the spiral paddle

3.2 Design of the Multiple- Fan-Type Paddle The design idea of the multiple-fan-type paddle is similar to the spiral paddle, and the biggest difference between them is the blade shape. The blades of the multiplefan-type paddle are fan-shaped. (Fig.5)

Fig. 5. The geometric structure of the multiple- fan-type paddle

Fig. 6. The path lines of the multiple- fan-type paddle

It can be seen from the path lines of certain point on the blade that the multiple-fan-type paddle can make the three kinds of fertilizers decentralized or gathered.

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4 Simulation of the Stirring Paddles The fertilizers stirred by the two paddles moved axially according to the PRO/E-based simulation. But the design and simulation in PRO/E can only supply the paddles’ own movements, and the mixing Process was simulated by the CFD fluid dynamic software. First of all, the designed geometries were imported into the GAMBIT software to finish the work of meshing and setting the boundary conditions. The GAMBIT was chosen for its compatibility with PRO/E. The GAMBIT software can automatically repair the tolerances in the course of importing files established by PRO/E, which guarantees the stability and fidelity of the interface between PRO/E and GAMBIT. The strong meshing ability makes GAMBIT can finish high-quality meshes with special requirements. The special mesh algorithm for GAMBIT can ensure the high quality of tetrahedral, hexahedral and hybrid grids meshed directly in the area with complex geometry. The most important reason is the meshed file established by GAMBIT can be exported into the mesh file, which can be imported into the FLUENT software to Provide good preparation for analysis. But if the whole paddle is to be meshed and simulated, the amount of meshes will increase greatly, which will make the calculation time too long. Because this paper mainly compares the advantages and disadvantages of the two paddles, some part of each paddle was intercepted to simulate in order to meet the design requirements. The simulation models are shown in Fig. 7 and Fig. 8. fluid2

fluid1 Fig. 7. The simulation model for the spiral paddle

In the meshing Process, the mixing tank was firstly subtracted with the fluid area retaining the fluid area, and then the fluid area was subtracted with stirring paddle forming the two basins: fluid1 and fluid2. The mesh size of the field containing the stirring paddle, fluid2, is smaller, while the mesh size of the filed containing the mixing tank, fluid1, is larger .This meshing method could make the analysis of the effect generated by the movement of the stirring paddle to the fluid better. Due to the complex blades of the two stirring paddles, the unstructured tetrahedral mesh was chosen for its strong adaptive ability (Fig. 9, Fig. 10).

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fluid2

fluid1 Fig. 8. The simulation model for the multiple- fan-type paddle

Fig. 9. The grid chart of the spiral paddl

Fig. 10. The grid chart of the multiple- fan-type paddle

After the models were meshed and the mesh quality was checked, the models should be exported in the form of mesh file, and be read into FLUENT to finish the numerical simulations. FLUENT software is designed for fluid analysis, which can simulate fluid floe with complex from incompressible to highly compressible. As a result of a variety

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of solving methods and multi-grid convergence acceleration technique, the FLUENT software can achieve the best convergence speed and accuracy. The standard k-ε turbulence model is used to simulate the single-phase flow fields, and the relationship between the paddles movement and the static wall of the mixing tank is apProached using MRF. The area of fluid1 is set to be motive while the area of fluid2 as static. The paddle is set to be moving wall with the movement of rotation, in other words, the paddle rotation is synchronized with fluid2.The momentum exchange and energy exchange between fluid1 and fluid2 are achieved through the interface. Fig. 11 and Fig. 12 are the convergence curves of the two types of paddles. From the two figures, it can be seen that the residuals of the parameters are convergence, which indicates that the used algorithm is feasible.

Fig. 11. The convergence curve of the spiral paddle

Fig. 12. The convergence curve of the multiple- fan-type paddle

Fig. 13 and Fig. 14 are the speed cloud maps of the two paddles.From the figures, it can be seen that the fluid velocity increases with increasing of the distance from the stirring shaft, and at the blade end position the velocity reached maximum. Then the velocity declines quickly with the distance increasing.

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For a paddle, the flow field formed at work is also an important basis to determine whether the fertilizers can be mixed sufficiently. Because the fertilizer-flow shape is the key factor of sufficient mixing.

Fig. 13. The speed cloud map of the spiral paddle

Fig. 14. The speed cloud map of the multiple- fan-type paddle

Fig. 15. The flow chart of the spiral paddle

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Fig. 15 is the flow field formed while the spiral paddle is operating. It shows that while the paddle is rotating forward, the fertilizers are pushed to move along the spiral by the paddles. Due to the mutual friction of materials, the materials are made to roll up and down, while some part of the materials move along the spiral, which forms the axial spiral movement. The spiral paddle finishes the radial mixing work mainly through making the fertilizers rolling up and down. Because the purpose of the spiral paddle’s movement is to gather or decentralize the fertilizers, there is a swirl with high rotation speed in the center of the paddle while the paddle is rotating. Fig. 16 is the flow field diagram of the multiple- fan-type paddle, from which it can be seen that the fertilizers mainly rotate under the action of the blades. But because the blades are inclined to install and the blade shape is a curved surface, the blade rotation can Promote the fertilizers contacted to move along the axial direction in the spiral form. It also can be seen from the Fig.16 that the multiple- fan-type paddle finishes the radial mixing work mainly through making the fertilizers rolling up and down.

Fig. 16. The flow chart of the multiple- fan-type paddle

From the above simulation and analysis, it can be seen that the two types of paddles both can gather up fertilizers into the center, on the contrary, they also can decentralize fertilizers. Thus, as long as there is one paddle installed in the mixing tank with certain rules to rotate, the fertilizers can be mixed sufficiently. The two types of paddles both can mix fertilizers sufficiently according to the analysis of the speed cloud maps and the flow field maps. Considering the high manufacturing cost of the spiral stirring paddle, and it causes the high-cost mix mechanism, the multiple-fan-type paddle is chosen to be the stirring paddle for the automated Proportioning equipment.

5 Conclusion Precision variable ratio fertilization can increase the fertilizer utilization ratio and decrease the environment pollution. Fertilizer automated Proportioning equipment can Proportion the three kinds of fertilizers: Nitrogen fertilizer, Phosphorus fertilizer

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and Kalium fertilizer, according to the crop needs and soil fertility conditions. The Proportioned fertilizers are spread into the soil after being mixed sufficiently. In the equipment, the stirring paddle is one of the key components. Thus this paper mainly researched the models designed by PRO/E through motion simulation, comparing and analyzing the velocity fields and flow field by the CFD software. The research results supply enough evidences to determine the more suitable paddle. According to the paper, the following conclusions can be launched: (1). Design the geometric structure and finish the motion simulation of the two paddles: the spiral paddle, the multiple-fan-type paddle by using PRO/E. The result shows that the two paddles can gather up or decentralize the fertilizers. (2). Mesh the two models by using the GAMBIT software. In the meshing Process, the mesh size and grid type should be chosen correctly, which is closely related to the simulation results in the FLUENT software. (3). Analyze the velocity fields and flow fields of the two paddles by using the FLUENT software. According to the analysis, the multiple-fan-type paddle is determined to be more suitable, which can make fertilizers mixed sufficiently. The multiple-fan-type paddle is chosen through simulation and comparison. In further researches, this paddle could be manufactured and installed in the automated matching mechanism. Acknowledgements. Funding for this research was Provided by National Science & Technology Pillar Program during the Eleventh Five-Year Plan (2007BAD89B00). The first author would like to acknowledge her tutor in College of Engineering in China Agricultural University for her help.

References 1. Blackmore, B.S.: An information System for Precision farming. Presented at the Brighton Conference Pests and Disease, British Crop Protection Council, pp. 18–21 (1996) 2. Yang, C.: A variable ratio applicator for controlling ratios of two liquid fertilizers. Applied Engineering in Agriculture 17(3), 409–417 (2001) 3. Duan, W.G.: Simulation of Mixing Machine Internal Flow Based on FLUENT. Modern Manufacturing Technology and Equipment, 33–34 (2009) (in Chinese) 4. Han, Z.Z.: FLUENT—Examples of Fluid Engineering Simulation and Analysis. Beijing Polytechnic University Press, Beijing (2009) (in Chinese) 5. Wang, X., Meng, Z.J., Bai, Y.L.: Application of Mechanical variable rate fertilizer in China. In: China Digital Agriculture and Rural Information Research Conference (2005) (in Chinese) 6. Wang, R.J., Zhang, K.: The Base and Application Examples of the Fluent Technology. Tsinghua University Press, Beijing (2007) (in Chinese) 7. Zhang, G.J., Min, J.: The numerical simulation of the mixing Process of the turboPropeller in the stirring tank. Beijing Chemical University Journal 31(6), 24–27 (2004) (in Chinese)

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8. Zhang, S.H., Ma, C.L.: Development and application of a variable ratio fertilizer Applicator for Precision agriculture. Transactions of the Chinese Society of Agricultural Engineering 19(1), 129–131 (2003) (in Chinese) 9. Zhang, X.H., Li, R.X.: The Research and Application of Precision Agriculture in France. Agricultural Research 1, 12–15 (2002) (in Chinese) 10. Zhao, J.: Variation Technology and Its Applications in Agricultural Mechanization. Journal of Modern Agriculture 12, 25 (2004) (in Chinese) 11. Zhao, W.Y., Yang, S.M., Yang, Q., Yang, S.C.: Development and Reflection of Precision Agriculture. Journal of Agricultural Mechanization Research 4 (2007) (in Chinese)

Simulation Study of a Novel Algorithm for Digital Relaying Based on FPGA Renwang He, Dandan Xie, Yuling Zhao, and Yibo Yang School of Electrical and Electronic Engineering, East China Jiaotong University, 330013 Nanchang, P. R. China [email protected]

Abstract. The paper proposes a new fast Fourier transform (FFT) protection algorithm for digital relaying based on field-programmable gate array (FPGA), constructs detailed models and performs corresponding simulations. Simulation results show that the harmonic components of voltages and currents can be conveniently calculated, and the digital protection functions can be effectively fulfilled with enhanced accuracy and reduced storage requirements. Keywords: radix-4 FFT; Qua-input/output; digital protection; MATLAB.

1 Introduction The power system fault signals contain a large number of harmonics and a DC component[1], it needs to extract necessary information to estimate whether the digital protection should operate. Most of the digital protections need to extract the fundamental signal, and filter the other harmonics to avoid malfunction[2]. Fast Fourier Transform (FFT) algorithm can extract every harmonic without attenuation, guaranteeing the other integer harmonics and the DC component attenuating to zero, but can not eliminate the non-integer harmonics and the capacity for suppressing non-periodic components in low-frequency is poor. The radix-4 decomposition, which divides the input sequence recursively to form four-point sequences, has the advantage that it requires only trivial multiplications in the four-point DFT. This results in the highest throughput decomposition, while requiring non-trivial complex multiplications in the post-butterfly twiddle-factor rotations only. Complex multiplication decrease and the four inputs and outputs parallel processing improves processing speed greatly.

2 Fourier Digital Protection Algorithm When ignore the non-integer periodic components, the voltage and current signals in power system can be expressed in the form of Fourier series, x (t ) =

∞

∑X k =0

k

cos( k ω1t + φ k ) .

(1)

where k =0 expresses the DC component; ω 1 is the fundamental angular frequency; Xk , ωk is the amplitude and phase of the kth harmonic component respectively. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 517–520, 2011. © IFIP International Federation for Information Processing 2011

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Expand expression (1) and accord the orthogonality of the trigonometric functions in [0, T1 ], it can be derived: 2 T1 x (t ) cos( k ω1t )dt . T1 ∫0 2 T1 X I k = − X k sin φk = − ∫ x(t )sin(kω1t )dt . T1 0

X R k = X k cos φk =

X

Rk

,X

Ik

(2) (3)

th are the real and imaginary parts of the k harmonic component respec-

tively. X k , X Rk , X Ik are analog while X (k ) , X R (k ) , X I (k ) are digital. Each cycle samples N points and the integral is approximate to the sum [3]. X

Rk

≈ X

R

2 N

(k ) =

⎛ 2π ⎞ x(n ) cos ⎜ nk ⎟ . ⎝ N ⎠

N −1

∑

n=0

⎛ 2π ⎞ x ( n ) s in ⎜ nk ⎟ . ⎝ N ⎠ n=0 th x (n ) x (t ) where is the n sampled value of . X

Ik

≈ X I (k ) = −

N −1

2 N

∑

(4)

(5)

3 Radix-4 FFT Algorithm The sampled power system voltage or current signals are discrete periodic sequences. Their Discrete Fourier Transform (DFT) expressions are: X (k ) =

N −1

∑

x ( n )e

−j

2π nk N

=

n=0

N −1

∑ x ( n )W n=0

nk N

(6)

.

where

WN = e

− j

2π N

From (5) and (6), it can be obtained X Rk + jX Ik = X R ( k ) + jX I ( k ) =

2 N

N −1

∑ x ( n )W n=0

nk N

=

2 X (k ) . N

(7)

Each harmonic needed in digital protection can be calculated by using DFT algorithm. FFT algorithm is an improvement of the DFT algorithm. This paper mainly introduces the radix-4 FFT algorithm according to the DIF (decimation in frequency) [4]. L The sampled points during every cycle are N = 4 , and expression (7) can be written as: 3

3

3

X (k ) = ∑∑" ∑ x(nL−1, nL−2 ,"n1, n0 )WNnk . n0 =0 n1 =0

where

nL−1 =0

(8)

Simulation Study of a Novel Algorithm for Digital Relaying Based on FPGA

n=

L −1

∑n 4

k =

i

i

i=0

,

L −1

∑k

i

i=0

4i

,

519

n i , k i = 0 ,1, 2 ,3

Expression (8) can be written in matrix form 0 ⎡ X 1 (0, nL −2 ,", n0 ) ⎤ ⎡W4 ⎢ X (1, n ,", n ) ⎥ ⎢W 0 ⎢ L− 2 0 ⎥ ⎢ 1 = 4 ⎢ X 1 (2, nL −2 ,", n0 ) ⎥ ⎢W 0 ⎢ ⎥ ⎢ 4 ⎣ X 1 (3, nL − 2 ,", n0 ) ⎦ ⎢⎣W40

W40 W40 W40 ⎤ ⎡ x(0, nL−2 ,", n0 ) ⎤ ⎡1 ⎥ W41 W42 W43 ⎥ ⎢⎢ x(1, nL− 2 ,", n0 ) ⎥⎥ ⎢1 =⎢ 2 4 6⎥⎢ W4 W4 W4 x(2, nL−2 ,", n0 ) ⎥ ⎢1 ⎥⎢ ⎥ ⎢ W43 W46 W49 ⎥⎦ ⎣ x(3, nL− 2 ," , n0 ) ⎦ ⎣1

1 1 1 ⎤ ⎡ x(0, nL −2 ,", n0 ) ⎤ ⎢ ⎥ − j − 1 j ⎥⎥ ⎢ x(1, nL− 2 ,", n0 ) ⎥ . − 1 1 − 1⎥ ⎢ x(2, nL− 2 ,", n0 ) ⎥ ⎥ ⎥⎢ j − 1 − j ⎦ ⎣ x(3, nL −2 ,", n0 ) ⎦

(9)

The multiplication of –j and j just needs only to exchange the real part with the imaginary and adds the necessary plus or minus sign. Expression (9) shows that the computer storage requirements are greatly reduced and the processing speed is enormously enhanced.

4 MATLAB Simulations Sample 64 points per cycle. The 64 points will be divided into four groups, paralleled qua-input and qua-output natural order in the same address, the most critical factor in the calculation is to determine twiddle factors and data reordering. The data flow figure of the basic processor, A0, A1, A2, A3 is the four sets of data. T0, T1, T2 are the corresponding twiddle factors in multiplications each four points group. The processing flow is shown in Figure 1. MATLAB simulation results verify the correctness and effectiveness of the proposed algorithm.

X[k,0] X [k ,1]

-1

G[k,0]

H[k,0]

G[k ,2]

H[k,2]

-1

X[k,2]

-j

-1

H[k,1]

G[k,3]

H[k,3]

j

j

X[k,3]

G[k ,1]

-1 -j

Fig. 1. Basic FFT data flow

5 Conclusions In this paper, the 64-point data in four groups have been reordered twice, and both of the input and the output of the qua-input/output radix-4 FFT algorithm are in natural order. All the necessary power system harmonics are employed as information and criterion of the digital protection. MATLAB simulation results show that the proposed algorithm can reduce the computer storage requirements and increase the processing speed of the digital protection system.

Acknowledgments The authors would like to thank the Research Fund of East China Jiaotong University which supports the project (09DQ08).

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References 1. Babu, P.S., Kumar, S.J.: Accelerated Fourier Algorithm based Digital Multifunction Relay for Transmission Line Protection. In: Fifteenth National Power Systems Conference, Bombay, pp. 23–27 (2008) 2. Zhang, C., Huang, Y., Wang, X.-g.: Application and research of new algorithm for microcomputer-based transformer relaying protection. Relay, Xuchang 27(2), 1–4 (1997) 3. Liu, J., Sun, T.: Application of Fourier algorithm based on FFT in microcomputer-based relay protection. Relay, Xuchang 32(10), 24–26 (2004) 4. Cheng, P.: Digital Signal Processing Tutorial. Tsinghua University Publication, Beijing (2006)

Simulation Study of Single Line-to-Ground Faults on Rural Teed Distribution Lines Wanying Qiu School of Basic Sciences, East China Jiaotong University, Nanchang, P. R. China

Abstract. Teed lines are widely used in rural power networks in China. The paper theoretically analyzes the voltages and currents when a single line-toground fault occurs, constructs a new mathematical model for fault location on the teed lines, performs corresponding MATLAB simulations. Simulation results show that currents near the source (bus M) are almost independent of fault positions and fault resistances. However, voltages at bus M are sensitive to fault positions while fault voltages at fault position are only sensitive to transient resistances. A novel approach to fault location on teed lines is proposed. First, the faulted line is identified with remote control equipment. Then, the transient resistances are calculated according to the fault voltages. And at last, the fault distances are computed according to the voltages at bus M and the calculated transient resistances. Keywords: simulation, fault location, teed line, single line-to-ground fault.

1 Introduction Power lines are typically teed in rural distribution systems in China. Faults occur when equipment insulation fails, due to system overvoltages, lightning, contamination or other mechanical causes. Single line-to-ground faults account for about 90% of the total faults according to the statistics[1-4]. Fault location equipment is developed to find and clear faults as soon as possible. Unfortunately, the accuracy and reliability of present location equipment for rural power networks are not satisfactory. Therefore, it is necessary to study the characteristics of faults in rural power networks and lay a solid foundation for developing corresponding fault location equipment.

2 Theoretical Analysis Consider a single line-to-ground fault from phase a to ground at the general three-phase bus shown in Figure 1. For generality, a fault impedance Zf is included. In the case of a bolted fault, Zf=0, whereas for an arcing fault, Zf is the arc impedance. In the case of a insulator flashover, Zf equals the total fault impedance between the line and ground, including the impedance of the arc and the post or tower. Since any of the three phases can be arbitrarily labeled phase a, analysis results can be easily applied to phase b or phase c. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 521–524, 2011. © IFIP International Federation for Information Processing 2011

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From Figure 1, fault conditions in phase domain are:

Fig. 1. Single line-to-ground fault

Ib = Ic = 0 . U ag = Z f Ia .

(1) (2)

Transforming fault conditions to sequence domain, the sequence components of the fault currents are obtained:

I 0 = I1 = I 2 =

Uf Z Σ + 3Z f

.

(3)

where Uf is prefault voltage and ZΣ=Z0+Z1+Z2. The fault current on phase a can be derived by transforming the sequence components to the phase domain:

Ia =

3U f Z Σ + 3Z f

.

(4)

The line-to-ground voltages can then be determined from equation (2). In the case of a bolted fault, Zf=0, it can be easily obtained that the line-to-ground voltage Uag=0, while Ubg=Ucg=1.732Uph, where Uph is the phase voltage.

3 MATLAB Simulations Typical teed lines are shown in Figure 2.

Fig. 2. Typical teed lines

The MATLAB/Simulink simulation circuit is illustrated in Figure 3.

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Fig. 3. Simulation circuit

In Figure 2, lMR=9 km, lRN=6 km, lRS=3 km. Each line is equivalent to a pi-block as shown in Figure 3. Parameters are from real statistics (written in MATLAB grammar). Source parameters are: Phase-to-phase rms voltage: 10500 V; Phase angle of phase A: 0 degree; Frequency: 50 Hz; Internal connection: Y; Source resistance: 0.1638 Ohms; Source inductance: 0.000236 H. Loads are: PAN=PBN=PCN=100000W; QAN=QBN=QCN=40000Var; PAS=PBS=PCS=300000W; QAS=QBS=QCS=40000Var. Line parameters are: r1=0.330 Ω/km, L1=0.001126 H/km, C1= 9.14e-9 F/km. Set Rf=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 500, 1000] Ω, change the fault point one by one, run MATLAB /Simulink/ SimPowerSystems, and many pages of simulation results are acquired. Parts of the results are shown in Table 1. Table 1. Parts of the Simulation Results

D/km 3 6 9 15

IM/A 62.2 62.2 62.2 62.2

Rf=20Ω UM/V 93.3 180 273 320

UF/V 4.95 4.95 4.95 4.95

IM/A 62.2 62.2 62.2 62.2

Rf=500Ω UM/V 193 273 354 400

UF/V 125 124 123 122

Table 1 implies that currents through bus M are almost independent of fault positions and fault resistances. In other words, fault currents are of little significance for fault location. However, voltages at bus M are sensitive to fault positions while fault voltages at fault position are only sensitive to transient resistances. Therefore, a new approach to fault location on teed lines is presented as follows. First, the faulted line, MR, RN, or RS, is identified with remote control equipment. Then, the transient resistances are calculated according to the fault voltages, which are approximately equal

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to voltages at bus N or bus S. And at last, the fault distances are computed according to the voltages at bus M and the calculated transient resistances. Take line MR for example, by using the least square method of MATLAB: echo on Rf=[0 20 100 500] UN=[0 4.95 24.5 124] plot(UN,Rf) pause Rfu2=polyfit(UN,Rf,1) % fit the Rf-UN curve end The relation between Rf and UF can be obtained as

R f = 4.0305U F + 0.3812 .

(3)

Similarly, the relation among d (fault distance from M), Rf and UM can be also obtained by the least square method.

4 Conclusions Fault currents through bus M are almost independent of fault distances and fault resistances. While voltages at bus M are sensitive to fault distances, and fault voltages at fault position are only sensitive to transient resistances. Therefore, a new idea to locate the fault on teed lines is propose. Firstly, identifying the faulted line; secondly, calculating the transient resistances; and at last, computing the fault distances.

References [1] Zoran, M.R., Shin, J.-R.: New One Terminal Digital Algorithm for Adaptive Reclosing and Fault Distance Calculation on Transmission Lines. IEEE Trans on Power Delivery 21(3), 1231–1237 (2006) [2] Venkatesan, R., Balamurugan, B.: A Real-Time Hardware Fault Detector Using Artificial Neural Network for Distance Protection. IEEE Trans. on Power Delivery 16(1), 75–82 (2001) [3] Sukumar, M.B.: New Fault-Location Method for a Single Multiterminal Transmission Line Using Synchronized Phasor Measurements. IEEE Trans. on Power Delivery 21(3), 1148–1153 (2006) [4] Salat, R., Osowski, S.: Accurate Fault Location in the Power Transmission Line Using Support Vector Machine Approach. IEEE Trans. on Power Systems 19(2), 979–986 (2004)

Single Leaf Area Measurement Using Digital Camera Image Baisong Chen1,2, Zhuo Fu3, Yuchun Pan2, Jihua Wang2,*, and Zhixuan Zeng2 1 School of geography, Beijing normal university, Beijing 100875, China National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China 3 Satellite Environmental Application Center, Ministry of Environmental Protection of the People’s Republic of China, Beijing 100029, China [email protected], [email protected]

2

Abstract. Leaf area index is one of the most important parameters in ecological and environmental studies. This paper presents a method for single leaf area measurement based on the counting of the leaf pixels in digital leaf image. Initially, the target leaf is put on a piece of white paper on which four endpoints of an equal-length and orthogonal cross are printed. A background color threshold is then set to separate the white background pixels from the leaf pixels. After the removal of the background pixels, the leaf pixels are left; and the ratio of the number of leaf pixels to the number of pixels of the reference facet formed by the four control endpoints is equal to that of their areas. The single leaf area can then be computed easily based on this proportional relation. Analysis and experimental results indicate that the proposed method is an efficient and precise method for single leaf or leaf-like object area measurement. Keywords: Digital camera; Leaf area; Area measurement; Image processing.

1 Introduction Leaf is a key functional organ of photosynthesis and transpiration, the size of left area impacts greatly on the physiological function of the plants. LAI (Leaf Area Index) is also an important input parameter of some plant, ecology and environment models [1, 2], thus fast and accurate estimation of leaf area is significant. The currently available approaches for single leaf area measurement are mainly including: using leaf area measuring tools, grids approximation, weighing [3], using planimeter [4], displacement volume computation [5], regression models [6-8], and image processing [9-11], and so on. Among them, using leaf area measuring tools or planimeter is highly accurate, however, with high cost of equipments as well. And other methods such as the grids estimation, weighing, displacement computation and regression models are available for different kinds of leaves and low in cost, but their accuracy is low too. Considering the cost of tools, applicability, accuracy, efficiency, and the convenience for outdoor measurement, the image processing method can be a considerable *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 525–530, 2011. © IFIP International Federation for Information Processing 2011

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method. Based on this, this paper proposes a method for computing single leaf area by separating the background and counting the leaf pixels.

2 Materials and Method 2.1 Leaves for Experiment and Their Areas Thirty pieces of Japan Euonymus leaves with different shapes were selected as experimental materials. They were numbered and put on a 10×10cm white referent paper, respectively. And then they were scanned with a high resolution, respectively. The number of pixels NB in the entire 10×10cm region was then counted in Photoshop, and the number of non-background pixels NL were then computed by removing the white background pixels. Because the scanner images objects in a progressive and orthographic projection manner, the proportion of the area of leaf SL and the area of background SB will equate to that of the number of leaf pixels and the number of background pixels.

SL NL = . SB N B

(1)

Using equation (1), we can compute the actual area of every leaf respectively, and their areas are then considered as true values in the validation section. 2.2 Method and Procedures The principle of this proposed method is the same as that of the above method for measuring leaf area by a scanner, that is, using the proportions of the area and the number of pixels. For the convenience of taking pictures on field, we use a common digital camera rather then a scanner. And for the convenience of program processing and the insurance of accuracy, the referent paper is replaced by four endpoints of an equal-length and orthogonal cross printed on a piece of paper. So the main procedures are as follows: 1) Four endpoints of an equal-length (10×10cm) and orthogonal cross are initially printed on the middle of a piece of common A4 paper. Leaves are then put onto this paper respectively, unwrapped with its centre lapping on that of the cross, leaving four endpoints uncovered. If the leaf cannot be unwrapped naturally, a cut or clip is then needed. 2) Choose suitable settings and parameters for the camera, and then take pictures of leaves respectively, bearing in mind that the primary optical axis should be perpendicular to the background paper with its focus aiming at the centre of the leaf. This ensures the correctness of above equation (1). 3) After the above image acquisition, we obtain images with white background, green leaf, and four black dots. Initially, we find the coordinates of the four endpoints on an image. And from these coordinates, we can compute the distances and included angle of the four endpoints. Because we had 4 endpoints of an equal-length and orthogonal cross printed on the paper, then their corresponding points in an image

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should also be equal-length and orthogonal, if the positions of camera and leaf are right when the picture is taken. Thus, the difference of the in-image distance and the actual distance should not be larger than a threshold, say, 1% of the longer distance. Also, the difference of the included angle should be less than one degree. If they do not meet these conditions, then the positions of the camera and the leaf may be wrong during taking picture, for example, the camera would be in a gradient position. If this is the case, then, equation (1) would no longer hold in such images, and thus they need to have picture retaken. If the image is available, a further operation can then be performed to remove the background. The background is white (RGB: 255, 255, 255), but it will be slightly changed due to the color error of the CCD, thus we can set a color threshold, say RGB greater than 230, to filter those background pixels. After the background pixels are counted, there are pixels of leaf and those four endpoints left. And the four points can be very tiny, so we can ignore their area. This means these non-background pixels can be considered as leaf pixels. Thus, we can compute the numbers of background and leaf pixels, and the area of background (10×10cm). Finally, the area of the interested leaf can be computed via equation (1). 2.3 Flowchart for the Image Processing Based on the above descriptions, we can conclude the main image processing procedures as: 1) coordinates acquisition, 2) distances and included angle computation, 3) error detection, 4) separation of background, 5) left pixels count, 6) leaf area computation. And then the flowchart for programming could be as follows.

Start

Coordinates acquisition by mouse

Compute distance d, included angleθ

△△ d,

θlarge?

Y

N

Set RGB threshold for bkgrd separation

Count the bkgrd and leaf pixels

Compute left area via equation (1)

End

Fig. 1. The flowchart for the image processing of single leaf area measurement

Here are some details need to notice during programming: 1) after four endpoints are picked, two distances can be computed, then the included angle can be calculated by: cosα = (vector1 • vector2) / (|vector1| * |vector2|). Again, the differences of distances and included angle should not be exceeding, otherwise the program should give an alert and terminate; 2) the RGB threshold for background separation can be different because of different lighting situations and camera settings, based on many experiments, we recommend RGB (230, 230, 230) as a threshold, pixels with R, G, and B values all greater than this can be considered as background; 3) if the differences of distance and included angle are not exceeding, then the average number of pixels should be used to compute the number of pixels of the 10×10cm square; 4) leaf area computed by equation (1) is just one side area of the leaf area, if area of double sides is wanted, a multiplication with two is then needed.

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3 Results and Discussions For the convenience of program processing, based on the above method, we developed a program within VC 6.0 and its MFC and an open JPEG lib called JpegLib. To test the accuracy and efficiency of this method, all the selected leaves were measured by this program, then, we computed the RMSE for every single leaf via the following formula:

RMSE =

1 n (Sm − St ) 2 . ∑ n i =1

(2)

Where n is total number of leaves, Sm is the area of a numbered leaf measured by program, St is its corresponding true value of area. To find a best resolution for this type of leaf, we took pictures of these leaves with different resolutions, and then we computed the RMSE and recorded the average processing time for every image, results are showed in the Table 1 below. Table 1. The RMSE and processing time for single leaf area calculation at different image resolutions Resolution 640×480 1024×768 1600×1200 2048×1536 2272×1704

RMSE (cm2) 0.07 0.04 0.02 0.02 0.02

Average processing time (ms) 23 78 221 617 1387

Configurations: CPU Pentium D3.2G, 2G DDR2 800RAM, WinXP; The best resolution would vary for different objects.

As indicated by Table 1, the RMSE remains the same when the resolution is higher than 1600×1200; instead, higher resolution image demands larger storage space and longer processing time. A Jpeg decoding is needed in every time when the program reads the image file, also, a full traverse of all pixels is needed when the program tries to count the amounts of the background and leaf pixels. Thus, there is no benefit to using a resolution higher than 1600×1200 for leaves like Japan Euonymus’s. Therefore, the best image-taking resolution is 1600×1200. To test the stability of this method, we took 20 pictures for a single leaf using the best resolution, and then we computed the area for every picture, and based on the results, we got the following basic statistics in Table 2. As shown in Table 2, the coefficient of variation and mean relative error are both small, indicating that the areas are mostly approximate, and this method is highly accurate and stable. The principle of this proposed method is quite similar to that of the reference [9]. To compare these two methods, we took one picture for every selected leaf at the best resolution, and then we computed areas using both methods, respectively. The results are shown in Fig. 2 below.

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Table 2. The statistical results of 20 area measurements for a single leaf at resolution of 1600×1200 True area(cm2)

Min(cm2)

Max (cm2)

Mean(cm2)

Standard deviation(cm2)

Coefficient of variation

5.26

5.16

5.35

5.27

0.06

1.1%

Mean relative error 0.2%

Note: Mean relative error = |measured area - true area| / true area × 100%.

] 9 7 [ e c 6.5 n e 6 r e f5.5 e r 5 f o4.5 d o h 44 t e M

Unit: cm^2

4.5

R2=0.9747

5

5.5

6

The proposed method

6.5

7

Fig. 2. The comparison of measurements of 30 pieces of leaves using two different methods

As shown in Fig. 2, the results of the two methods are quite similar, with high correlation too. But, there is still an improvement in accuracy in our method. Because of the virtual equal-length and orthogonal cross, the program can determine if the positions of camera and the leaf are correct when the picture is taken. This ensures the correctness of equation (1) and thus avoids the gross error and improves the accuracy. Our method distinguishes the leaf pixels from the background ones by means of their color difference. This brings additional benefits such as: automatic removal of the holes and the sear or ill spots. This method can also be used to measure areas of some leaf-like slices such as paper money and so on.

4 Conclusion This paper proposes a method for single leaf area measurement by means of counting of the background and leaf pixels. In this method, a virtual equal-length and orthogonal cross is adopted to avoid the potential gross error and improve the accuracy of leaf area measurement. Experiments indicate that the best image resolution for moderate or small leaves is 1600×1200. This method is also available for the area measurement for any leaf-like slice. Acknowledgments. This work was supported by the National Key Technology R&D Program of China (No. 2008BAJ08 B03). The authors would also like to thank the anonymous reviewers for their constructive comments and suggestions.

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References 1. Chen, J.M., Pavlic, G., Brown, L., et al.: Derivation and Validation of Canada-Wide Coarse-Resolution Leaf Area Index Maps Using High-Resolution Satellite Imagery and Ground Measurements. Rem. Sens. Environ. 80, 165–184 (2001) 2. Jongschaap, R.E.E.: Run-Time Calibration of Simulation Models by Integrating Remote Sensing Estimates of Leaf Area Index and Canopy Nitrogen. Eur. J. Agron. 24(4), 316– 324 (2006) 3. Diao, J., Lei, X.D., Hong, L.X., Rong, J.T., Shi, Q.: Single Leaf Area Estimation Models Based on Leaf Weight of Eucalyptus in Southern China. J. Forest Res. 21(1), 73–76 (2010) 4. Gist, C.S., Swank, W.T.: An Optical Planimeter for Leaf Area Determination. Am. Midi. Nat. 92, 213–217 (1974) 5. Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M., Plummer, S.: Leaf Area Index of Boreal Forests: Theory, Techniques, and Measurements. J. Geophy. Res. 102(D24), 29429– 29443 (1997) 6. Williams III, L., Martinson, T.E.: Nondestructive Leaf Area Estimation of ‘Niagara’ and ‘Dechaunac’ Grapevines. Sci Hortic-Amsterdam 98(4), 493–498 (2003) 7. Vertessy, R.A., Benyon, R.G., O’Sullivan, S.K., Gribben, P.R.: Relationships between Stem Diameter, Sapwood Area, Leaf Area and Transpiration in a Young Mountain Ash Forest. Tree Physiol. 15, 559–567 (1995) 8. Kumar, R.: Calibration and Validation of Regression Model for Non-Destructive Leaf Area Estimation of Saffron (Crocus Sativus L.). Sci. Hortic-Amsterdam. 122, 142–145 (2009) 9. Xiao, Q., Ye, W.J., Zhu, Z., Chen, Y., Zheng, H.L.: A Simple Non-Destructive Method to Measure Leaf Area Using Digital Camera and Photoshop Software. Chinese Journal of Ecology 24(6), 711–714 (2005) (in Chinese with English abstract) 10. Caldas, L.S., Bravo, C., Piccolo, H., Faria, C.R.: Measurement of Leaf Area With a HandScanner Linked to a Microcomputer. Revista Brasileira de Fisiologia Vegetal 4(1), 17–20 (1992) 11. Li, M., Zhang, C.L., Fang, J.L.: Extraction of Leaf Area Index of Wheat Based on Image Processing Technique. Transactions of the CSAE 26(1), 205–209 (2010) (in Chinese with English abstract)

Sliding Monitoring System for Ground Wheel Based on ATMEGA16 for No-Tillage Planter—CT246 Lianming Xia, Xiangyou Wang*, Duayang Geng, and Qingfeng Zhang School of Agricultural and Food Engineering, Shandong University of Technology Zibo, Shandong, China [email protected], [email protected], [email protected], [email protected]

Abstract. The seed-metering device of the no-tillage planter was driven by the ground wheel. Sliding was unavoidable when the ground was covered by the straw. High sliding brought seeding absence and affected the quality of sowing. In order to monitor the sliding of the ground wheel, a monitoring system composed of hardware and software was designed based on ATMEGA16 Microcomputer. In the system, the practical and the theoretical driving distance of the ground wheel were measured by the photoelectric sensor and rotary encoder respectively, then the sliding can be calculated and displayed on the LED. The system could achieve the data of sliding when the planter worked, and this could insure that the driver may recognize the working conditions at any moment. So it had very important significance which can improve the precision of sow. Keywords: no-tillage planter, sliding, photoelectric sensor, rotary encode, single-chip microcomputer.

1 Introduction Conservation tillage has been defined as tillage and planting system that retains at least 30% of the cover residues on the soil surface after planting operation is completed. Erosion is reduced by at least 50% in these soils as compared to bare soils [1][2][3]. Retain crop residues on the soil surface provide a source of plant nutrients, improves organic matter level in the soil, and increase soil water content by reducing evaporation and increasing infiltration rate[4]. In the last three decades, no-till sowing practices that promote soil and water conservation have slowly become an accepted alternative to conventional tillage systems. The precision and no-till seeder has been a key factor in the successful shift to no-till sowing [5]. In the precision and no-till seeder, the seed-metering device which is driven by the ground wheel places seeds at the required spacing. The sliding which can bring seed absence is unavoidable when the field is covered with crop residue and this can affect the quality of sowing. Moreover, in response to the recent trend toward improving the quality of sowing, many researchers have focused their research on the development of precision *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 531–537, 2011. © IFIP International Federation for Information Processing 2011

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seed-metering device and little work has been done to evaluate the sliding for the no-till precision planter. Although it was realized that sliding of ground wheel could affect the quality of sowing, what could do was only to let the tractor drive for a certain distance and then calculate the sliding by counting the number of turns. Then the quantity of sowing can be adjusted based on it. This could only improve the average sliding to a certain extent. However, the quality of sowing could not be improved by only adjusting the quantity of sowing with the varying sliding. In this research, a sliding monitoring system based on the technologies of single-chip microcomputer and sensor was designed to help the driver realize the sliding at real-time.

2 Analysis of the Sliding Monitoring Theory The sliding of the ground wheel is caused by the different driving distances between the tractor and the ground wheel. So, the sliding can be calculated by the formula below

η=

s1 − s2 ×100% s1

(1)

Where s1 is the practical distance the tractor drives, s2 the theoretical distance the ground wheel drives [6]. A five-wheel gauge was usually used to measure the practical distance of the ground wheel drove. However, the result was often affected by the factors such as the bounce of the fifth wheel brought by the ground flatness, the difference between the arc and lineal length of the ground surface, the sliding which was caused by the no-pure rolling between the fifth wheel and the ground. In the no-tillage sowing, the ground is covered by a lot of crop residue. So, the error would be very obvious when it was measured by the five-wheel gauge [7]. In this paper, the practical driving distance is measured by the photoelectric sensor and this is showed in Fig1.

Fig. 1. The theory for measurement of the practical driving distance

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3 The Structure of the Monitoring System In this paper, the single-chip microcomputer used is ATMEGA16. Its pins are showed in Fig2.

Fig. 2. The pins of ATMEGA16

3.1 The Circuits of the Sensors In this paper, the model of the photoelectric sensor used is E3F-DS60C4 which can detect the signals in 70-centimeter range. The rotary encode E6B2-CWZ6C is produced by

Fig. 3. The circuits of the sensors

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the company of OMRON and it can send out 360 pulse sis when the ground wheel revolves for a round [8] [9]. The circuits of the sensors are showed in Fig3. From Fig2 and Fig3, it can be seen that the photoelectric sensor and the rotary encode are connected to PD2 and PD3 which are the external interruption pins of ATMEGA16. The output level sent out from PD2 is high when the photoelectric sensor is near the bamboo pile and low when it is away from it. The PD3 pin sends out high LEVEL when the rotary encode revolves and low or high level when it does not revolve [10]. All interruptions are triggered at the falling edge. Although the photoelectric sensor and the rotary encode can send out changing level, it can not absorb and send out too high current, or it can not make the current of the interruption pin be low. So a PNP9013 is used as switch in Fig3. 3.2 Design of the LCD Circuit In the monitoring system, LCD12864 with FLASH is used to show the sliding of the ground wheel. Its wiring diagram is showed in Fig4. As showed in Fig4, port LCDDATA is used as the I/O port of LCD, LED and the four-digit digital diode. It is also the extension of PA. Its ports are composed of ten groups of two-pin jumpers which are linked to the pin MAMBD. The pins of the LCDDATA from left to right are showed in Table1. LCDControl is the extended port of PB which is the control port of LCD12864 or LCD1602. The ports of LCDControl are composed of five group two-pin jumpers which are linked to pin PB0 to PB4 of M2MD1.0. The pins of port LCDControl are showed in Table2.

Fig. 4. Wiring diagram of LCD12864

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Table 1. The pins of LCDDATA

Table 2. The pins of LCDControl

The wiring diagram of the monitoring system is showed in Fig5.

Fig. 5. Wiring diagram of the monitoring system

4 Design of Software System In the main program, four global variables and one global array are set. In the array, only three elements are used. The interrupt 0 and 1 are used to count the numbers of

Fig. 6. Flow chart of the main program

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Pulse signals from the photoelectric sensor and the rotary encode. In the interruption system, flag bit num0 and num1, flag0 and flag1 are needed to be updated. The screen will blink when interruption0 counts one pulse signals and interruption1 counts ten pulse signals. The value of the sliding will refresh when the interruptions count twice and at the same time all flag bits are cleared. In this system, rate0 and rate1 are used to indicate the distances when one impulse signal is interrupted. The flow chart of the main program is showed in Fig6 [11].

5 Experiments and Discussion In the experiments, the factors which can affect the sliding are the soil conditions, the parameters of the ground wheel and the vertical load on the wheel [12]. In order to test the performance of the monitoring system, some ground wheels with different parameters and vertical loads were chosen to work in different soils. The parameters of the ground wheel and the vertical load were showed in Table3. Table 3. The parameters of the ground wheel

Table 4. The results of the experiments with different ground wheels

According to the parameters of Table3, five ground wheels with different parameters were chosen to test the performance of the system, the parameters and results were showed in Table4.

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From the results above, it could be seen that the monitoring system correctly tested the sliding of the ground wheels with different parameters.

6 Conclusions In this paper, an automatic instrument based on the technologies of sensor and singlechip microcomputer was used for precision planter. It could measure the sliding of the ground wheel when the tractor drove on the soil which was covered by the straws at real-time. The driver could recognize the sliding of the ground wheel and this would help him control the quality of sowing timely.

References 1. Karayel, D.: Performance of a Modified Precision Vacuum Seeder for No-till Sowing of Maize and Soybean. J. Soil & Tillage Research 104, 121–125 (2009) 2. Dawelbeit, M., Babiker, E.A.: Effect of Tillage and Method of Sowing on Wheat Yield in Irrigated of Rahad, Sudan. J. Soil & Tillage Research 42, 127–132 (1997) 3. Barut, Z.B.: Determination of the Optimum Working Parameters of a precision Vacuum Seeder. PhD Thesis University of Cukuriva, Adana, Turkey (1996) 4. Wang, Q., Chen, H., Li, H., Li, W., Wang, X., McHughb, A.D., He, J., Gao, H.: Controlled traffic farming with no tillage for improved fallow water storage and crop yield on the Chinese Loess Plateau. J. Soil & Tillage Research 104, 192–197 (2009) 5. Baker, C.J., Saxton, K.E., Ritchie, W.R.: No-tillage Seeding and Practice, 2nd edn. CAB international, Oxford (2002) 6. Gao, Y.: Ground wheel Slide of no Tillage Seeding-machine. China Agricultural University (December 2001) 7. Zhang, B., Yu, Q.: The simulation study of sliding For wheeled tractor based on PD control. Journal of Beijing Agricultural University (February 1995) 8. http://www.fa.omron.com.cn 9. Xu, L., Zhao, Y.: Design of Incremental Photoelectrical Encoder Interface Based on Single-chip. Machinery & Electronics 12, 10–11 (2006) 10. Cui, Y.: A Design Instance of Measurement System for Rotary Speed Based on Photoelectric Encoder. Management and Development 10, 74–75 (2007) 11. Taner, A.H.: Virtual instrumentation: a solution to the problem of design complexity in intelligent measurement/control 7, 165–171 (1996) 12. Walsh, P., Jenson, T.: Towards a Standard for Controlled Traffic Machinery. In: Proceeding of the Second National Controlled Traffic Conference of Australia, pp. 27–28 (1999)

Soil Erosion Features by Land Use and Land Cover in Hilly Agricultural Watersheds in Central Sichuan Province, China Zhongdong Yin1, Changqing Zuo2, and Liang Ma3 1

Key Laboratory of Soil & Water Conservation and Desertification Combating, Ministry of Education, Beijing Forest University, 100083 Beijing, China 2 Research Center on Soil and Water Conservation of the Ministry of Water Resources, Beijing 100044, China 3 Key Laboratory of Water Resources and Environment of Shandong Province, Water Resources Research Institute of Shandong Province, Jinan 250013, China [email protected]

Abstract. The study assessed soil erosion features in hilly agricultural watersheds in central Sichuan Province, China. Relationships among area percentages of land covers, soil erosion modulus, soil loss rate, and area percentage of erosion in the watersheds were examined by multivariate regression analysis. The first two regression models were constructed with area percentages of land covers as independent variables and soil erosion modulus or soil loss rate as dependent variables. The third one was built with soil erosion modulus as dependent variable and area percentage of the four erosion intensity classes as independent variables. Results showed that for flat and slope fields, forests, shrubs, abandoned fields, and other land covers, percentage of soil loss were -6%, 89%, -4%, 11%, 12%, and -3%, and area percentage of erosion were 1%, 72%, 0%, 18%, 9%, and 0%. The slope fields were the main source of soil loss. In contrast, flat crop fields, forests and other land covers could conserve soil from eroding. Changing land covers and reducing area of slope fields to 7% and of abandoned fields to 0%, were proposed as a management strategy for soil conservation in the watersheds. As completion of the land cover conversion, percentages of the land covers should be 35%, 7%, 5%, 45%, 0%, and 8% for flat and slope fields, forests, shrubs, abandoned fields, and other land covers. The study suggested that statistical models could be successfully used for processing inventory data for make decisions in soil conservation. Keywords: land use, soil erosion, Watersheds.

1 Introduction Hilly land in central Sichuan Province, China is the major agricultural production area in Sichuan and in the Yangtze River upstream, supporting approximate 75% of population in the province. Around 1950’s and1960’s, forests were harvested and the forested lands were converted to crop fields under increasing population pressure (Xu, D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 538–550, 2011. © IFIP International Federation for Information Processing 2011

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2000). During this period of time, the rates of deforestation and expanding of agricultural fields reached the pike. Consequently, the land cover conversion contributed to exposure of purple shale, which was vulnerable to weathering, and thereby caused serious soil erosion in the region with an soil erosion modulus of 4, 000 – 7, 000 t/km2·a. As massive soil particles were transported into the Yangtze River, soil fertility was declined. Consequently, agricultural productivity and local economics were influenced severely. In order to conserve soil from erosion, key soil and water conservation projects in the upper and middle reaches of the Yangtze River (KSWCPUMRYR) has been in place for 20 years. Including central Sichuan province, it has been implemented in 7 regions across 10 provinces. It is one of the largest projects in China in ecological and environmental conservation. The goal of this project is to conserve soil and prevent soil from eroding at watershed scale through changing proportion of land covers in the watersheds. Therefore, analysis of relationships of land covers and soil erosion features could supply scientific information for successful implementation of the project.

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Fig. 1. Study area in central Sichuan Province, China

Relationships of land cover and soil erosion have been studied extensively. Most studies have demonstrated that soil erosion is correlated to land-use type (Kosmas et al., 1997 Cerdan et al., 2002; Bakker et al., 2008). For instance, soil erosion modulus in abandoned fields (De Santisteban et al., 2006 Kakembo and Rowntree, 2003) is the highest, and then followed by cultivated land (Ge et al., 2007 Martinez-Mena, et al., 2008). In contrast, soil erosion modulus in forested land is much lower than cultivated lands (Hou et al., 2007 Cox et al., 2006), even in some cases, the soil loss in forested land is less than 10% of the total loss (Fox and Papanicolaou, 2008). However, the loss in forested land varies to a great extent by types of forests (Andreu et al., 1998). The main research methods in exploring the relationships between soil erosion and land cover consist of remote sensing (Pandey et al., 2007; Huang et al., 2007), tracer element

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；

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(Song et al., 2003; Fox and Papanicolaou, 2007), and field investigation (Hartanto et al., 2003). Advantage of these methods is the high accuracy of the collected data, but most results from the data are site-specific. Applying statistical methods in studying relationships between soil erosion and land cover at large sale may be an alternative approach as adequate sampling data are collected. Therefore, the goal of this study is to analyze relationships of land covers with soil erosion features in agricultural watersheds using statistical modeling approaches. The watersheds located in hilly lands, with crop coverage greater than 60%, in four cities and 13 counties in Sichuan Province, China (Fig. 1).

2 Methods 2.1 Study Area The hilly land in central Sichuan province is located in central part of Sichuan Basin, north of the Yangtze River, south of Jiange, Cangxi, and Yilong Counties, east of Longquan Mountain, and west of Hua Ying Mountain, and across 9 cities and 48 counties. The region occupies a total land area of 120 thousand km2, with 29.5% cultivated land, 21.3% forestland, and 7.5% water bodies. The low hilly land occupies 21.4% of the purple soil region, mid-hilly 25.5%, and low mountain 25.5% (State Land Bureau of Sichuan province, 1989). In the purple soil hilly land region, elevation with a range of 300-700m decreases gradually from north toward south. The soil is mainly from purple-red mudstone and sand-stone formed in Mesozoic period. This region has a subtropical humid climate characterized by average annual temperature about 17ºC, an annual average precipitation of 1000 mm, reaching the pikes between May and September, and about 280 - 330 non-frost days. In Sichuan, erosion area and amount of soil loss account for 70% of the total areas and loss in the Yangtze River upstream. Hilly land in central part of the province is suffering the severest soil erosion, where erosion area and soil loss comprise 21.19% and 24% of the totals in the entire upstream of the river. Intensity of soil erosion reaches class moderate and above it, and soil erosion modulus climbs up to 3000t/km2·a as remotely sensing data reveal (China PRC, 2002). 2.2 Data Collection The data for this study were collected by following standard methods in “Annual Inspection and Evaluation Regulations of KSWCPUMRYR” and “Detailed Inspection and Evaluation Regulations Enforced as Completion of KSWCPUMRYR.” First of all, the land covers were classified into five categories of crop, forest, pasture, abandoned land, and others, and then area percentage of each land cover was calculated. After than, the land cover was further classified into 13 classes including paddy fields , terrace, slope fields with slope less than 25º and greater than 25º, forests, shrubs, sapling/seedling forests, orchards, abandoned land, pasture, water, wastelands, and others.

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In reality, the first land classification scheme was too broad and not practicable. As the area fractions of the 13 classes were used to develop a model for simulation of the relations between land cover and soil erosion, the model did not fit the data well. Therefore, we combined the original 13 land cover classes into six: flat crop fields (including paddy fields and terraces), slope fields include slope fields with slope less than 25º and greater than 25º), forests, shrubs (consist of shrubs, sapling/seedling forests, and orchards), abandoned land (comprise abandoned land and pasture), and others (include water, wastelands, and others).

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3 Model Development Relationships between soil erosion and land cover were analyzed by constructing regression models with variables of absolute area or area fraction of land covers in watersheds and total soil loss from the watersheds. A preliminary study showed that a satisfied result could be achieved as absolute area of each land cover was used. But this approach did not consider effects of watershed size on amount of soil loss. If area percentages of land covers were employed, then the effects of watershed areas on total soil loss could be minimized. However, adding or removing independent variables in regression analysis could impact the modeled relations to a great extent. The first two multivariate regression models were constructed with area percentages of land covers as independent variables and soil erosion modulus or soil loss rate as dependent variables. The third model was constructed with soil erosion modulus as dependent variable and area percentage of the four erosion intensity classes as independent variables. All statistics analysis were conducted by running software SPSS. In the regression equation Y = b1X2 + b2X2 + … + biXi + … + bnXn, Y was soil erosion modulus (t/ km2·a), and X stood for percentage area of land covers with subscript i representing types of land cover. Percentage of soil loss for a land cover (P) in a watershed was computed by the equation 1:

Pi =

bi X i 6

∑b X i

i =1

(1) i

The percentage of soil loss for a land cover in all 40 watersheds was computed by the equation 2: 40

Pi =

∑b X 6

j =1 40

i

ij

∑∑ b X i =1 j =1

i

(2) ij

where j was index of the watersheds, from 1 to 40. The acceptable maximum area percentage of slope fields for soil conservation was computed by equation 3:

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P = 500/b

(3)

where b was the regression coefficient of slope fields.

4 Results 4.1 Soil Erosion in the Watersheds Average area of the 40 watersheds was 31.03 km2 with 35% flat crop field, 36% slope fields, 5% forests, 11% shrubs, 5% abandoned fields, and 8% other land covers. The average annual soil loss in the watersheds was 79187t, with average soil erosion modulus of 2562 t/km2·a. Soil erosion modulus in 16 watersheds was between 500 and 2500t/km2·a, in 23 watersheds between 2500 and 5000t/km2·a, and only in one watershed between 5000 and 8000t/km2·a. In the study area, 51.2% land area did not show apparent soil erosion. In contrast, on 48.8% eluding land, the percentage area of light, moderate, intense and above erosion accounted for 9.3%, 19.1%, and 20.4% (Table 1). Table 1. Soil erosion characteristics by erosion intensities

insignificanta Light Moderate intense and above

Land area percentage (%) 51.2% 9.3% 19.1% 20.4%

Average soil erosion Average percentage Reduction of modulus of soil loss soil lossb (t/km2·a) (%) (%) -397.78 -7% 1639.95 6% 4% 5000.00 34% 31% 9119.12 67% 64%

a

Insignificant erosion denoted the erosion class for which soil erosion modulus was less than 500t/km2·a. b Reduction of soil loss was the percentage of soil loss after to before the conservation. After the conservation, the soil erosion modulus was decreased to 500t/km2·a.

4.2 Soil Erosion by Erosion Intensities A regression equation between soil erosion modulus and area percentage of the four class of erosion intensity was constructed and expressed below: Y = -397.78X1 + 1639.95X2 + 5000X3 + 9119.12X4 R2 = 0.761

(4)

Y was soil erosion modulus in t/km2·a, X1-X4 were percentage areas of the four erosion intensity classes: insignificant erosion, light, moderate, intense and above. The equation showed that soil erosion modulus differed by erosion intensity. The soil erosion modulus rose with increasing erosion intensity. For the intense and above erosion as well as moderate intensity, percentages of soil loss were 67% and 34%. On the contrary, for the light intensity, the erosion area and rate were low (6%) (Table 1 .

)

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Among all 40 watersheds, soil loss in 32 watersheds was mainly due to intense erosion, and only in eight watersheds, the loss was primarily from the moderate erosion intensity. The percentage of soil loss for erosion intensity of light, moderate, intense and above varied by watersheds and presented wide ranges of 0% - 21%, 8% - 100%, and 11% - 98% for the three intensities. In contrast, the retained soil on insignificant erosion land was 3% - 23%. 4.3 Area Percentage of Soil Erosion by Land Covers The regression model with soil loss rate as dependent variable and area percentage of flat crop field (X1), slope fields (X2), forests (X3), shrubs (X4), abandoned fields (X5), and other land covers (x6) as independent variables was constructed and expressed below: Y = 0.01X1 + 0.99X2 + 0.78X4 + X5

(5)

Where R2 = 0.936.

The model was constructed by setting ranges of regression coefficients between 0 and 1. Forests and other land covers were excluded from the model owing to the preset regression conditions. Based on regression coefficients, the area percentage of soil loss for slope fields was the highest (72%), and then followed by forests (17%), abandoned fields (10%), and flat crop fields (1%), indicating that area of soil loss on slope fields was the highest, and prevention erosion on slope fields should the priority of soil conservation. In addition, the soil erosion rate showed that 100% of abandoned fields experienced soil erosion, then followed by slope fields (99%), shrubs (78%), flat crop fields (1%), forests (0%), and other land covers (0%) (Table 2). Table 2. Soil erosion by land covers

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In the 40 watersheds, area percentage of soil loss in slope fields was the highest. Range of the area percentages for slope fields, shrubs, abandoned fields, and flat crop fields were 52% - 96%, 4% - 39%, 5% - 26%, and 0.3% - 2%. 4.4 Soil Erosion Modulus by Land Covers The regression equation between soil erosion modulus and percentage area of the different land covers was built and expressed below: Y = -458.273X1+6410.798X2-2083.886X3+2668.428X4+6639.325X5-759.599X6

R2 = 0.574

(6)

Where, X1 through X6 represented area percentage of flat crop field, slope fields, forests, shrubs, abandoned fields, and other land covers in the watersheds. The equation revealed that soil loss was mostly from slope fields where percentage of soil loss reached 89%, then followed by abandoned fields (12%) and forests (11%). The remaining land covers contributed about 13% of soil loss. Among all the land covers, percentage of soil loss in slope fields was the highest (Table 2). The percentage ranged by land covers with 67% - 121% for slope fields, 2% - 37% for shrubs, and 0% - 29% for abandoned fields. Even though the soil erosion modulus in abandoned fields was the highest, but the soil loss from the land was low because of small area fraction in the watersheds.

5 Discussion 5.1 Methods of Studying Relations between Soil Erosion and Land Use

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Many studies have demonstrated that soil erosion modulus varies by land covers of crop, forests, uncultivated lands (Vacca et al., 2000 López-Vicente et al., 2008; Capolongo et al., 2008; Zhang et al., 2003; McDonald et al., 2002). The applied research methods mainly include remote sensing, field observation, and isotope tracer. These methods have their own advantages or disadvantages. For instance, remote sensing techniques are appropriate for assessing large area soil erosion. The land covers derived from remote sensing is very close to the field investigation. However, models such as USLE, SWAT, and WEPP are also required for studying soil erosion (Pandey, et al., 2007; Baban and Yusof, 2001; Demissie et al., 2004; Catani et al., 2002; Cochrane and Flanagan, 1999). These methods did not directly target on soil erosion as field investigations and are complicated for applications. The field investigations can accurately measure the amount of soil loss. However, the field investigations usually do not cover the all land as the soil conservation regulations require. Therefore, the research activities cannot supply practical information for land management and soil conservation (Okoba and Sterk, 2006). In addition, sampling sites are often too few to represent characteristics of soil erosion for large area. However, government inventory data covering the entire watersheds collect data from enormous field sites. Therefore, accuracy of the data is not high enough as the

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data collected from fixed plots. Therefore, errors in the inventory data have to be minimized by using appropriate research methods, such as fixed plots. It necessities to explore which method is the best. 5.2 Soil Erosion by Erosion Intensities This study showed that soil erosion modulus varied by erosion intensity, implying that the various strategies for soil conservation could be taken for the specific soil erosion condition. In addition, the rates of soil loss for moderate and intense erosion intensity were much higher than the lowest limits in the Standards for Classification and Gradation of Soil Erosion proposed by the Ministry of Resources of the People’s Republic of China. Therefore, the conservation measures should be enhanced based on the results from this study. This study indicated that percentages of soil loss varied greatly by erosion intensities. The percentage for intense and above erosion intensity reached 67%, which was very close to the loess soil erosion (Pan and Dong, 2006). If soil erosion on the land with intense and above erosion intensity were controlled to insignificant erosion intensity (<500t/km2·a), then soil loss could reduce 64%, and the average soil erosion modulus might be decreased to 932t/km2·a. Therefore, controlling intense and above soil erosion could apparently reduce soil loss in the watersheds. Strategy of controlling intense and above soil erosion should be addressed as the priority in KSWCPUMRYR. Percentage of soil loss on moderate erosion land (34%) was lower than it on the intense and above erosion land. If the moderate erosion were controlled to the insignificant erosion intensity, then soil erosion modulus could be reduced to 1776t/km2·a. Even though the reduction of soil loss from controlling moderate erosion was much lower than it from the intense and above erosion, the moderate erosion had to be prevented for meeting the criterion of controlling erosion to insignificant erosion intensity in the watersheds. For light erosion, both the erosion area and the soil erosion modulus were lower than those of the moderate and sever erosions, and the soil loss was only 6% of the total loss in the watersheds. After completion of conserving soil from intense and moderate erosions, even the light erosion was not be conserved, the soil erosion modulus in the watersheds could be reduced to 149/km2•a. Therefore, the study suggested that it was not necessary to protect light erosion land. However, due to the possible conversion of the light to moderate erosion and the difficulties in controlling soil loss from moderate erosion, therefore, simple measures, such as enclosing, were recommended for the conservation. For insignificant erosion, it was not necessitated to apply any conservatory measures. 5.3 Area Percentage of Soil Erosion by Land Covers Soil loss rate in abandoned land in the watersheds reached to 100%, implying soil erosion on the land should be conserved firstly. The land was vulnerable to soil erosion owing to low vegetation coverage, high slope degree, and low fertility. In terms of amount of soil loss and erosion area, which land, shrubs or abandoned fields, should be conserved firstly have to be determined according to specific situations on the watersheds.

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In terms of erosion areas, the sequential priority on soil conservation should be slope fields first, then followed by shrubs, and abandoned fields. However, in some watersheds, the erosion area of abandoned land was greater than that of shrubs. Therefore, conservation of abandoned land should be set earlier than forests. 5.4 Soil Erosion Rate by Land Covers The typical feature of land cover in the study area is the low rate of forest coverage (5%) caused by population pressure and deforestation in 1950’s and 1960’s (Xu, 2000). The dominant agricultural land cover was formed at this period of time. As the region was encompassed in the KSWCPUMRYR, the soil has been conserved through changing the land use and land covers by following polices proposed by KSWCPUMRYR. The study revealed that in the purple soil region in central Sichuan province, the soil erosion modulus varied greatly with land covers. In slope fields, the average soil erosion modulus was 6410t/km2·a, which is similar with the values by previous studies (Shi, 1999 Cui et al., 2008). In the slope fields, the percentage of soil loss was 89%, and area percentage of soil loss was 72%, which were greater than the values by Cui et al.(2008) and Hua et al.(2007). The possible reason is that the studied watersheds are agricultural land dominant, and amount and area of soil loss are great too. This study demonstrated that abandoned fields experienced intense soil erosion, which is in agreement with previous studies (De Santisteban et al., 2006; Lesschen et al., 2007). In terms of soil erosion modulus, abandoned fields should be conserved firstly. The results in this study also implied that flat crop field, forest, and other land covers had the ability reducing soil erosion. The other land covers consisting of water bodies and urban area could decrease soil erosion as Wu et al. (2007) demonstrated. However, other lands are not for crop productivity, area of these land covers cannot be expanded on the bases of the land resist to soil erosion. In addition, Hou et al.(2007) reported that cultivated land located at the foot of hills and paddy fields were areas for sediment deposition. In terms of percentage of soil loss, soil conservation should start on slope fields first, and then on shrubs, and abandoned fields.

；

5.5 Strategies of Changing Land Covers for Soil Conservation Based on soil erosion modulus on various land covers, the upper limits of land area for different covers could be calculated by regression equation 2 as the soil erosion modulus was set as 500 t/ km2·a. Owning to small area of abandoned fields and low soil erosion modulus of forested lands in the region, the calculated values was actually for slope fields. The computed upper limit of 7% for hilly crop land indicated that 29% slope fields should be converted to other uses according to the average percentage of slope fields in all the watersheds. In addition, on 5% abandoned fields and 11% other land covers, the soil loss rates were greater 500 t/ km2·a too. The shrub lands are not needed to be converted but conservation measures are required to protect soil from eroding. The economic and ecological benefits of abandoned fields are not high, therefore, the cover should be converted. Overall, for most watersheds, about 34% of

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land cover should be converted, most of which are occupied by slope fields and abandoned fields. When the slope fields and abandoned fields are planning to be converted to other uses, the conversion strategies should consider soil erosion. The slope fields should be converted to flat crop fields, shrubs, or other land uses. Flat crop fields along with the remained 7% slope fields already accounted for 42% of the total watershed area, implying this type of land cover does not need any changes. The percentage of forests could be increased to 45% through afforestation and planting fruit trees (Table 2). After land cover changes, the soil erosion modulus could be changed to 1324 t/ km2·a. However, the soil erosion modulus on 7% the slope fields and 45% shrubs were still greater than 500t/km2·a. The soil erosion on the left 7% slope fields could be reduced by planting vegetation fence, applying tillage practices, or, construction of some engineering structures (e.g. small ditches). In addition, the soil erosion modulus could be gradually declined as forests grow to mature.

6 Summary and Conclusion Quantitative analysis of effects of land covers and erosion loss on soil erosion modulus, percentage of soil loss, and area percentage of soil loss at watershed scale can supply information to policy-makers for soil conservation. Analysis of inventory data with statistical methods for soil erosion is a good approach to study erosion in multiple watersheds, and the results could be a good representation of the erosion for large area. This research studied features of soil erosion in response to various land use and land cover, as well as divergent erosion intensity by constructing regression models among soil erosion modulus, area percentage of the land covers in 40 watersheds. Results showed that for the four classes of erosion intensity of insignificant erosion, light, moderate, and intense and above erosions, the soil erosion modulus were -397.78t/km2·a, 1639.95t/km2·a, 5000.00t/km2·a, and 9119.12t/km2·a, percentage of soil loss were -7%, 6%, 34%, and 67%, and the percentages in all watersheds varied at a range of -3% - -23%, 0% - 21%, 8% - 100%, and 11% - 98%. In 80% watersheds, soil loss is mainly from intense erosion, and for the remains, it is from moderate erosion. For flat and slope fields, forests, shrubs, abandoned fields, and other land covers, the soil erosion modulus were 458.27t/km2·a, 6410.8t/km2·a, -2083.89t/km2·a, 2668.43t/km2·a, 6639.33 t/km2·a, and -759.6 t/km2·a, percentage of soil loss were -6%, 89%, -4%, 11%, 12%, and -3%, and area percentage of erosion were 1%, 72%, 0%, 18%, 9%, and 0%. The slope fields were the main source of soil loss, and then followed by abandoned fields, and shrubs. In contrast, other land covers could conserve soil from eroding. Changing land covers, reducing area of slope fields to 7% and of abandoned fields to 0%, were proposed as a management strategy for soil conservation in the watersheds. As completion of the conversation, percentages of the land covers should be 35%, 7%, 5%, 45%, 0%, and 8% for flat and slope fields, forests, shrubs, abandoned, and other land covers. As forests growing to mature, the tillage practices, reforestation, and constructing soil conservation measures could reduce soil erosion.

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Acknowledgements This study was sponsored by the Open Projects Foundation of Key Laboratory of Soil and Water Conservation & Desertification Combat Beijing Forest University ,Ministry of Education, “Modeling for soil erosion control process on “Changzhi” Project” (201006); the National Basic Research Program of China (No. 2007CB407207-1); the Fundamental Research Funds for the Central Universities(BLYX200927); Key Projects in the National Science and Technology Pillar Program in the 11th Five Year Plan Period(2009BADC6B06), the Governmental Public Industry research Special Funds for Projects (200901049).

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Spatial and Temporal Variability of Annual Precipitation during 1958-2007 in Loess Plateau, China Rui Guo, Fengmin Li, Wenying He, Sen Yang, and Guojun Sun∗ Ministry of Education Key Laboratory of Arid and Grassland Ecology, Lanzhou University, China 730000 [email protected]

Abstract. The non-parametric Mann-Kendall method and Sliding t-test were applied in this study to analyze the spatial and temporal patterns of trends of the precipitation in the Loess Plateau from 1958 to 2007. The main trends of the precipitation in this region is downward, only four upward time series have not reach the significant level. Most of the abrupt changes happened in northeast of Loess Plateau during 1980s and 1990s. Furthermore, the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau. Keywords: Trend analysis, Abrupt change, Mann-Kendall method, Sliding t-test method.

1 Introduction Loess Plateau is located in arid and semi-arid area of China. It faces too many problems such as low vegetation cover, water scarcity and severe soil erosion and so on. Lots of the problems were affected by the precipitation. The precipitation is also one of the major factors limited agricultural and forestry productivity. Fierce human activities increase the possibility of abrupt changes in climate[1]. It is a meaningful work to find out the regulation of the precipitation in Loess Plateau. Mann-Kendall trend test is a non-parametric, rand-based method for assessing the significance of trends in time-series data such as water quality, streamflow, temperature, and precipitation[2]. Not only can examine the trends in time-series decline or increase, but also reflect the degree of changes, Mann-Kendall trend test could be a very good description of the characteristics of the time-series trend. Now, the Mann-Kendall trend test has been widely used and tested as an effective method to evaluate a presence of a statistically significant trend in hydrological and climatological time series[2-4]. In the study, we use this method to test trends on precipitation in Loess Plateau from 1958 to 2007. The Sliding t-test[5] was used to detect the abrupt changes in the study. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 551–560, 2011. © IFIP International Federation for Information Processing 2011

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The objectives of this study are (1) to reveal the spatial and temporal patterns of trends of the precipitation in the Loess Plateau; (2) to analyze the abrupt changes of the precipitation in the Loess Plateau from 1958 to 2007.

2 Study Area and Data Processing 2.1 Loess Plateau The Loess Plateau is a subsystem of mutually interlinked and feedback system of northwest Gobi desert, middle Loess plateau and east alluvial plain[6]. It is located in the middle reaches of the Yellow River and extends 7 latitudes (35-41°N) and 13 longitudes (102-114°E), with total area of more than 630,000km2 and a population of 86 million. The plateau area is 1000-1600 m above sea level and surface is covered by an average 100-meter thickness of loess-paleosol layers. The Loess Plateau belongs to the continental monsoon region with annual average temperature of 4.3 in northwest and 14.3 in southeast. Annual precipitation ranges from 300mm in northwest to 700mm in southwest[7]. There is much sunshine in the Loess Plateau. The soil erosion rate on the Loess Plateau of China is among the highest in the world. Loess Plateau contains arid and semi-arid regions; the eco-environment is fragile.

℃

℃

2.2 Precipitation Data The precipitation data were downloaded from China Meteorological Data Sharing Service System. The time series of annual precipitation records at 42 meteorological stations were used in this study from 1958 to 2007. The stations in the Loess Plateau were shown in Fig.1.

Fig. 1. The location of the Loess Plateau in China. The locations of the Meteorological stations are shown by the black triangle.

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2.3 Methods 2.3.1 Mann-Kendall Method The non-parametric Mann-Kendall method was used to assess the significance of a trend, and originated from Mann (1945)[8] and rephrased by Kendall (1948)[9]. The test statistic is calculated as follows: n −1

S =∑ i =1

n

∑ sgn( x − x ) . i

j =i +1

(1)

j

Where n is the length of the data set, and x is the data point at time i and j, and

⎧ 1 for θ > 0 ⎪ sgn(θ ) = ⎨ 0 for θ = 0 . ⎪−1 for θ < 0 ⎩

(2)

For independent, identically distributed random variables with on tied data values: E(S) = 0 .

Var ( S ) =

(3)

n(n − 1)(2n + 5) =σ2 . 18

(4)

When some data values are tied, the correction to Var(S) is: n

Var ( S ) =

n( n − 1)(2n + 5) − ∑ ti (i)(i − 1)(2i + 5) i =1

.

18

(5)

Where ti is the number of ties of extent i. The standardized test statistic Z is computed by:

⎧ S −1 for S > 0 ⎪ σ ⎪ Z =⎨0 for S = 0 ⎪ S +1 ⎪ for S < 0 ⎩ σ

.

(6)

The standardized MK statistic Z follows a standard normal distribution with mean of zero and variance of one under the null hypothesis of no trend. A positive Z value indicates an upward trend, whereas a negative one indicates a downward trend. The p

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value (probability value p) of the MK statistic S of sample data can be estimated using the normal cumulative distribution function:

p = 0.5 − φ ( Z ) .

(7)

where

1 φ( Z ) = 2π

∫

Z

0

−

t2 2

e dt .

(8)

If the p value is small enough, the trend is quite unlikely to be caused by random sampling. At the significance level of 0.05, if p ≤ 0.05, then the existing trend is assessed to be statistically significant [10, 11]. 2.3.2 Sliding t-Test Method One moment is set to be a reference point in time series “x” with a sample size “n”. The two subsequences before and after the reference point were defined to be x1 and x2.

t=

X1 − X 2 . 1 1 − s⋅ n1 n2

(9)

Where

s=

n1s12 + n2 s2 2 . n1 + n2 − 2

(10)

n1, n2 ——the sample size of x1 and x2, separately.

X 1 , X 2 ——the average of x1 and x2, separately. s12,s22——the variance of x1 and x2, separately. If |ti|
3 Results and Discussion 3.1 The Spatial Structure of the Precipitation The linear regression was used to detect the relationship between the average rainfall for this 50 years and longitude, latitude, elevation (Fig.2). The results presented that the

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average precipitation was linear positively related to the longitude, but it is not significant. The average precipitation was not the linear relation with the latitude or the elevation. It was suggested that the spatial differentiation of the precipitation was not significant on the Loess Plateau from1958 to 2007.

Fig. 2. Relationship between the average rainfall and longitude, latitude, elevation in Loess Plateau, the linear models and R2 also gave in the figure

3.2 Trend Analysis and Abrupt Changes The non-parametric Mann-Kendall method was used to assess the significance of the trend of precipitation. By this method the significant trends were detected for 42 time series of annual precipitation for different stations in the Loess Plateau (Fig. 3). There were only 7 among 42 stations showed a significant trend (p=0.05), the remaining 35 stations showed no significant trend. Moreover, all these 7 stations with significant trend (p=0.05) displayed a downward trend. The four stations with an upward trend showed in Fig.3 were not assessed to be statistically significant. In other words, the standardized MK statistic Z of these four stations is positive, but did not reach significant levels. The standardized MK statistic Z of the remaining 31 stations with no significant trend is negative. It can be easily found from the distribution of the trends for the annual precipitation that no trend and upward trend were mainly in the western portion of Loess Plateau. The downward trend concentrated in the eastern portion of central Loess Plateau. The results also indicated that the precipitation possesses the longitude zonality. This result was similar as Q. Liu et al. [12]that studied the precipitation during 1961-2006 in Yellow River Basin. The precipitation changed

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Fig. 3. The trends in annual precipitation during 1958-2007 detected by the Mann-Kendall method in Loess Plateau

Fig. 4. The abrupt change tested by the Sliding t-test for annual precipitation series in Youyu station from 1962 to 2002 (n1=n2=5)

obviously from west to northeast. This situation may be due to the monsoon, as the precipitation was sensitive to the climatic change. And the influenced by human activities might another reason. The Sliding t-test method was used to test the abrupt changes. The Youyu station (112°27′E, 40°00′N) was selected as an example to demonstrate the abrupt change

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happening in the time series of annual precipitation (n1=n2=5). It revealed that the abrupt change occurred in 1987, 1992 and 2001 (Fig.4). The t-test in 1987 reached the significant level (ti < -t0.05) which revealed the abrupt change was from less to more. In 1992, ti > t0.05, i.e. the abrupt change was from more to less. The situation in 2001 was similar to that in 1987. Because the choice of n1 and n2 are man-made, the results may drift. In order to avoid this situation, the n should be chose combined with specific needs, and constantly changes, so as to enhance the reliability of the results. The n1=n2=5, n1=n2=7, n1=n2=10, n1=n2=11 and n1=n2=12 were chose in the study (Tab.1). By comparing, n1=n2=10 had been chose to analyze abrupt change. The time and frequency of the abrupt changes showed in Fig.5. There were 31 abrupt changes happening in 18 meteorological stations. Two abrupt changes took place in the fallowing 5 stations, Minhe (1979-decreasing change, 1991-increasing change), Huajialing (1985-decreasing change, 1986-decreasing change), Huanxian (1968-decreasing change, 1970-decreasing change), Suide (1995decreasing change, 1996-decreasing change), Lishi (1967-decreasing change, 1990decreasing change). Three abrupt changes took place in Xiji (1967-decreasing change, 1968-decreasing change, 1992-decreasing change). Four abrupt changes took place in the fallowing 2 stations, Jiexiu (1989, 1990, 1991 and 1992-decreasing change), Wutaishan (1988, 1989, 1992 and 1996-decreasing change). 12 stations among these 18 stations locate in the northeast of Loess Plateau. 20 times of the 31 abrupt changes occurred in the northeast. All of this indicated that the northeast of Loess Plateau was sensitive to the climate change.

Fig. 5. The time and frequency of the abrupt change detected by the Sliding t-test (n1=n2=10) in the meteorological stations of Loess Plateau. The year that the abrupt change happened has been simplified in two figures, e.g., 67 for 1967; ** indicated p<0.01.

Yuzhong

Xifeng

Linxia

Kongtong

68*

00*

85*,86*

85*,86*,87*

92*

Lintao

Huajialing

68*

79*

76*

67*

Linfen

Yangcheng

84*,85**,86*,90*

77*

Xiji

Tongxin

Haiyuan

Guyuan

Yanchi

Menyuan

Xining

Minhe

Guide

93*,94**

Jiexiu

93**,94*,95*,00* 89*,90*,91*,92* 88*,89*,90*,91** 85*,88**,89*,90* ,93* *,91*,92*,93*,94 *

Yulin

96*

Suide

Yushe

Yan'an

Luochuan

Xixian

69*

90*

Hengshan

90*

Yushe

Yangquan

96*

Lishi

Taiyuan

96*

67*,90*

Yuanping

64*,67*

Changwu

72**

Sanmenxia

96*

78*,79*

Wuzhai

Weixian

Jingtai

Huanxian

Jingyuan

Stations

Xingxian

n1=n2=12

88*,89*,92*,96* 88*,89*,90*,92*, 87*,88**,89**,90 95*,96* **,91*,92*,93*,9 4*

n1=n2=11

80*

96*

n1=n2=10

Sliding t-test

Wutaishan

92*

n1=n2=7

87*

87*,92*,01*

n1=n2=5

Hequ

Datong

Youyu

Stations

68*

68*

79*

02*

70**

80*,85*

66*

n1=n2=5

67*,68*,95*

79*

74*

92*

68*

n1=n2=7

67*,68**,92*

67*

79*,91*

90*

95*,96*

90*

92*

68*,70*

n1=n2=10

Sliding t-test

68*

79*,91*

94*,95*,96*

68*,69*,88*

92*,95*

68*,69*,71*

n1=n2=11

Table 1. The time of the abrupt change detected by the Sliding t-test. (* p<0.05. ** p<0.01. The year that the abrupt change happened has been simplified in two figures, e.g. , 67 for 1967).

69*,70*,94*,95*

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88*,94*,95*

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Fig. 6. The times of abrupt change detected by the Sliding t-test (n1=n2=10) in different decades

Moreover, the times of abrupt changes happened in different decades showed in Fig.6. It can be found from the chart that the times of abrupt change increase with the time except from 1971 to 1980. Only one station which located in the west of Loess Plateau happened abrupt change from 1971 to 1980. The abrupt change mainly happened in west of Loess Plateau from 1961 to 1970. However, during 1981-1990 and 1991-2000, the abrupt change was mainly happened in the northwest of Loess Plateau. That is the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau. Moreover, there were 6 stations among 12 which locate in the northeast happened abrupt change in 1996. It indicated that the northeast of Loess Plateau became more and more sensitive to the climate change since 1980s. The reasons that affected the precipitation in the Loess Plateau need further study on the interaction between the climate change and human activities. The results in this study are significance for the further studies and forecast of climate in Loess Plateau, and also offer the basis for decision-makers.

4 Conclusions In this study, the Mann-Kendall method and Sliding t-test were used to analyze the precipitation from 1958 to 2007 in Loess Plateau. The results presented as follows: (1) The relationship between precipitation and longitude/latitude/elevation is not linear. The precipitation didn’t have clear trend with the longitude/ latitude/elevation increasing. (2) The results of Mann-Kendall method indicated that only 7 stations showed a significant trend, the other 35 stations have no significant trend. Only 4 stations have positive Z values but didn’t reach the 95% significant level. (3) The results of Sliding t-test showed that there were 31 abrupt changes happening in 18 meteorological stations. Most of the abrupt changes happened in the northeast of Loess Plateau. Moreover, the abrupt changes happened in the northeast

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concentrated during 1980s and 1990s. And the abrupt changes happened in the northeast portion of the Loess Plateau were relatively later than those in the west portion of the Loess Plateau. (4) The northeast of Loess Plateau was sensitive to climate change recent years, as most of the abrupt changes occurred at here during 1980s and 1990s. More attention should be paid to this region, and the changes in land cover/use could be considered in further studies.

Acknowledgments This work is financial supported by National Key Basic Research Program (973) project (2007CB106804) and the Construction of an Information Platform/module in Eco-agricultural Assessment and Management (EAM) (2010DFA31450). The authors are grateful to the anonymous referees for their careful corrections and constructive comments on the manuscript. The authors thank the China Meteorological Data Sharing Service System for offering the precipitation data.

References 1. Alley, R.B., Marotzke, J., Nordhaus, W.D., et al.: Abrupt climate change. Science 229, 2005–2010 (2003) 2. Yue, S., Pilon, P., Cavadias, G.: Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. Journal of Hydrology 259, 254–271 (2002) 3. Douglas, E.M., Vogel, R.M., Kroll, C.N.: Trends in floods and low flows in the United States: impact of spatial correlation. Journal of Hydrology 240, 90–105 (2000) 4. Birsan, M.V., Molnar, P., Burlamdo, P., Pfaundler, M.: Streamflow trends in Switzerland. Journal of Hydrology 314, 312–329 (2005) 5. Wei, F.: The statistics and prediction of climate. China Meteorological Press (1999) (in Chinese) 6. He Xiubin, L.Z., Hao, M., Tang, K., Zheng, F.: Down-scale analysis for water scarcity in response to soil–water conservation on Loess Plateau of China. Agriculture, Ecosystems and Environment 94, 355–361 (2003) 7. Jie We, J.Z., Tian, J., He, X., Tang, K.: Decoupling soil erosion and human activities on the Chinese Loess Plateau in the 20th century. Catena 68, 10–15 (2006) 8. Mann, H.B.: Non-parametric test against trend. Econometrika 13, 245–259 (1945) 9. Kendall, M.G.: Rank Correlation Methods. Hafner, New York (1948) 10. Douglas, E.M., Vogel, R.M., Kroll, C.N.: Trends in floods and low flows in the United States: impact of spatial correlation. Journal of Hydrology 240, 90–105 (2000) 11. Bouza-Deaño, R., Ternero-Rodríguez, M., Fernández-Espinosa, A.J.: Trend study and assessment of surface water quality in the Ebro River (Spain). Journal of Hydrology 361, 227–239 (2008) 12. Liu, Q., Yang, Z.F., Cui, B.S.: Spatial and temporal variability of annual precipitation during 1961-2006 in Yellow River Basin, China. Journal of Hydrology 361, 330–338 (2008)

Spatial Statistical Analysis in Cow Disease Monitoring Based on GIS Lin Li1, Yong Yang2,*, Hongbin Wang3, Jing Dong1, Yujun Zhao1, and Jianbin He1 1 School of Animal Husbandry and Veterinary Medicine, Shenyang Agricultural University, 110866 Shenyang, P. R. China [email protected] 2 School of Information and Electrical Engineering, Shenyang Agricultural University, 110866 Shenyang, P. R. China [email protected] 3 School of anima Medicine, Northeast Agricultural University, 150030 Harbin, P. R. China

Abstract. In this paper, we analyze cow endemic fluorosis spatial distribution characteristics in the target district based on combination of spatial statistical analysis and GIS. This disease geographical distribution relates closely to the environmental factors of habitats and the correct analysis of the spatial distribution of the disease for monitoring and prevention plays an important role. However, the amount of monitoring spots is smaller and the monitored data are very limited. How to use the limited data to show the general spatial distribution of the disease is a key issue in building the efficient spatial monitoring method. We use GIS application software ArcGIS9.1 to overcome the lack of data of sampling sites and establish a prediction model in estimation of the disease distribution. Comparing with the true disease distribution, the prediction model has a well coincidence and is feasible and provides a fundamental application for other study on space-related cow diseases. Keywords: Cow disease, GIS, Spatial statistic analysis.

1 Introduction The use of geographical information systems (GIS) and spatial statistical analysis in public health is now widespread. GIS is powerful automated system for the storage, retrieval, analysis, and display of spatial data. The system offers new and expanding opportunities for epidemiology because it allows an informed user to choose between options when geographic distributions are part of the problem. Even when used minimally, this system allows a spatial perspective on disease. Used to their optimum level, as tools for analysis and decision making, they are indeed a new information management vehicle with a rich potential for animal epidemiological research. Epidemiologists have traditionally used maps when analyzing associations between location, environment, and the disease (Busgeeth et al., 2004). GIS is particularly well suited for studying these associations because of its spatial analysis and display *

Corresponding author.

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capabilities. Recently GIS has been used in the surveillance and monitoring of vectorborne diseases (Glass GE et al., 1995; Beck et al., 1994; Richards et al.,1993; Clarke et al., 1991), water borne diseases (Braddock et al.,1994), in environmental health (Barnes, 1994; Wartenberg et al., 1993; Wartenberg et al.,1992), and the analysis of disease policy and planning (Marilyn et al.,2004).Spatial analysis function of GIS can be widened and strengthened by using spatial statistical analysis, allowing for the deeper exploration, analysis, manipulation and interpretation of spatial pattern and spatial correlation of the animal disease. Cow endemic fluorosis is a disease of Signs of dental discoloration, difficulty in mastication, bony exostosis and debility. It can cause lameness, dental lesions and illthrift in an extensive cow. The herd was lame and the disease forced the culling of large numbers of cows in northern Australia. Cow endemic fluorosis is biogeochemical disease. Its geographical distribution is related closely to the environmental factors of habitats and has some spatial characteristics, and therefore the correct analysis of the spatial distribution of cow endemic fluorosis for monitoring and the prevention and control of endemic fluorosis has a very important role. However, the application of classic statistical methods in some areas is very difficult because of the pastoral nomadic context. The high mobility of livestock and the lack of enough suitable sampling for the some of the difficulties in monitoring currently make it nearly impossible to apply rigorous random sampling methods. It is thus necessary to develop an alternative sampling method, which could overcome the lack of sampling and meet the requirements for randomness.

2 Materials and Methods In this paper, we analyzed the cow endemic fluorosis spatial distribution characteristics in the target district A (due to the secret of epidemic data we call it district A) based on the established GIS of the cow endemic fluorosis in this district in combination of spatial statistical analysis and GIS. We established a prediction model to estimate the endemic fluorosis distribution based on the spatial characteristic of the density of cow endemic fluorosis. A two-stage random sampling was used. It consisted in generating sampling sites at random and then drawing the required number of cow from the nearest herd to each point, and this, for practical reasons, within a specified radius. In this sampling method, the primary unit was defined as a settlement, watering point or grazing area where animals were expected to be found. Due to a lack of an exhaustive list of these locations in some areas and because of the high mobility of pastoral herds, the unpredictability of their movement, the classical random selection of sites was not feasible. Therefore, there was the need to develop an alternative method that allows random selection of the first sampling units. GIS computer application software (ArcGIS) was used.

3 Cow Endemic Fluorosis Data Sampling Based on GIS GIS computer application software (ArcGIS) was used. ArcGIS Spatial analyst is an extension to ArcGIS Desktop that provides powerful tools for comprehensive, raster-based spatial modeling and analysis. With its extension to generate random

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coordinates, ArcGIS is able to generate at random the required number of sites within the area where sampling needs to take place, be it at zone, country, region or even district level, simultaneously allowing the application of weight factors to different sub-units of the surveyed area. Each site is identified by its longitude and latitude coordinates. In reality, a generated site is materialized only as a point on the map and therefore a fixed radius is defined around each point in order to determine a geographical area within which the sampling of cow endemic fluorosis is carried out. The determination of length of the radius takes into consideration the cow endemic fluorosis density. A radius below 5 km in a dry area may result in too many sampling sites with no animals, whereas in a densely populated area around permanent watering points and grazing areas, a relatively shorter radius suffices. A surplus of the total number of sites is generated at random to replace target sites that could not be accessed due to natural obstacles or sites with no animals within the defined circle.

4 Spatial Cluster Analysis of Cow Endemic Fluorosis Density Spatial cluster analysis plays an important role in quantifying geographic variation patterns. It is commonly used in disease surveillance, spatial epidemiology, population genetics, landscape ecology, crime analysis and many other fields, but the underlying principles are the same. Spatial clustering analysis is the main research field of spatial data mining. We analysis cow endemic fluorosis data spatial cluster using High/Low Clustering tool in ArcGIS which can measures the degree of clustering for either high values or low values. The tool calculates the value of Z score for a given input feature class. A z-score is a measure of the divergence of an individual experimental result from the most probable result, the mean. Z is expressed in terms of the number of standard deviations from the mean value.

where: x is ExperimentalValue, μ is Mean, and σ is StandardDeviation. This z-value or z score expresses the divergence of the experimental result x from the most probable result μ as a number of standard deviations σ The larger the value of z, the less probable the experimental result is due to chance. For this tool, the null hypothesis states that the values associated with features are randomly distributed. The higher (or lower) the Z score, the stronger the intensity of the clustering. A Z score near zero indicates no apparent clustering within the study area. A positive Z score indicates clustering of high values. A negative Z score indicates clustering of low values.

5 Spatial Autocorrelation Analysis of Cow Endemic Fluorosis Density Spatial Autocorrelation is correlation of a variable with itself through space. If there is any systematic pattern in the spatial distribution of a variable, it is said to be spatially

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autocorrelation. If nearby or neighboring areas are more alike, this is positive spatial autocorrelation, Negative autocorrelation describes patterns in which neighboring areas are unlike. Random patterns exhibit no spatial autocorrelation. Spatial autocorrelation is important, because most statistics are based on the assumption that the values of observations in each sample are independent of one another. Positive spatial autocorrelation may violate this, if the samples were taken from nearby areas .Goals of spatial autocorrelation Measure is the strength of spatial autocorrelation in a map and test the assumption of independence or randomness. Spatial autocorrelation is, conceptually as well as empirically, the two-dimensional equivalent of redundancy. It measures the extent to which the occurrence of an event in an area unit constrains, or makes more probable, the occurrence of an event in a neighboring area unit. We analysis cow endemic fluorosis data Spatial Autocorrelation using Global Moran's I tool in GEODA. Moran's I is a measure of spatial autocorrelation developed by Patrick A.P. Moran. Moran's I is defined as:

n∑∑ wij ( xi − x )(x j − x ) n

I=

n

i =1 j =1 n

n

n

∑∑ w ∑ (x i =1 j =1

ij

i =1

i

− x)

2

where: N is the number of spatial units indexed by i and j, x is the variable of interest, is the mean of x, and wji is a matrix of spatial weights. Negative (positive) values indicate negative (positive) spatial autocorrelation. Values range from −1 (indicating perfect dispersion) to +1 (perfect correlation). A zero values indicate a random spatial pattern. For statistical hypothesis testing, Moran's I values can be transformed to Z-scores in which values greater than 1.96 or smaller than −1.96 indicate spatial autocorrelation that is significant at the 5% level.

6 Results The results of cow endemic fluorosis density spatial cluster analysis showed that the density of dairy cattle endemic fluorosis distribution in space with a high concentration of the Z Score = 5.5, P < 0.01, which means that there is a spatial clustering model. The results of Cow endemic fluorosis density spatial Autocorrelation analysis showed Moran's I = 0.4216, which means there is an intensely spatial autocorrelation.

Fig. 1. Superimposing map of predict and the true distribution of cow endemic fluorosis

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Based on the spatial characteristic of the density of cow endemic fluorosis we established a prediction model to estimate the endemic fluorosis distribution by Ordinary Kriging tool in ArcGIS. Superimposing map of predict map and Distribution of cow endemic fluorosis epidemic focus in district A (Fig1). Comparing with the true distribution, the prediction model has a well coincidence and is feasible to the application.

7 Conclusion Applied Spatial Statistics used in conjunction with geographic information systems (GIS) provide an efficient tool for the surveillance of diseases. In the veterinary epidemiology, the advantage of mapping the locations of farms and other facilities with animals is obvious. In an outbreak of a disease it could make the management of the situation easier, and it could also provide a tool to evaluate different strategies to prevent the spread of infectious diseases. GIS have tremendously enhanced ecological epizootiology, the study of diseases in relation to their ecosystems. They have found increasing application for surveillance and monitoring studies, identification and location of environmental risk factors as well as disease prediction, disease policy planning, prevention and control. In this article we discuss the application of GIS to veterinary and medical research and the monitoring method of cow endemic fluorosis based on GIS and spatial statistical analysis. The method using a GIS tool facilitates is implemented significantly in the cow endemic fluorosis monitoring and investigation, and the space statistics - related prediction model provides a fundamental use for other study on space-related animal diseases.

Acknowledgement Funding for this research was in part provided by china postdoctoral science foundation (NO.20090461189), the postdoctoral fund of Shenyang Agricultural University, Research fund for young teachers of Shenyang Agricultural University, Dr. Start Fund of Liaoning Province, P. R. China. The authors are grateful to the Shenyang Agricultural University for providing conditions with finishing this research.

References 1. Wartenberg, D.: Screening for lead exposure using a geographic information system. J. Environ. Res. Dec. 59, 310–317 (1992) 2. Glass, G.E., Schwartz, B.S., Morgan III, J.M., Johnson, D.T., Noy, P.M., Israel, E.: Environmental risk factors for Lyme disease identified with geographic information systems. Am J. Public Health 85, 944–948 (1995) 3. Beck, L.R., Rodrigues, M.H., Dister, S.W., Rodrigues, A.D., Rejmankova, E., Ulloa, A., et al.: Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am. J. Trop. Med. Hyg. 51, 271–280 (1994) 4. Jubb, T.F., Annand, T.E., Main, D.C., Murphy, G.M.: Phosphorus supplements and fluorosis in cattle–a northern Australian experience. J. Aust. Vet. 70, 379–383 (1993)

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5. Richards Jr., F.O.: Use of geographic information systems in control programs for onchocerciasis in Guatemala. J. Bull. Pan. Am. Health Organ. 27, 52–55 (1993) 6. Clarke, K.C., Osleeb, J.R., Sherry, J.M., Meert, J.P., Larsson, R.W.: The use of remote sensing and geographic information systems in UNICEF’s dracunculiasis (Guinea worm) eradication effort. J. Prev. Vet. Med. 11, 229–235 (1991) 7. Braddock, M., Lapidus, G., Cromley, E., Cromley, R., Burke, G., Branco, L.: Using a geographic information system to understand child pedestrian injury. Am. J. Public Health. 84, 1158–1161 (1994) 8. Queiroz, J.W., Dias, G.H., Nobre, M.L., et al.: Geographic Information Systems and Applied Spatial Statistics Are Efficient Tools to Study Hansen’s Disease (Leprosy) and to Determine Areas of Greater Risk of Disease. J. American Journal of Tropical Medicine and Hygiene 82, 306–314 (2010) 9. Barnes, S., Peck, A.: Mapping the future of health care: GIS applications in Health care analysis. J. Geographic Information Systems 4, 31–33 (1994) 10. Wartenberg, D., Greenberg, M., Lathrop, R.: Identification and characterization of populations living near high-voltage transmission lines: a pilot study. J. Environ. Health Perspect. 101, 626–632 (1993) 11. Busgeeth, K.: The use of a spatial information system in the management of HIV/AIDS in South Africa, J. International Journal of Health Geographics 3, 13–15 (2004)

Study for Organic Soybean Production Information Traceability System Based on Web Xi Wang, Chun Wang, Xinzhong Wang, and Weidong Zhuang College of Engineering, Heilongjiang Bayi Agrucultural University, Post Code: 163319, Daqing, China [email protected]

Abstract. The paper analyzes the mode of organic soybean production supply chain and identifies the information needed to be recorded in each step of producing organic soybean through surveying the actual situation of organic soybean on the Red Star Farm combined with the mode of operation among enterprises, then it ensures integrity and consistency of information flow chain. At the same time, it establishes organic soybean production information traceability system, which adopts B/S architecture, ASP dynamic web technology and SQL Server 2005 database. By the system, enterprises can quickly inquire about the information of cultivation and processing of raw materials as well as the situation of products in time. Meanwhile, consumers can also inquire about the information of products by means of Internet quickly and easily, which enhances the transparency of production chain and ensures food safety. Keywords: soybean, traceability system, internet, agriculture information.

1 Introduction More and more people in our country even in the world have paid aettention to the topic of food quality since the 90s of last century, along with a series of mad cow disease, hoof-and-mouth disease and bird influenza which made people feel fear greatly and gave destructive strike on the related industries like export obstacle, sales decline. China is a major exporter that has more exports to the European Union, the United States and other countries after it has joined WTO, it cries out for implementing product tracking and tracing in order to meet the food quality requirements of developed country, serve the export for better, avoid technical barriers, improve the quality of agricultural products and enhance international competitiveness. Therefore, it is very important to build products safety traceability system which can promote information security system, ensure the safety of consumable and enhance the competitiveness of our country[1]. Taking organic soybean products which are produced by the Great Northern Wilderness People Organic Food Co., Ltd for example, it makes organic soybean production information traceability system true based on researching the process of organic soybean production and processing and analysis of tracing basic framework. The system can record information that related to product quality and safety while it D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 567–572, 2011. © IFIP International Federation for Information Processing 2011

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produces and manages, which not only can supply the products information of quality and safety for consumers, but also quality control operations for business.

2 Analysis of Organic Soybean Production Supply Chain The goal of analysis of organic soybean production supply chain is to enable producers to sell their products conveniently, making good quality and prices true and increasing revenue, enable administrators to understand the quality of products accurately in time, enable consumers to know product quality traceability and related information. It is meaningful to have the function of recording, transmission and management, which is good to improve products quality safety, increase the transparency of the whole process information and build products traceability system[2]. 2.1 The Meaning of Soybean Production Supply Chain Soybean production supply chain refers to do some physical economic activities of soybean and its relevant information from producers to consumers in order to meet consumers’ demand. It is the chain system that is composed of soybean growers enterprises and so on. Specifically, it is composed of seeding, fertilizer, stalks and labour control, mechanism management, harvest, processing and packaging, and making the soybean appreciated in the process. 2.2 The Basic Structure of Organic Soybean Production Supply Chain Organic soybean production supply chain includes organic soybean plant, organic soybean processing, organic soybean packaging and so on. Every step is composed of different participants, among whom the constant distribution process makes organic soybean come true from planting, finished products to being bought by consumers at last, which makes up physical flow. In the process of distribution, participants from different steps send information to each other, which makes up information flow that has the characteristic of tracking and traceability. material flow farm

enterprise

consumer

Information flow Fig. 1. Structural model of supply chain

Participants from organic soybean production supply chain. (1)Organic soybean plant farmers: the process of production supply chain plant, which supply materials for organic soybean. (2)Organic soybean processing enterprises: the process of supply chains processing, packing and marketing, especially processing.

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2.3 Organization Modes of Organic Soybean Production Supply Chain Organization modes of organic soybean production supply are composed of participants that interact with organic soybean production and communicate with each other, moreover enable to finish its communication function. As participants play different parts in the organic soybean production supply chain, organization modes of organic soybean production supply chain become different. It analyses organic soybean organization modes according to Heilongjiang reclamation area based on agricultural standardized production, laying stress on green organic food, making processing industry as centre shaft, aiming for improving core competitiveness of Great Northern Wilderness Group. Farmer or base >processing enterprises >exports or marketing for wholesalers, retailers, processing agricultural products businesses export products which have been processed from our own base and farmer, or sell to wholesalers, or set windows for selling to consumer directly. We must keep the information chain unblocked and integrated in order to achieve the need of organic soybean traceability. That means participants must keep detailed records about planting and processing, which can supply some information for quality problem and give consumers useful information. Participants of distribution step are responsible for recording the source of goods so that bad products can be recalled easily.

一

一

(1)Growing information: terrains numbers information, planting time information, planter information, the proposed crops information, soil information, seeding information, management information and harvest information. (2)Processing information: crop source, time of arriving to factory, peeling processing. (3)Packing information: traceability number, worktable, quality inspector, packing time. (4)Products information: product name, production date, product strains, product specification, product categories, etc.

3 System Design 3.1 Traceability System Function Traceability system does not provide quality assurance, because it is only good at tracking, without improving the quality of products. It can define responsibilities of each participant through recording information, then reducing the incidence of counterfeit and improving the quality of products. Traceability system combined with management system methods can paly a stronger part, such as GMP, HACCP and so on. These quality management system has their own record systems, which provide a good foundation for enterprise interior traceability[3]. 3.2 Development Platform Under the software engineering ideas guidance, the study collects a great number of planting and producing information and designs two functional modules which

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include the background information management system and the foreground information enquiry system combined with the basic theory and methods of agriculture and computer subject. The background information management system is responsible for recording, modifying and deleting information, the foreground information enquiry system is responsible for tracking information of quality, planting and growing. It establishes a network of organic soybean products traceability system through using ASP object-oriented dynamic page, SQL serve 2000 database and B/S system structure, the overall design framework as in figure 2.

Fig. 2. Architecture of the B/S

3.3 System Structure Design The modern organic soybean traceability system has built the perfect management system, including crop seeds, fertilizer information, artificial management, machinery management, diseases management, pests management, harvest management and so on, it makes electronic filing true from planting to harvest and from producing to processing. The system which inquire about planting information through management system focus on the process of planting and processing corps.

Fig. 3. The overall structure of the system function

Enterprises collect traceability information in detail, which can go back to the production process of each product. It can achieve functions by the query numbering system.

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(1)Farm can inquire about all the planting situation using terrains code. (2)Processor can inquire about processing chain information through products code, and then goes back to the production process of each product. (3)Consumers who are using web can inquire about producing information of all organic agricultural products through products traceability numbers, which can guarantee consumers' legitimate rights and interests.

4 Key Technology 4.1 ASP Technology ASP (Active Server Pages), dynamic server page, including three parts of the content: VB Script or JavaScript scripts the code, which is used to implement the logical control; Integrated server functionality components, which is used to passing on a information of the client and server, ADO( ActiveX Data Objects, ActiveX), which is used to access the database. 4.2 JavaScript JavaScript is a type of light and interpretation programming language and has ability of oriented object. The language common core has been embedded Netscape, Internet Explorer and other web browser, it makes web process design rich and colorful expressing the web browser window and its content object. The client version of JavaScript adds executable contents to the web, in this case, the web is not static HTML, but including the process of interacting with the procedures, the process of controlling browser and the process of building HTML dynamically[4]. 4.3 ASP+ADO Database Access Technology ADO (ActiveX Data Object) a database access control of asp built in the server, it is the most efficient, simplest, powerful function method aiming at data operation that Microsoft software supports. It supplies database technology which can gain access to Internet easily for dynamic web page. ADO can supply main content including database information for users through accessing and operating data sheet in the database and can make user have access to background database through executive SQL command. In addition, ADO is almost compatible with all kinds of the database system, such as Microsoft Access, FoxPro, SQL Server and so on, ADO supplies the same mediashow for programmers [5].

5 System Operation Organic soybean quality security traceability system has been put in the actual application, including prevention crops pests query, organic agricultural products implementation standard query, organic crops production operation procedures query, prevention crops diseases query, organic agricultural products traceability system, etc. Organic soybean product traceability system can make us understand traceability information of single product production supply chain from planting to processing and make tracking products true, which ensures the quality safety of products, in this

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Fig. 4. Traceability information system of organic soybean

case, enterprises can inquire about materials information of production and inspection and understand the situation of agricultural production quickly. Consumers also can inquire about the information of products, it enhances the transparency of production and improves consumers’ confidences. The red star farm organic soybean quality safety traceability information system, as shown in fig 3.

6 Conclusions and Prospects It analyses organic soybean production supply chain concretely in the face of planting, harvest, processing and packing. combining with organic soybean production practice, it ensures the integrity of organic soybean quality safety information ,which is good for tracking information, and it also supplies data supporting for organic soybean quality safety information platform. It put forward the structure of B/S organic soybean traceability system which makes system have better operation, security and stability. The problem of food quality safety is still concerned by people at home and abroad. The implementation of digital information management is more and more popular with the spread and development of information technology, transparency of production information. It researches on the multi-factor of organic soybean traceability combining with the characteristics of Great Northern Wilderness organic soybean. Currently, the system only involves the parts from planting on the farm to processing in the enterprise, but in the future, the system can reach more fields such as selling field.

References 1. Shi, Y.: Agriculture Products Traceability System Study. Chinese Agriculture University, Beijing (2006) 2. Chen, H., Shi, Y., Tian, Z.: Game Analysis of Agriculture Products Traceability System in Our Country. China Economist (7), 10–12 (2007) 3. Geng, C.: Research on Agriculture Products Quality Security Information System Building. Shandong Agricultural University, Shandong (2006) 4. Wang, P., Feng, M.: Web2.0 Development of Technology Solutions, pp. 13–15. People Postal Press, Beijing (2006) 5. Sun, Q.: Implementation of Assignment System Based on Web Developed by ASP. Journal of Xuzhou Normal University (Natural Science Edition) (9), 25–28 (2001)

Study of Agricultural Informatization Standards Framework Yunpeng Cui, Shihong Liu, and Pengju He Key Laboratory of Digital Agricultural Early-warning Technology, Ministry of Agriculture, Beijing, The People’s Republic of China 100081

Abstract. This paper introduces the research methods, the content and technical route about the study of agricultural informatization standards Framework, which describes the structure of the agricultural informatization standard system, the construction process of agricultural informatization standard system diagram, and put forward the working ideas and deployment of the future standardization work of agricultural informatization. Finally, the paper put forward that the standardization of agricultural informatization is the necessary route for the construction and development of agricultural informatization, it’s significant for the construction of agricultural informatization in China. Keywords: Standards Framework, Agricultural Informatization.

1 Introduction To strengthen the construction of agricultural informatization is very important for China’s agriculture development and upgrading. In recent years, China has made significant progress in agriculture informatization construction, on the other hand, because the lack of overall planning and related standards, the construction can’t develop orderly and formally[1]. Therefore, to speed up the process of agricultural informatization standardization is an important measure for ensuring the progress of china's agricultural informatization[2]. Against the background, commissioned by the market economic information department of MOA, PRC, the agriculture information institute of CAAS start the agriculture informatization standards system study, the purpose of the study is coordinate and deploy the agriculture informatization standardization work, sort out existed national and domestic agriculture informatization standards, and promote the agriculture informatization standardization process, ensuring the development of the agriculture informatization.

2 Agriculture Informatization Standardization System Reference Model The reference model of agriculture informatization standardization system shows as Figure 1. The agriculture informatization standards can be divided into three categories D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 573–578, 2011. © IFIP International Federation for Information Processing 2011

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according to their usage[3], basic standards, universal standards and specialized standards, among the three kinds of standards, the management standards, the technical standards and the working standards spread over all these standards. The management standards are used in the management of the agriculture informatization work, which standardize the management of agriculture informatization construction; while the technical standards regulate the technical details involved in the process of the implementation of agriculture informatization; the work standards standardize and restrain the concrete work of the development and implementation of agriculture informatization.

Fig. 1. The reference model of agriculture informatization standardization system

3 The Agriculture Informatization Standards System Framework and Standards System Table The thoughts we construct the agriculture informatization standards system framework includes 2 methods, firstly, we use the three-dimensional standards system framework thoughts put forward by Dr. Verman[4], to divide complex problems into easy sub tasks, and express the tasks with human thinking ways, so make the complex problems simplified and organized.

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Fig. 2. The three-dimensional model of agriculture informatization factor categories

We divide all the agriculture information factors according to their categories, represent any 3 categories related closely with three-dimensional model (shown as Figure 2), then fill the every category with concrete factors ,and combine the related factors, filter, analyze the combined factors, finally, produce the framework. Secondly, through the top-down and bottom-up methods, top-down means extracting and summary of the existing taxonomy (such as FAO Agricultural subject classification scheme and Chinese library classification) to product the framework, and

Fig. 3. The top-down and bottom-up methods for constructing the agriculture informatization standards system framework

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Fig. 4. The top layer of agriculture informatization standards framework

bottom-up means to sort out existing agriculture informatization standards and related standards to produce and verify the framework (see Figure 3). Figure 4 shows the top layer of the agriculture informatization standards framework. The agriculture informatization standards system framework is consist of 7 categories, they are related standards, basic standards of agriculture information technology, security standards of agriculture information, agriculture information infrastructure standards, agriculture information resources and services standards, agriculture informatization management standards and agriculture informatization special application standards. We established an agriculture informatization standards system table based on the framework and the sorting out of the existing standards, now we have more than 2,000 standards in the table, include about 900 standards that haven’t been formulated but necessary for agriculture informatization construction in our country.

4 The Working Scheme of Agriculture Informatization Standards System Construction The whole work of the construction of agriculture informatization standards system is consist of 6 stages, system pre-research, system planning, standards formulation, standards trial and verification, standards training and carry out and standards application. The task of pre-research stage includes analysis for the national and domestic agriculture informatization and standards, find the problems, trends of the construction of agriculture informatization in our country, ensure the background and purpose of the construction of agriculture informatization standards system. The study of the paper is a part of this stage. At the system planning stage, the purpose and the responsible organization of the agriculture informatization standardization should be put forward clearly, and the system should be planned, the platform for standards study and formulation should be established.

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The task of standards formulation stage is the organizing of the concrete standards formulation. The basic standards should be formulated first, and then other standards. the agriculture informatization standardization working community should be established at this stage, to manage and coordinate the standards formulation work. At the standards trial and verification stage, based on the work objectives, contents and application of object of last stage, the standards application and verification tools should be developed, to verify the applicability of the standards. At standards training and carry out stage, the related organization should organize the training for relevant organizations and individuals, and organize the standards publicly and implementation work. Finally, the standards application stage, the standards conformance certification mechanism and implementation mechanism should be established, the standards should be applied in the related product development and project completely, and constantly improve the agriculture informatization standards system and the specific standards based on the application.

5 Conclusion The appearance of agriculture informatization standards system is the inevitable consequences of agriculture informatization construction[5], and it’s the basis of agriculture informatization technology system, it’s also one of the key bottlenecks for agriculture informatization progress. The purpose of agriculture informatization standardization is to establish a series of agriculture informatization standards, the implementation of the standards can work together to support the planning of agriculture informatization and related project, guiding the development and innovation of agriculture information industry, ensure the development of construction of agriculture informatization in our country scientifically and orderly, and accelerate upgrading of agriculture information industry.

Acknowledgement The research was supported by the special project from ministry of agriculture of the people’s republic of China, named study of agriculture informatization standards system and special fund of basic commonweal research institute project of information institute of CAAS, and National 11th five-year technology based plan topic named study of Agricultural product quantity Safety Data obtained standards (2009BADA9B02).

References [1] Xie, X., Liu, X.: Study of Beijing agriculture standards system construction. China Standardization (7), 8–9 (2007) [2] Liu, S., Hu, H.: Study on standard system frame of agriculture informatization. Agriculture Network Information (2), 13–17 (2006)

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[3] Jiang, Z., Yao, Y.: Reference model for information standards of land and resources. Geomatics World (5), 12–17 (2003) [4] Verman, L.: Standardization-a New Discipline. Archon books, The Show String Press Inc. (1980) [5] Yang, L.: Study of construction of national agriculture technical information classification and encoding standards system. China Standardization (12), 13–16 (2006)

On Countermeasures of Promoting Agricultural Products’ E–Commerce in China Weihua Gan, Tingting Zhang, and Yuwei Zhu School of Mechanical and Electronical Engineering, East China Jiaotong University, 330013, Nanchang, P. R. China [email protected], [email protected], [email protected]

Abstract. China is a large agricultural country, yet the development environment of E-business in China is not mature. E-Commerce of agricultural products is a revolutionary change of traditional agricultural economy. This paper focuses on agricultural products’ electronic commerce in China. Firstly, the paper describes the current situation of electronic commerce. Afterwards, the paper uses Fuzzy Analytic Hierarchy Process (AHP) to analyze the factors hindering agricultural products’ electronic commerce in China. Finally, it puts forward the countermeasures to promote the development of agricultural products’ e-commerce. Keywords: Agricultural products, electronic commerce (EC), Fuzzy Analytic Hierarchy Process (AHP).

1 Introduction Research of agricultural products in electronic commerce (EC) can be dated back to 1970s in the United States. Then much attention comes from Japan, Netherland, etc [1]. China is a large agricultural country, yet the development environment of E-business in China is not mature. E-Commerce of agricultural products is not a simple substitution of traditional distribution way. It is a revolutionary change of traditional agricultural economy since agricultural products in E-Commerce need the electronic commerce system through producing, processing and selling. Moreover, information infrastructure, supporting facilities and talents are the key factors influencing agricultural products’ E-Commerce.

2 Background of Agricultural Products’ E-Commerce in China The application of internet technology gives agricultural circulation a new life. Contrary to the traditional face-to-face trading in the agricultural products market, agricultural products’ E-Commerce can integrate all kinds of resources. In recent years, some agricultural e-commerce platforms are emerging in China, e.g. Shouguang county, Shandong province has set up E-Commerce service platform of vegetable future transaction, auction and exchange online. Shanghai Nongxin E-Commerce D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 579–586, 2011. © IFIP International Federation for Information Processing 2011

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Co. Ltd. is operating a high-quality-agricultural-product ordering platform providing the door-to-door service for customer [2]. Nowadays the ministry of agriculture tries to help more and more peasants to utilize such advanced technology, e.g. website of Chinese agricultural information net, “one-stop-trade” and “online-exhibition-hall”, to expand their market and increase their incomes.

3 Factors Influencing Agricultural Products’ E–Commerce There are many factors hindering e-commerce of agricultural products, but major factors is still on the debate [3]. With the development of network economy, E-commerce gradually turns to be significant trading patterns in the modern society. In order to mature such pattern, effective measures should be taken as soon as possible. Furthermore, the key factors should be found. Then use Fuzzy Analytic Hierarchy Process (AHP) to determine the influence of factors related to agricultural e-commerce. 3.1 Introduction of Fuzzy AHP AHP proposed by American professor T·L·Saaty in 1980s is an evaluation and decision making method with qualitative and quantitative analysis. Fuzzy AHP based on AHP and the fuzzy method comprehensively analyzes every element in the system to find the best scheme. By fuzzy AHP the values to a 0-1 range are used instead of 1-9 nine evaluation methods to establish decision matrix [4]. The basic process is as follows: (1) Firstly, draw analytic hierarchy chart based on the decision object. Objective level——decisions among several plans Criteria level——factors in decision making processing. Scheme level——several alternatives (2) Secondly, grade the scores. According to the importance of each factor in criteria level with decision-making requirement, the weight vector is obtained: W=(wl, w2, … w5). Wi ≥0, and

s

∑W

i

= 1.

i =1

If Wi =0, it means that failure to take the factor into consideration in some situation. Then, evaluation system can be established to make a comparison among chosen plans with every factor in criteria level. Scoring vector of factor i in several alternatives can be achieved by making points within 0, 1 .

﹛ ﹜

Pi = (Pi1 , Pi2 ,..., Pin ), i = 1,2,..., s . Pi as Row i of a matrix to get an s × n matrix P and weight vector W as a 1 × s matrix. The scoring vector and weight vector can be called fuzzy sets in Fuzzy Mathematics, matrix is called Fuzzy Matrix.

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(3) Thirdly, calculate the product of W and P to matrix multiplication. The result is a 1×n matrix A. s

a i = w1 P1i + w2 P2i + ... + ws Psi = ∑ wk Pki , i = 1,2,...n . k

a i is the final evaluation of Scheme i under the impact of factors S. (4) Finally, carry out a strategic choice to the vector in the matrix A. 3.2 Main Factors Affecting Agriculture Products’ E-Commerce Figure 1 shows the structure of factors affecting agriculture products’ E-Commerce. The first layer-objective level is the target, i.e. the result for such analysis. The second layer-criteria level, lists some main factors by the method of Delphi, they are transaction subject, object and platform, environment. And the third layer-scheme level, respectively presents each main factor in detail, e.g. for the transaction subject, subject cognition and information quality are considered, while for the transaction object, standard, brand and industrial and commercial level are considered, and for the transaction platform, information infrastructure and classification and normalization are considered, and for social environment, logistics level, transaction security, electronic payment, social credibility are considered. Afterwards, determine the weight of these factors in layer B accounting for layer A, then calculate the weight of factors in layer C accounting for layer B. Finally, obtain the affecting scores of each factor in layer C accounting for layer A.

A: Objective level

B: Criteria level

C: Scheme level

Factors Hindering EC Transaction Subject

Transaction Object

(subject cognition, information quality)

(standard, brand, industrial level, commercial level )

Transaction Platform

(information infrastructure, information classification, normalization)

Social Environment

(logistics, transaction security, electronic payment, social credibility)

Fig. 1. Factors Hindering Agricultural Product’s E–Commerce Development

Determine each factor's importance from 0 to 1 by comparison. The bigger the value is, the more important. Table 1 shows the fuzzy decision matrix [5].

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Signification Compared with each other, one element is obviously less important than the other. Compared with each other, one element is less important than the other. Compared with each other, one element is slight unimportant than the other. Compared with each other, elements both have the equivalent importance. Compared with each other, one element is more important than the other. Compared with each other, one element is greatly important than the other. Compared with each other, one element is critically important than the other.

） [0.1-0.3） [0.3-0.5） [0-0.1

0.5 (0.5-0.7) [0.7-0.9

）

[0.9-1]

From the above, there are four elements in layer B: B1= Transaction Subject B2= Transaction Object B3= Transaction Platform B4= Social Environment A decision matrix can be achieved according to the method of Delphi (see Table 2). Table 2. Importance for elements of layer B

B1

B1 -

B2 0.6

B3 0.65

B4 0.7

B2 B3 B4

0.4 0.35 0.3

0.4 0.35

0.6 0.45

0.65 0.55 -

∵ D = ∑ B , F = ∑ (∑ B ) , 且(i≠j). ∴ the weight of every element in layer B is w =D/F. 4

j =1

ij

4

4

j =1

i =1

ij

i

WiB = (0.325, 0.275, 0.217, 0.183).

In the same way, there are 13 elements in layer C. C1= subject cognition C2= information quality C3= standard C4= brand C5= industrial level C6= commercial level C7= information infrastructure

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C8= information classification C9= normalization C10= logistics C11= transaction security C12= electronic payment C13= social credibility Table 3, Table 4, Table 5 and Table 6 show the weights of element in layer C accounting for the element in layer B: Table 3. Importance for elements of group CB1

C1

C1 -

C2 0.51

C2

0.49

-

Table 4. Importance for elements of group CB2 C3 0.44 0.46 0.58

C3 C4 C5 C6

C4 0.56 0.48 0.47

C5 0.54 0.52 0.51

C6 0.42 0.53 0.49 -

Table 5. Importance for elements of group CB3 C7 0.40 0.44

C7 C8 C9

C8 0.60 0.56

C9 0.56 0.44 -

Table 6. Importance for elements of group CB4

C10 C11 C12 C13

C10 0.30 0.35 0.40

C11 0.70 0.40 0.55

C12 0.65 0.60 0.42

∴W

B1 C = (0.51, 0.49) B2 C = (0.254, 0.245,

W

0.235, 0.260)

WCB3 = (0.357, 0.260, 0.333) WCB4 =(0.326, 0.225, 0.221, 0.225)

∵W

i

B

(i= l, 2, 3, 4) and

WkC (k=1, 2, 3,…13).

C13 0.60 0.45 0.58 -

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∴ The influence of each factor on electronic commerce can be found. The following conclusions are drawn:

WCA = (0.16575, 0.15925, 0.06985, 0.0682, 0.06545, 0.0715, 0.083979, 0.05642, 0.072261, 0.59658, 0.041175, 0.040443, 0.041724). It is obvious that 0.596589 (C10) is the maximum, then 0.16575 (C1) and 0.15925 (C2), while 0.040443 (C12) is the minimum, following is 0.041175 (C11) and 0.041724 (C13). Therefore, the most important factors to the development of agricultural products’ e-commerce are logistics development level, subject of cognition, information quality of peasants. On the contrary, electronic payment, transaction security and social credibility make the slightest effect on agricultural e-commerce.

4 Countermeasures for the Development of Agricultural EC 4.1 Set Up Effective Agriculture Product Logistics System Owing that agricultural products varied from types and volumes, much difficulty will come up in the logistic activities, i.e. fresh-keeping, transportation, follow-up treatment [6]. Nowadays weakness of agricultural products distribution system is one of the bottlenecks in the development process of agricultural products’ E-commerce in China. It is necessary to set up refrigerated-warehouses, distribution centers, etc. to accelerate the speed from the countryside to city. On the other hand, tracking and tracing system through GPS and GSI should also be introduced to help buyers and sellers monitor the whole process via network to improve the safety, reliability, accuracy of agricultural products. 4.2 Improve Rural Information Infrastructure Although internet is available everywhere in the city, the users of internet in the countryside are so less [7]. Such unbalanced situation becomes the second obstacle for agricultural EC. It is necessary to improve rural information infrastructure in China in order to reduce the cost of gaining information for the peasants. 4.3 Implement Standardization and Brands After accession to WTO, our exports of agricultural products often reject. No wonder that consumers prefer safety and famous agricultural products. It is eager for agricultural products to implement standardization and brands. Ministry of agriculture in China has carried out some standards about agricultural products [8]. In addition, our agricultural products should have our own brands and register trademarks all over the world. It is useful to depend on the intellectual property rights to protect our own brands and expand our markets. If national agricultural product becomes the famous brand in the world then the buyers abroad can easily click the button “Search” and catch the related information.

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4.4 Cultivate Talents in Agricultural Products’ EC Another key element for the success of agricultural products’ EC is talents in this field. If millions of peasants can understand the principles of EC and are in their positions to trade via internet, agricultural products’ EC will realize. Firstly, a long-term education plan about science and technology should be made in the countryside [9]. Secondly, knowledge related to information technology and electronic commerce should be taught for the peasants. Another way is preferential policies guiding EC professional talents in the colleges to service for countryside. 4.5 Promote Industrial Management of Agriculture The current main body in agricultural products’ EC is agricultural operation enterprises since the firms could take use of their own advantages on capital and technology to improve the value of agricultural products and speed up the standardization of local agricultural production, enhance the competitiveness. Industrialized operation of agriculture is not only useful to open both international and domestic market of our agricultural products, but also can accelerate the development of rural social economy. Therefore, more and more agricultural products operating and processing enterprises should be encouraged to open to strengthen the competitiveness and raise agricultural labor employment. 4.6 Specialize E-Commerce Websites The EC website is the network platform to develop EC, there are more than 6000 websites related to agriculture in the countryside in China [10]. However, agricultural websites in western China are fewer than in other regions. A number of websites focus on information integrating function, the payment function of website is not perfect. At present, the EC domestic websites of agricultural products are far from professional and colorful. As a result, more agricultural enterprises and intermediary organizations should establish commercial websites. And these websites should be estimated regularly by the public and government.

5 Conclusions Depending on the basic industry of national economy, agriculture has a bearing on the healthy and stable development for economic and society. Along with optimized structure of agricultural production and improved level of e-commerce, technology innovation could accelerate modernization and industrialization of agriculture, and provide more comprehensive prospects for agricultural e-commerce. A new generation agricultural e-commerce platform comes to the top. With the development of network economy, e-commerce gradually will turn to be significant trading patterns in the future.

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Acknowledgments Thanks for the support from national science fund: On Relation Between Mode of Pig Green Supply Chain and Performance around the Poyang Lake (70962002).

References 1. Hu, T.: Analysis in EC Development of Agricultural Products in China. J. Agricultural Economy 5, 23–27 (2005) 2. Shao, B.: EC Summarize, p. 115. Academic Press, Beijing (2004) 3. Yang, C.: Research of Electronic Trade Market of Supply Chain Management. J. Commercial Research 11, 88–91 (2006) 4. Sun, W.: Research of Rural Economical Increasing. China Agriculture Science &Technology Press, Beijing (2002) 5. Yang, Q.: Technology and Application of EC. Prentice Hall International, Inc., Beijing (1999) 6. Li, D.: Basis of Electronic Commerce. University of Capital Business and Economics, Beijing (1999) 7. Zhao, J.: Some Thoughts about the Development of Agricultural E-Commerce in China. J. Inquiry into Economic Issues 1, 98–99 (2005) 8. Timmesr, P.: Business Models of Electronic Markets. J. Electronic Market 8(2), 3–8 (1998) 9. Kraemer, K.L., Dedrick, J.: Strategic Use of Internet and E-commerce: Cisco System. J. Strategic Information System 11, 5–29 (2002) 10. Henderson, J., Dooley, F., Akridge, J.: Internet and E-Commerce Adoption by Agricultural Input firms. J. Review of Agricultural Economics 26(4), 505–520 (2004)

Study on Approaches of Land Suitability Evaluation for Crop Production Using GIS Linyi Li, Jingyin Zhao, and Tao Yuan Shanghai Academy of Agricultural Sciences, China [email protected]

Abstract. In this paper, an approach to assessing ecological suitability of crop growth was developed using GIS, through a case study of maize planting in Jilin Province, in the northeast China, in order to improve the accuracy and objectivity of the evaluation, promote the rationality of crop distribution, and increase the efficiency of the agro-resource utility. Climate and soil conditions in the region were taken into consideration in the research. The crop potential productivity under the climatic condition of 46 meteorological stations in the region, namely photo-temperature-water climatic potential productivity, was calculated based on the meteorological data of the precipitation, temperature, wind speed and sunshine hours from 1980 to 2006, and the crop potential productivity was used as index to evaluate the climatic suitability for crop growth. The point data were interpolated in a GIS environment and formed climatic suitability map. The assessment of soil suitability for crop growth was derived from soil fertility map drawing on the basis of information on 12 parameters, such as terrain, depth of soil body, thickness of black earth, soil erosion and organic mater. The ecological suitability map is completed using methods of spatial overlay in GIS. Comparing with the crop yield map that based on the statistical yield of 30 years in each county, the result of ecological suitability assessment is consistent with the yield. Therefore this method is feasible to assess the ecological suitability in the region. Keywords: Land Suitability Evaluation, Crop Production, Climatic Potential Productivity, Grid Segmentation, GIS.

1 Introduction Land suitability evaluation is the process of assessment of land performance when land is used for specified purposes. Adaptation of crop growth to the potentialities and constraints of local agroecologies is a key principle of sustainable land management. The principal objective of land evaluation is to select the optimum land use for each defined land unit and the conservation of environmental resources for future use. Detailed objectives can vary considerably according to the purpose and scale of land evaluation[1]. Large area lands are used for grain crop production in the northeastern China. The degree of land suitability for crop production is quite different by reason of the diversity of climate, complexity of soil and variety of terrain. Land suitability is the fitness D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 587–596, 2011. © IFIP International Federation for Information Processing 2011

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of a given type of land for a defined use, but large-scale crop production can not arrange optimum crop for each land unit. Proper land suitability classification approaches are needed to meet the demand of evaluating land suitability for crop growth and large-scale crop production management. Photo-temperature-water climatic potential productivity, calculated based on the meteorological data of the precipitation, temperature, wind speed and sunshine hours, etc., was taken as indicator for climatic suitability evaluation, and soil fertility composite index based on terrain, depth of soil body, thickness of black earth, soil erosion, organic mater, etc. was taken as index for soil suitability evaluation. Taking maize production in Jilin province as a case study, this research brought forward an approach to evaluating land suitability for large-scale crop production using the spatial analysis and overlay function of geographic information system.

2 Methods 2.1 Index and Method for Climatic Suitability Evaluation The crop climatic potential productivity (photo-temperature-water potential productivity) is the highest theoretical productivity of the crop when crop makes full use of the present climatic resources in this area with the most appropriate technology and managerial techniques. The climatic potential productivity is not the real productivity in the area, but it could be taken as the indicator for evaluating climatic suitability for crop production. The higher the climatic potential productivity is, the more suitable the climate in the area is for the crop growth. Climatic potential productivity was calculated by the formula as follows [5]: Y = f (Q) f (T ) f (W )

(1)

Where: Y is Climatic potential productivity f(Q) is photosynthetic productivity and is the utmost productivity of the crop when crop makes full use of solar radiation in this area with the most suitable temperature, water, soil fertility, technology and managerial techniques. f (Q) = ∑ Qε a (1 − ρ )(1 − γ )Φ (1 − ω )(1 − X ) (1 − H ) −1 −1

(2)

ΣQ is total radiation per unit area and per unit time casting on the area, ε is the ratio of photosynthetically Active Radiation(PAR) to total solar radiation, αis plant absorption rate for PAR, ρis absorption rate of nonphotosynthesis organs, γis limitation rate of photosaturation, Φ is quantum efficiency, ω is loss rate of respiration, X is ash content in the product harvested , H is water content in the product harvested. f(T) is temperature reversionary function ⎧ 0.027T − 0.162 ⎪ ⎪⎪ 0.086T − 1.41 f (T ) = ⎨ 1.00 ⎪ − 0.083T ⎪ ⎪⎩ 0

6D C ≤ T < 21D C 21D C ≤ T < 28D C 28D C ≤ T < 32D C 32D C ≤ T < 44D C T < 6D C T ≥ 44D C

(3)

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f(W) water reversionary function ⎧ (1 − C ) R ⎪ f (W ) = ⎨ ETm ⎪⎩ 0

ETm = kc ET0

0 < (1 − C ) R < ETm (1 − C ) R ≥ ETm

(4) (5)

R is precipitation, C is runoff coefficient, ETm is crop water requirement, ET0 is reference evapotranspiration, Kc is crop coefficient. ET0 was calculated by modified Penman equation. 2.2 Index for Soil Suitability Evaluation Some soil elements that influenced crop production such as organic mater, total nitrogen, available N, available P, depth of soil body, depth of black earth, terrain and soil texture were derived to evaluate the suitability of soil. The range of each element is divided into 3-5 level scales on the basis of their degrees of suitability or limitations to the crop growth. The different levels are defined as follows: z Level 5: the characteristic (quality) is optimal for crop growth. z Level 4: the characteristic is nearly optimal for crop production. z Level 3: the characteristic has moderate influence on yield decrease and benefits can still be made. z Level 2: the characteristic decrease the yield and the land becomes marginal for crop growth. z Level 1: the characteristic may prohibit the use of soil for crop cultivation. The suitability of each soil unit was evaluated on the basis of soil fertility composite index, which accumulates all the level numbers of each element of this soil unit. The higher the accumulated numerical value is, the more suitable the soil is for crop growth. 2.3 Land Evaluation Methods 2.3.1 Classification of Climatic Suitability Corresponding to each meteorological station, there is a numeric value of climatic potential productivity calculated. Classification of climatic potential productivity was made using spatial interpolation method and a classification map was formed in geographical information system. The map indicated that the region with high value had more suitable climate for crop growth. 2.3.2 Classification of Soil Suitability For large scale area, complex landform and soil quality determined that there are big differences among the suitability of soil units. The problem is how to classify soil units according to their fertility. The methods we used were: first, to form the polygon map of soil fertility composite index on the basis of soil type map by calculating soil fertility composite index of each soil unit in GIS platform. Next, segment the study area by rectangular grid of a certain size. The size of grid was determined by the scale

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of the study area and research objective. Then, calculate the weighted average soil composite fertility index of each grid taking the area of the soil with different types in the grid as weighting, and assign the value of the average fertility on the center of the grid. Finally, form the map of soil suitability classification on the basis of the values of fertility in each grid by spatial interpolation method. 2.3.3 Synthetical Evaluation of Land Suitability Overlay the climatic and soil suitability map to synthetically evaluate the land suitability. The GIS platforms used were Arcview 3.2 and ArcGIS 9.0.

3 Case Study 3.1 Study Area Jilin province is located in the central part of northeast China(122-131°E , 41-46°N), covering the area of 187,400 square kilometers, The eastern part of the province is the mountainous area of the Changbai Mountains with an elevation of over 1,000 meters and the Jidong hilly land about 500 meters above sea level. The western part of the province is the Songliao Plain, whose low and level western section is the grain producing area of the province. The soil type changes from dark-brown earth, black soil to chernozem from east to west. The annual time of sunshine is 2,200-3,000 hours. The annual average effective accumulated temperature is 2,700-3,600 . The precipitation of the province in a year is 550-910 mm and the frost-free period lasts 120-160 days. Jilin is one of the most important commodity grain producing areas in China. It abounds with soybean, corn, sorghum, millet, rice, small red bean, wheat, tuber, sunflower seed, beet and tobacco.

℃

3.2 Data 1:500,000 soil map and attribute data were provided by Soil and Fertilizer Station of Jinlin Province. The data of crop yield, planting area of 30 years in each county were obtained from Statistics Bureau of Jilin Province. Meteorological data of precipitation, temperature, wind speed and sunshine hours from 1980 to 2006 in 46 meteorological stations were provided by Meteorological Bureau of Jilin Province. 3.3 Result 3.3.1 Climatic Suitability Evaluation The value selection of the parameters in the photosynthetic productivity calculation formula were as follows [2,3,4]: ε=0.49,α=0.48,ρ=0.10,γ=0.01,Φ=0.224,ω=0.30,X =0.08,H =0.14 Y. Shan calculated the water requirements of maize in the Bajiazui irrigation area in Qingyang city, Gansu Province. Modified Penman equation was used to calculate reference evapotranspiration, and crop coefficient Kc was obtained by experiment[2]. Compared with the test result, the result calculated by penman equation was more

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accurate than that of the other methods. We used this value of Kc in this research to calculate water requirements. Climatic potential productivity was calculated using the methods mentioned above. The higher the value of climatic potential productivity was, the more suitable the climate was for crop growing. The classification of climatic suitability in Jilin Province was made by equally dividing the range of the numerical value of climatic potential productivity of all counties into 8 level and mapping in GIS software. The evaluation map is presented in Fig. 1.

Fig. 1. Classification of climatic suitability for maize production in Jilin Province

From the view of climatic conditions, mid-eastern Jilin Province is the most suitable region for maize production, including most part of Ji’an, Tonghua, Liuhe, Meihekou, Dongfeng, Liaoyuan County and a part of Yongji and Jiaohe County. Light, heat and water resources are abundant in the farming season in this region. The climatic suitability gradually decreases from this region to the east and the west region respectively. The eastern part of Jilin Province is full of water resources, but is lack of light and heat resources. Maize production in the western part is restricted by the water condition despite the abundance of light and heat resources. 3.3.2 Soil Suitability Evaluation Soil elements which influence crop production were used to analyze soil fertility, including: organic mater, total nitrogen, available N, available P, depth of soil body, depth of black earth, thickness of strata feature, terrain, soil texture, soil moisture, obstacle factors, soil erosion, and unit production. The irrigation was not taken into consideration because maize planting is rainfed in Jilin Province. The different level scales of suitability or limitation were defined in table 1[6].

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Levels Organic mater (%) Total nitrogen (%) Available N (mg/kg) Available P (mg/kg) Depth of soil body (cm) Depth of black earth (cm) Thickness Burying depth Depth of calcic horizon Terrain Soil texture (Tilth layer) Soil moisture

5

4

3

2

1

Note

>6 >5 >4 >3 >0.3 >0.25 >0.20 >0.15 >250 >200 >150 >120 >30 >25 >20 >15

6~5 5~4 4~3 3~2.5 0.3~0.25 0.25~0.20 0.20~0.15 0.15~0.125 250~200 200~150 150~120 120~90 30~25 25~20 20~15 15~10

5~4 4~3 3~2 2.5~1.5 0.25~0.20 0.20~0.15 0.15~0.10 0.125~0.075 200~150 150~120 120~75 90~60 25~20 20~10 15~10 10~5

4~3 3~2 2~1 1.5~0.6 0.20~0.15 0.15~0.10 0.10~0.05 0.075~0.03 150~120 120~75 75~30 60~30 25~20 10~5 10~5 5~3

3~2 <2 <1 <0.6 0.15~0.1 <0.1 <0.05 <0.03 120~75 <75 <30 <30 <10 <5 <5 <3

1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4

>100

100~50

50~30

<30

>50

50~30 >30 >20

30~20 30~20

<20 <20 <20

Yellow exposure

>200 >50

100~50 <50

(4) (5)

<30

(6)

200~100 >30 Plainness Loam soil Highly sufficient

Obstacle factors(depth) Soil erosion Unit production ˄kg.ha-1˅

(1) (2) (3)

>7500

Slight unevenness Sandy loam soil moderate

Slow slope Sandy soil

None (Deep)

Mild influence (Middle)

liabledrought Moderate influence (Shallow)

No or mild erosion

Moderate erosion

Severe erosion

7500~6000

6000~4500

4500~3000

Sufficient

Note: 1 Eastern cold and humid mountain area 2 Eastern warm and humid semi-mountainous area 3 Central semi-humid liable-drought plateau area 4 Western semi-humid semiarid plain area

Steep slope Rocky soil Drought Severe influence (Exposure) Extreme serious erosion <3000

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(1) Black soil, chernozem, meadow brown soil, and paddy soil (2) Albic soil, aeolian sandy soil (3)Dark-brown earth (4) Peat soil (5) Swampy soil, bury peat soil (6) Chestnut soil

Fig. 2. Classification of soil suitability for maize production in Jilin Province

Soil suitability was divided into 8 levels on the basis of soil fertility composite index in Jilin Province. The evaluation result was presented in Fig. 2. The most suitable region for maize production expresses zonary distribution from Yushu, Nong’an to Changchun in Jilin Province. In general, the soil in the eastern humid semimountainous area and the western part of central semi-humid liable-drought plateau area, with sufficient or moderate soil moisture, plain or slightly uneven terrain, thick soil body and black earth, no or mild erosion and rich nutrients, is suitable for maize production. In the eastern part of central semi-humid liable-drought plateau area, soil is moderately eroded and the soil nutrients in these areas are less nourishing than those in the region mentioned above. In the western semi-humid semiarid transition plain region and semiarid plain region, the surface soil is liable to erosion and black earth layer is thin due to the rugged or long gentle slope terrain, and soil condition restricts crop production. 3.3.3 Synthetical Evaluation of Land Suitability Mapping land suitability for maize production was made by overlaying the climatic and soil suitability in GIS platform. Result of evaluation was presented in Fig. 3.

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Fig. 3. Classification of ecological suitability for maize production in Jilin province

Ecological condition is suitable for maize growth in the central part and mideastern part of Jilin Province. Light, heat and water resource are rich in the maize growing season. Soil condition satisfies the need for maize growth. In fact, there are several national commodity grain bases in the region. Most of eastern mountain area is covered with forest because the cool and cold climate is not suitable for crop cultivation. In the western part of Jilin Province, the poor soil condition and deficiency of water restrict the utility of light and heat resources, therefore the ecological condition is unsuitable for maize production.

4 Conclusion Comparing with the crop yield map which is based on the statistical yield of 30 years in each county, the result of ecological suitability assessment is approximately consistent with the yield. Crop production is determined by two major aspects, the aspect of land physical resources such as soil, topography, and climate, and the aspect of socioeconomic resources such as management level, market position, and other human activities. The former are relatively stable properties, while the latter are much more variable and dependent on social and political decisions (E. Van Ranst et al., 1991). This method is feasible to assess the ecological suitability in the region. More socioeconomic factors should be taken consideration during crop structure arrangement.

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The crop climatic potential productivity (photo-temperature-water potential productivity) is the highest theoretical productivity of the crop when crop makes full use of the present climatic resources in this area with the most appropriate technology and managerial techniques. The climatic productivity could properly evaluate the climatic suitability for crop growth by establishing the relationship between crop product and the light, heat and water resources. Analyzing spatial data in large scale area, it is difficult to extract the regularity among data due to the complexity of distribution of spatial attribute. Geographic information system is increasingly applied in the analysis of complex spatial data by its powerful function of data management, spatial analysis and graphics display. In this research, grid segmentation method is used to extract the regularity of soil fertility in large area in GIS. The analysis result is more rational and reliable. In the paper an approach to assess the ecological suitability of land is suggested. Parameters in the method such as crop coefficient, grid size and the number of classification grades should be adjusted according to the crop species, scale of area and purpose of analysis. Acknowledgements. Research funded by project from STCSM (08DZ2210600, 08QA14058) and National Key Technology R & D Program (2008BAD96B01). Many thanks to the staff in Soil and Fertilizer Station, Meteorological Bureau, Jilin Province for providing soil and meteorological data.

References 1. Van Ranst, E., Debaveye, J.: Land Evaluation Part I, Principles in Land Evaluation and Crop Production Calculation. Agricultural Publication. No. 7. Brussels, Belgium: General Administration for development cooperation (1991) 2. Shan, Y., Zhang, X., Chen, L.: Application of Penman Equation in Reference of Crop Water Requirements. Journal of Anhui Agri. Sci. 36(10), 4196–4197 (2008) 3. Guo, J., Gao, S., Pan, Y.: Study on Crop climatic potential productivity and the measures of its development and utilization in northeast China. Meteorology 21(2), 3–6 (1995) 4. Gurforch, H.W., Shay Kewich, C.F.: A Temperature Response Function for Corn Development. Agric. for Meteorol. 50, 159–171 (1990) 5. Deng, G.: Study on Light and Temperature Resources and Climatic Productivity of China. Natural resources (April 1980) 6. Soil and fertility station, Jilin province. Soil in Jilin Province. China Agricultural Press (1998) 7. Geerts, S., Raes, D., Garcia, M., Del Castillo, C., Buytaert, W.: Agro-climatic Suitability Mapping for Crop Production in the Bolivian Altiplano. Agricultural and Forest Meteorology 139, 399–412 (2006) 8. Qiu, B., Zhou, Y., Li, X.: Dynamic Assessment of Regional Land Resource Suitability Based on Geographical Information System. Acta Pedologica Sinica 39(3), 301–306 (2002) 9. Dai, Q., Liu, M., Wang, Y., Liu, G.: Land use Adaptability Evaluation and Potential Capability Analysis of Hilly Region in Northeast China. Bulletin of Soil and Water Conservation 23(1), 27–31 (2003)

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10. Wang, F., Xing, S.: Progress in the Research on Regional Agricultural Land Suitability Evaluation. Agriculture Network Information 1, 32–34 (2006) 11. Yu, J., Nie, Y., Zhou, Y., He, Y.: Niche-fitness Theory And its Application to GIS-based Multi-suitability Evaluation of Cultivated Land. Acta Pedologica Sinica 43(2), 190–196 (2006) 12. Zhang, J., Feng, J., Bian, X., Zang, M., Hu, D.: Variable Weight Approach in Evaluation of Crops Ecological Adaptability. Journal of Nanjing Agricultural University 29(1), 13–17 (2006) 13. Du, H.y., Li, J.: Agricultural Land Suitability Evaluation. Model and System Implementation-A Case Study of Panzhihua. Resources Science 23(5), 41–45 (2001)

Tracking of Human Arm Based on MEMS Sensors Yuxiang Zhang1, Liuyi Ma1, Tongda Zhang2, and Fuhou Xu1 1

203 office, Xi’an Research Inst. of Hi-Tech Hongqing Town, Xi’an , 710025 P.R. China 2 Department of Automation, Tsinghua University, Beijing, 100084, P.R. China [email protected], [email protected], [email protected], [email protected]

Abstract. This paper studied the method for motion tracking of arm using triaxial accelerometer, triaxial gyroscope and electronic compass. The motion model of arm is established. The hardware of tracking system of arm is designed. The track method of arm gesture based on multi-sensors data fusion is analyzed. The compensation algorithm for motion accelerations is researched. The experimental results demonstrate that the motion acceleration compensation algorithm is validity, which can improve the dynamic measure precision of arm gesture angle. Keywords: MEMS, movement track, gesture fusion, lever arm effect.

1 Introduction The human body movement track technology has been widely used in the areas of virtual reality and game development. At present, the human body movement track technology is mainly on the basis of pattern recognition technology or on wearable equipment technology. The human body movement track technology based on pattern recognition only need simple equipment and do not affect the personnel movement, but it has following shortcomings: it is rigorous to the recognition environment and it has blind angle when not in the open space; distinguishes; The recognition is affected under circumstance of occlusion; The recognition algorithm is hard to reach high precision. The wearable equipment used in movement track technology includes mechanical equipment and electromagnetism equipment. The mechanical devices adopt the support installation, which will restrict user's activity. The electromagnetism equipment is easy to be disturbed by the magnetic field, and is not suitable for complex electromagnetism environment. With the development of MEMS technology and semiconductor technology, the mini-accelerator sensors have been used in the human motion track. The movement track technology based on MEMS sensors has the advantages of frequency bandwidth, stability small size, low cost, long life and light weight. Many researches have concerned the technology. Hou W. s., Dai J.m. used triaxial accelerometer to track the human motion. Because the accelerometer is easy to be disturbed by motion acceleration and can’t detect the change of horizontal angle, the effect of their method is not good always. W.w. Feng [2] used both the magnetic sensors and accelerometer to D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 597–606, 2011. © IFIP International Federation for Information Processing 2011

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track the motion of upper limb. Since the magnetic is week and easy to be disturbed by environment, so the measurement is not accurate. Ryan Aylward [3] applied both triaxial accelerometer and triaxial gyroscope to measure the attitude of dancers. Liu X.y., Zhou Z.y.[4] add the triaxial magnetic into micro inertial measurement element to detect the motion of lower limbs. However, when the limbs have motion acceleration, their method will produce a relative large error. To obtain high precision measurement result, the compensation of acceleration is necessary. This paper studies the arm movement track technology based on the triaxial accelerometer, triaxial gyroscope and electronic compass, and researches the compensate algorithm for the accelerate component of arm movement. The arm movement track system is designed. The experiments show that the movement track effect is good.

2 The Motion Model of Arm and the Movement Track Principle The movement of arm can be simplified to the rotation under elbow joint and shoulder joint. The simplified arm is a linkage mechanism with two joints (figure 1). Taking the shoulder joint as origin, the reference coordinate system Ob - X b Yb Z b and

linkage coordinate system O p1 - X p1 Yp1 Z p1 are established. The references coordinate

system Ob is parallel to the human body, and is constant in the process of arm movement. The linkage coordinate system O p1 rotate with the big arm, and is described with pitch angle θ1 , roll angle ϕ1 , course angle ψ 1 . The position of big arm is as following: ⎡ x p1 ⎤ ⎡ xb ⎤ ⎢ y ⎥ = T −1 ⎢ y ⎥ . ⎢ b ⎥ 1 ⎢ p1 ⎥ ⎢ z p1 ⎥ ⎢⎣ zb ⎥⎦ ⎣ ⎦

(1)

⎡cosϕ1 cosψ1 − sinϕ1 sinθ1 sinψ1 − cosθ1 sinψ1 sinϕ1 cosψ1 + cosϕ1 sinθ1 sinψ1⎤ T1 = ⎢⎢cosϕ1 sinψ1 + sinϕ1 sinθ1 cosψ1 cosθ1 cosψ1 sinϕ1 sinψ1 − cosϕ1 sinθ1 cosψ1 ⎥⎥ . ⎥⎦ ⎢⎣ sinθ1 cosϕ1 cosθ1 − sinϕ1 cosθ1

(2)

where, T1 is the strapdown matrix between the coordinate system O p1 and the coordinate system Ob . The position of big arm can be obtained by solving the T1 real time. Because the roll angle dose not affect the position change of elbow joint, the roll angle of main arm can be considered as 0. The position of elbow joint can be expressed as: ⎡ xop 2 ⎤ ⎡ cosψ 1 sinψ 1 0 ⎤⎡0⎤ ⎡ sinψ 1 ⎤ ⎥ ⎢ ⎢ ⎥ . ⎥⎢ ⎥ = ⎢ ⎢ yop 2 ⎥ = ⎢− cos θ1 sinψ 1 cos θ1 cosψ 1 sin θ1 ⎥ ⎢l1 ⎥ l1 ⎢cos θ1 cosψ 1 ⎥ ⎢ z ⎥ ⎢ sin θ sinψ ⎢⎣ sin θ1 cosψ 1 ⎥⎦ sin θ1 cosψ 1 cos θ1 ⎥⎦ ⎢⎣ 0 ⎥⎦ 1 1 ⎣ op 2 ⎦ ⎣

where, l1 is the length of big arm.

(3)

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Fig. 1. The motion model of arm

The linkage coordinate system O p 2 - X p2 Yp2 Z p2 is established at elbow joint O p 2 . the linkage coordinate system O p 2 rotates with the forearm. The forearm can be described with pitch angle θ 2 , roll angle ϕ 2 , course angle ψ 2 , the position of forearm can be expressed as: ⎡ x p 2 ⎤ ⎡ xop 2 ⎤ ⎡ x p2 ⎤ ⎡ xb ⎤ ⎡ sinψ 1 ⎤ ⎢ y ⎥ = T −1 ⎢ y ⎥ + ⎢ y ⎥ = T −1 ⎢ y ⎥ + l ⎢cos θ cosψ ⎥ . 1 1⎥ ⎢ b ⎥ 2 ⎢ p 2 ⎥ ⎢ op 2 ⎥ 2 ⎢ p 2 ⎥ 1 ⎢ ⎢z ⎥ ⎢z ⎥ ⎢z ⎥ ⎢⎣ zb ⎥⎦ ⎢ sin θ cos ψ 1 1 ⎥⎦ ⎣ ⎣ p 2 ⎦ ⎣ op 2 ⎦ ⎣ p2 ⎦

(4)

where, is the T2 is the strapdown matrix between the coordinate system O p 2 and the coordinate system Ob .

3 Hardware of Arm Movement Track System The arm movement track system consists of big arm gesture detection module, forearm gesture detection module and the host computer. The hardware frame is shown in figure 2. The big arm detection module and the forearm detection module are fixed on the big arm and forearm, respectively. The pitch angle, roll angle and the course angle data of big arm and forearm can be measured, and then sent to the host computer by wireless module. According the motion models of arm, the position of arm can be obtained by the position solver. Hence, the movement track of arm can be realized. The arm gesture detection modules are consist of triaxial accelerometer, triaxial gyroscope, triaxial magnetic intensity sonsors, data sample module, microprocessor and wireless transmit module. The hardware frame is shown in figure 3.

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Fig. 2. The arm movement track system hardware frame

Fig. 3. The arm gesture detection module hardware frame

The triaxial accelerometer ADXL330 is produced by AD corp, which has a measure rage of ± 3g, with a sensitivity of 300mV/g. The gyroscope LPR530Al( with a rage of ± 300o/s) and LY530ALH (with a rage of ± 300o/s) are produced by ST corp.. The electronic compass sonsor HMC5843 (with a rage of ± 4gauss) is produced by Honeywell corp.. The data sample module use the ADS8344 (16 bit, 8 channels) which is produced by TI corp., it used for the A/D translation of the triaxial accelerometer and the triaxial gyroscope. The microprocessor is STM32F103RBT6, which is used for the noise reduction, position solver and data fusion. The results from the microprocessor are sent to the host computer by wireless module RF24L01. The gesture detection module is shown in figure 4.

Fig. 4. The arm gesture detection module

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4 The Arm Gesture Movement Track Algorithm Based on Multisensors 4.1 Multi-sensors Data Fusion Based on Kalman Filter

The gesture fusion algorithm of arm consists of three parts. Firstly, according the angle velocity vector measured by the triaxial gyroscope, the gesture angle is solved by the quaternion rotation vector three samples method which has good compensate effect to noncommutativity error; secondly, according the observe values of gravity vector and geomagnetism vector from the triaxial accelerometer, triaxial geomagnetism sonsors, the gesture angle and the course angle of arm can be obtained; finally, the arm gesture optimal estimation can be obtained by the data fusion of the gesture angle and the course angle using Kalman filter. Since the rotational angle is big and the change of gesture angle is quick, this paper adopts the closed loop correction indirect method Kalman filter to realize the data fusion of arm gesture [5]. K K K K (5) ωtrue = ωm − btrue − nw1 . K

K

K

ωest = ωm − best .

(6-a)

K 1G K b = − b − nw 2 .

(6-b)

τ

K K where, nw1 is the measurement noise of gyroscope, nw2 is the drift noise of gyroscope, τ is the drift time. Take the error angle and the gyroscope drift as status variable X = Δθ Δϕ Δψ Δbx Δb y Δbz T .The system equation is established as follow-

[

]

ing: X = ΑX (t ) + ΓW (t ) . where,

(7)

K ⎡[ω ×] − I 3×3 ⎤ ⎥ A=⎢ 1 ⎢03×3 − I 3×3 ⎥ τ ⎣ ⎦ ⎡− I 3×3 Γ=⎢ ⎣ 0 3×3

(7-a)

03×3 ⎤ I 3× X ⎥⎦

K K W (t ) = [nw1 nw2 ]T .

[

]

K W (t ) is white noise with zero-mean-value, and E W (t )W (t )T = Q , [ω ×] is the symK metric matrix of ω . When the object is in non-accelerate status, we can get the error angle Δθ m which is the difference between the observe values from the accelerometer or magenetic sensors and the calculation value from gyroscope. We can establish the Observation Equation

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Z K = HX K + VK

.

[

]

(8)

where, H = [I 3×3 03×3 ] , V K is white noise with zero-mean-value, E VK VK T = R . According to Eqs.(7), (8), we can design the Kalman filter to estimate the error angle of arm gesture. By taking error angle into the arm gesture quaternion, we can realize the correction of calculation values from gyroscopes. 4.2 Lever Arm Effect and Acceleration Compensation of Arm Gesture Detection Module

When the object is in non-accelerate status, the output of accelerometers have no acceleration components, the accelerometer can be used as inclinometer, the output is the gesture angle. By using the Kalman algorithm proposed in section 4.1, we can realize the good estimation of gesture angle and the gyroscopes drift, and then correct the gesture angle and the gyroscope drift. In our movement track system, the accelerometer can’t be installed at the joints of arm. Hence, when the arm is moving, a big deviation is produced due to the normal and tangential acceleration. The deviation will cause the gesture estimation error increased or even failure, which known as lever arm effect. 4.2.1 Lever Arm Effect Lever arm effect will produce if the inertial navigation system is not installed at the center of rolling motion, which can be described by the following model [6],

zn

zi K RO

oi

xn

on

K rp

yn

K RP

P yi

xi Fig. 5. The sketch of installation position

Show as in figure 5, define the static coordinate system O i - X i Yi Z i and the dynamic coordinate system O n - X n Yn Zn , and consider On as the roll center. The accelK erometer of inertial navigation system is installed on the fix point P. rp is the position K vector of point P in dynamic coordinate system, and the acceleration δf of point P in static coordinate system can be described as:

Tracking of Human Arm Based on MEMS Sensors

K

K

K

K

K

K

δf = ω in × rp + ωin × (ωin × rp ) .

603

(9)

K K K K K where, ωin × rp is the tangential acceleration, ωin × (ωin × rp ) is the normal acceleraK tion. ωin is the angle velocity vector of the dynamic coordinate system relative to the K static coordinate system, ω is the angle acceleration of the dynamic coordinate in

system relative to the static coordinate system. 4.2.2 Motion Acceleration Compensation of Big Arm Detection Module G The output of accelerometer in big arm gesture detection module a1 can be expressed as K G K K K a1 = δf1 + T1aop1 + g + v1 . (10)

K Where, aop1 is the line acceleration of shoulder joint O p1 in reference coordinate K K system Ob , δf1 is the acceleration of big arm lever arm, g is the component of K K K gravity, v1 is the measure noise. δf1 and T1aop1 is namely motion acceleration components, which will affect the observation precision of gesture angle, hence, a compensation is needed. When the arm is moving, the shoulder joint can consider as in static, K namely, aop1 =0, big arm lever arm acceleration can be calculated by Eq(9). The compensate method of acceleration components of big arm gesture detection module can be expressed as: K G G (11) aˆ1 = a1 − δf1 . G where, aˆ1 is the observation value of gravity component of big arm gesture detection module by compensation. 4.2.3 Motion Acceleration Compensation of Forearm Detection Module The accelerometer output of forearm detection module is affected by gravity components, forearm lever arm acceleration, shoulder joint acceleration, measure noise and G the line acceleration of elbow joint aop 2 , which can be described as following

K G G K K K a2 = δf 2 + T2aop1 + T2 aop 2 + g + v2 .

(12)

G According to Eq.(11), the main part of aop 2 is the lever acceleration caused by the rotation of big arm, which can be calculated as: K G aop 2 = T1−1δf op 2 .

K

K

K

K

K

(13)

K

δf op 2 = ω1 × rop 2 + ω1 × (ω1 × rop 2 ) .

(14)

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K K where, rop 2 is the position vector of elbow joint O p 2 in coordinate system O p1 , ω1 is the rotational angle velocity vector of big arm. The compensate method of acceleration components of forearm gesture detection module can be expressed as: K K G G (15) aˆ 2 = a2 − T2T1−1δf OP 2 − δf 2 .

G where, aˆ2 is the observation value of gravity component of forearm gesture detection module by compensation.

""$ !#$ $""$ !#$

""$ !#$ $""$ !#$

$ "$

$ "$

#

#

3

3

$"

$"

""$ !#$ $""$ !#$

#

""$ !#$ $""$ !#$

4

#

4 Before acceleration compensation

""$ !#$

After acceleration compensation

$""$ !#$

Roll (degree)

"

#

5

Fig. 6. The effect of acceleration compensation for big arm

#

5

Fig. 7. The effect of acceleration compensation for forearm

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4.3 Experiments and Results

In order to demonstrate the validity of compensation algorithm, we fix the big arm detection module and forearm detection module upon big arm and forearm, respectively. The arm is in random motion status. The observation effect of gravity vector by accelerometer is justified by following equation

ε = a x2 + a 2y + a z2 − g .

(16)

If ε has a relative big absolute value, the outputs of accelerometers have relative big motion components, and the outputs can’t be used for gesture calculation directly. Figure 6 and figure 7 are the motion acceleration compensation effect of big arm and forearm, respectively. From figure 6 and figure 7, we can see that the peek value of εis decreased from 0.7g to 0.1g by compensation, the motion acceleration components is filtered effectively. In order to study the influence of motion acceleration to the arm gesture observation, we compared the calculated gesture angle using origin acceleration as input and that using compensated acceleration. The results are shown in figure 6b, 6c and figure 7b, 7c. When the arm gesture changes slowly, the results are consistent, while the arm gesture change fast, the results that use origin acceleration as input will produce big error. The big arm gesture error peek is near 20 degree, and the forearm gesture error peek is near 50 degree. If the gesture angle with big error is introduced into the Kalman filter in section 4.1, the arm gesture angle estimation will produce big error. Therefore, the measure precision of arm gesture can be improved by the acceleration compensation method proposed in this paper.

5 The Arm Gesture Movement Track Algorithm Based on Multi-sensors According to the motion characteristic of arm, this paper established the motion model of arm. On the basis of triaxial accelerometer, triaxial gyroscope and triaxial magnetic field sensors, the arm movement track system hardware is designed. The arm gesture track method based on multi-sensors data fusion is researched. The motion acceleration components compensation algorithm is analyzed. The arm movement track experiments show that: when arm move quickly, the existence of arm motion acceleration will cause a big measure error of arm gesture; Measure error peek of big arm is about 20 degrees and that of forearm is about 50 degrees; the proposed compensation method in this paper can filter the motion acceleration component effectively, and then improve the arm track precision.

References 1. Hou, W., Dai, J.: Detection of human upper limb motion gesture based on acceleration sensor. Transducer and Microsystem Technologies 1, 106–108 (2009) (in Chinese) 2. Feng, W.: Design and Implamentation of the Motion Attitude Detection system, Master degree,Chongqing university (2008) (in Chinese)

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3. Ryan, A., Joseph, A.P.: A wireless,compact,multi-user system for interactive dance. In: Proceeding of the 2006 International Conference on New Interfaces for Musical Expression, Paris, France, pp. 134–139 (2006) 4. Liu, X., Zhou, Z.: Design and Test of MEMS Attitude Measurement Unit for Human Motion Tracking. Instrument Technique and Sensor 8, 225–227 (2009) (in Chinese) 5. Zhang, X.: Research on the Key Technologies of Economic Strapdown Attitude Heading Reference System: Master degree. Xi-Dian University, Xi’an (2008) (in Chinese) 6. Gan, S.: Research on the Lever Arm Effect of Speed Matching Transfer Alignment, Master degree. Harbin Engineering University, Harbin (2009) 7. Feng, Z.: Research on 3D hand tracking method, Master degree. Shandong University, China (2006) (in Chinese)

Study on Integration of Measurement and Control System for Combine Harvester Jin Chen1, Yuelan Zheng1, Yaoming Li2, and Xinhua Wei2 1

School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu, 212013, China 2 Key Laboratory of Modern Agriculture Equipment and Technology, Ministry of Education, Jiangsu University, Zhenjiang, 212013, Jiangsu, China [email protected], [email protected], [email protected]

Abstract. Using CAN (controller area network) field bus and iCAN protocol, an integrated measuring and controlling system for combine harvester is constructed in this paper. ARM is used as the processor of host computer. Lower computers are several measuring and controlling modules of combine harvester. Host computer communicates with lower computers through CAN bus. Information between measurement and control modules can be shared so that combine harvester’s whole performance can be improved. LCD on host computer shows working state and values of relative parameters of each module in real-time. Experiment shows that the system can work correctly and reliably. Keywords: Combine harvester, ARM, CAN bus, iCAN protocol, integration.

1 Introduction With supervision and automatic alarm installed in the combine harvester, it is much easier for operator to know the working condition of some running parts and adjust machine’s operating parameters in time. So the fault frequency of combine harvester can be reduced, MTBF (the Mean Time Between Failure) can be longer, and the purpose of improving the whole machine’s work efficiency can be realized [2]. But at present, the monitoring and controlling objects of combine harvester are mainly walking speed, threshing cylinder speed, state of hydraulic system, etc. Each parameter is basically handled separately. Connections between one another aren’t so close [3], and the information can’t be shared [4]. Therefore subsystem’s function will be influenced and lead to the degradation of the whole machine’s performance. To solve this problem, the integration technology is studied in this paper, taking CAN bus as tie. The system is based on the relatively independent modules designed by our studying team. They are Load Feedback Control Module, Real-time Loss Quantity Detecting Module, Electric System Self-checking Module, Mechanical System Fault Monitoring Module, and Hydraulic System Fault Monitoring Module. The system is based on the structure of working process intelligent monitoring and controlling for combine harvester [1], aiming to realize information sharing and improve coordinative relations between parameters effectively. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 607–614, 2011. © IFIP International Federation for Information Processing 2011

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2 The System’s General Structure The general structure of intelligent monitoring and controlling system for combine harvester is shown in Fig.1 [1]. The system consists of host computer and five measurement and control modules as lower computers. Lower computers realize the function of measuring and controlling of subsystems. Host computer is used for parameter setting, communication and display, etc. Lower computer modules and their functions are as follows: 1)

2) 3) 4)

5)

Load Feedback Control Module: Control the walking speed of combine harvester in real time according to parameters like feed rate, threshing cylinder speed and so on. Real-time Loss Quantity Detecting Module: Detect cleaning loss and entrainment loss real-timely. Electric System Self-checking Module: Detect working status of electric components like battery and generator. Mechanical System Fault Monitoring Module: Offer the early warning and fault alarm for mechanical conveying appliance’s jam fault based on parameters like threshing cylinder speed, crop screw conveyor speed, etc. Hydraulic System Fault Monitoring Module: Detect parameters like oil temperature of cooler inlet and outlet, HST forward pressure and recession pressure in real time.

Functions of host computer system and each module of it are as follows: 1) 2) 3)

ARM: The processor of host computer. LCD: Display data delivered from lower computers properly. Keyboard: Input parameters.

Fig. 1. System’s general structure

3 The System’s Realization 3.1 ICAN Protocol CAN bus, developed in 1983 by Bosch Company of Germany for application in cars, is a serial communication network which can support distributed control and real-time control effectively. CAN bus is of extremely high reliability, high data transmission

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rate, long transmission distance and good real-time performance. It is especially suitable for computer control system in cars, interconnection of industrial field monitoring devices under circumstances of harsh temperature, strong electromagnetic radiation and large vibration [5]. Considering combine harvester’s harsh working conditions and its own characteristics, CAN field bus is used in this system. ICAN is the abbreviation of “Industry CAN-bus Application Protocol”, one of the latest application layer protocols of CAN-bus. It is easy to understand, realize, and has properties of real-time and reliability. ICAN protocol uses the way of connection and management that basically similar to CANopen and DeviceNet protocol. The complicated handshake management and resource allocation etc are deleted. Nods on CAN bus is managed in the way of predefined data combination. ICAN protocol’s meeting surface is wide, application mode is flexible. It is particularly suitable for distributed data control network under different industrial environment conditions. Only the extended frame format of CAN message is used in iCAN protocol. It regulates 29 bytes ID and data part of CAN message. ICAN protocol identifier is allocated as shown in Table 1. Table 2 shows how the data part of frame is allocated [6]. Table 1. ICAN message ID allocation

Frame ID

ID28

ID27

0

0

ID26 ~ID21 Source node ID

ID20

ID19

0

0

ID18 ~ID13 Target node ID

ID12 ACK

ID11 ~ID8 FUNC ID

ID7 ~ID0 Source ID

RTR 0

Table 2. Allocation of data part of iCAN frame iCAN frame data byte

Function

Byte 0

Segment flag

Byte1(Length Flag, Error ID )

Parameters relevant to FUNC ID

Byte2~7

Take cooler inlet oil temperature delivered from Hydraulic System Fault Monitoring Module to host computer as an example: 1) 2) 3) 4) 5) 6) 7) 8)

Source node ID: 0x03(Nod number of Hydraulic System Fault Monitoring Module) Target node ID: 0x01(Nod number of host computer) ACK: 1(Response frame, no need to be answered) FUNC IN: 0x01(Write port continuously) Source ID: 0xb4(Use this number to represent cooler inlet oil temperature) DLC: 4(Data length) Byte 0: 0(No Segmentation for data in this frame) Byte1~Byte4: oil temperature

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3.2 System’s Software Design The software of this system is designed by Embedded Visual C++, running under Windows CE circumstance. The host computer software is an important part of the whole measurement and control system. It is mainly used for realizing man-machine interface, receiving and displaying data, communicating with lower computers via CAN bus, setting parameters and showing working state of lower computers etc. Software is designed in the way of modularization. Main modules consisted in this software are as follows: 1)

2)

3)

4)

Main interface module: Get into main interface firstly since the system starts running. Main interface gives three buttons, which are “Self-Check”, “Set Parameters” and “Real-Time Display”. Click one of these three buttons to get into the corresponding interface. Self-check at the beginning interface module: After getting into self-check at the beginning interface, host computer will broadcast message to lower computers asking for their self-check status of the beginning. Meanwhile Timer 1 is activated. Host computer will do the process according to the information returned from lower computers: if the self-check state is normal, the edit control that represents this module will have a background color of green and show word of “Normal”; if the self-check state is fault, the background color of this edit control is red and word of “Fault” is showed. When Timer 1 reaches its time-out value and host computer still hasn’t received self-check message from a lower computer, this module will be regarded as fault. The button relevant to this module showing words of “Error Information” will be able to be seen which is unseen before. Click this button, error code and information will be displayed. Controls of subsequent interfaces relative to this module are forbidden to use. If all of the self-check states of the five lower computer modules are normal finally, the “Self-Check” button on the main interface will be hidden. Otherwise it will keep showing for checking fault information in later time. Parameter setting interface module: In this interface, operator can modify parameters about hydraulic (such as cooler inlet and outlet oil temperature) and loss (1000-grain weight and correction coefficient). After operator input new parameter values and click “OK”, host computer will store them and send them to relevant lower computers. Or else, the previous values will be maintained. Real-time display interface: After getting into real-time interface, host computer will broadcast message to lower computer modules for their real-time state data, including the module’s current working status and relevant parameters’ real-time values. Timer 2 is activated in the mean time. Since then host computer will broadcast to lower computers for real-time status data in certain intervals repeatedly.

Host computer will show the parameter’s real-time value in the relevant edit control after received it. If host computer receives a lower computer module’s current working state, it will process it based on information received. Take Mechanical System Fault Monitoring Module as an example: if the current state is normal, then its edit

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control will have a green background color and show characters of “Mechanical System is normal”; if current working status is early warning, the edit control has a background color of yellow and shows characters of “Mechanical System is giving early warning”; the background color of the edit control will be red and characters of “Mechanical System is fault” will be displayed if current working state is fault. When a lower computer module’s working state at present is fault, fault code and information can be seen by clicking the edit control that represents this module.

4 Experiment 4.1 Hardware Connection of Indoor Experiment ARM on the Embedded Experiment Development System OURS-PXA270-EP produced by Beijing Owles Electronic Technology Limited Company is taken as host computer in laboratory. PXA270-EP is a processor based on INTEL XSCALE PXA270, supporting Windows CE 5.0 operating system. There are LCD, serial ports and keyboard in the experiment box. So it can meet the demand of the experiment. Lower computers are Load Feedback Control Module, Real-time Loss Quantity Detecting Module, Electric System Self-checking Module, Mechanical System Fault Monitoring Module and Hydraulic System Fault Monitoring Module and Hydraulic System Fault Monitoring Module. CSM100, integrated with microprocessor, CAN-bus controller, CAN-bus transceiver, DC-DC module and high photoelectric isolation, is an embedded module used for converting from UART (serial port) to CAN. It is produced by Guangzhou Zhiyuan Electronic Limited Company. It can be embedded in device with UART port conveniently. Without changing the previous hardware structure, the device can get CAN-bus communication interface, realizing data communication between device with UART and CAN-bus network. Connect serial port 1 of the experiment box with CSM100, and CSM100 with lower computer measurement and control modules. Then the whole indoor experiment

Fig. 2. Devices used in experiment

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system of measurement and control integrated system for combine harvester is constructed, and the indoor communication experiment can be taken. Fig. 2 shows the devices used in the experiment. 4.2 Indoor Experiment Application programs of this system are based on dialog, including the main application program class, serial port class, self-check at the beginning dialog class, hydraulic system parameter setting property page class etc. To avoid main thread being blocked, both reading serial port and writing serial port possess a single thread. First, use UART debug assistant to make sure that each module of this system, including upper computer and all of the lower computers can respectively communicate correctly. Then do the experiment to test the whole system’s performance. The result of the experiment is shown as followed. Main interface, self-check at the beginning interface, parameter setting interface and real-time display interface are shown in Fig. 3, Fig. 4, Fig. 5, Fig. 6 and Fig. 7 respectively. Main interface (shown in Fig. 3) is activated when system start running. Click “Self-Check” button to get into self-check at the beginning interface (shown in Fig. 4) to see self-check states of measurement and control modules and fault information when a module is in fault.

Fig. 3. Main interface

Fig. 4. Self-check at the beginning interface

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Get into parameter setting interface by clicking “Set Parameters” button. Working parameters of hydraulic and loss quantity can be modified here (shown in Fig.5 and Fig.6 respectively).

Fig. 5. Hydraulic parameter setting interface

Fig. 6. Loss parameter setting interface

Fig. 7. Real-time display interface

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Click “Real-Time Display” button to get into real-time display interface (shown in Fig. 7). Each lower computer module’s current working state and relevant parameters’ real-time values are showed here. Click its edit control to see fault information when a module’s current working state is fault.

5 Conclusion The experiment shows that this system can realize communication in real-time between host computer and lower computers. LCD on host computer shows working state and parameters of several objects of combine harvester correctly and properly. And the whole machine’s performance is increased by shared information between modules. And due to CAN bus’s characteristics of high reliability and real-time, the system’s reliability is greatly improved. It has a very good application prospect. Acknowledgments. This work was supported by the Digestion Longitudinal Flow Intelligent Controlling for Rice and Wheat Harvesting Technology & Equipment Research (2010AA101402) during the Eleventh Five-Year Plan Period.

References 1. Wei, X.H., Li, Y.M., Chen, J., Song, S.P., Gu, J., Zuo, Z.Y., Ni, J.: System Integration of Working Process Intelligent Monitoring and Controlling Devices for Combine Harvester. Agricultural Engineering Journal 20, 56–60 (2009) (in Chinese) 2. Zhang, S.H., Chen, J.: A Study on the Auto-alarm System of Combine Harvester. Agricultural Mechanization Study 8, 68–73 (2002) (in Chinese) 3. Wang, J.X.: Automotive Electronics Technology Integrated Control System Based on CAN Bus. Harbin Industrial University Journal 5, 811–814 (2006) (in Chinese) 4. Li, J.L., Yu, L., Liu, H., Zhou, H.L.: State-of-Art and Prospect on Combine Monitor System. Modern Agricultural Equipments 12, 46–48 (2005) (in Chinese) 5. Wang, L.M., Xia, L., Shao, Y., Yan, X.L.: The Design and Application of CAN Field Bus System. Electronic Industrial Press, Beijing (2008) (in Chinese) 6. Zhou, L.G.: ICAN Field Bus Principle and Application. Beijing Aeronautics and Astronautics University Press, Beijing (2007) (in Chinese)

Study on Jabber Be Applied to Video Diagnosis for Plant Diseases and Insect Pests Wei Zhang*, JunFeng Zhang, Feng Yu, JiChun Zhao, and RuPeng Luan Agriculture and Forestry Academy of Beijing; Beijing 100097; China

Abstract. Experts diagnose plant diseases and insect pests via long-distance video is a new and effective way for prevention and cure. Jabber based on XMPP. So there are good communication, standardization and expansibility in Jabber. This thesis gave a method which was long-distance video diagnosis based on Jabber. First, VFW was used to capture video of diseases and insect pests. Second, H.264 compressed the video. Third, compressed video was transmitted and controlled by Jabber. Finally the compressed video was decoded and showed in other end. Client used Delphi as developing tool and Sever used Jabber2.0 realized this design. Keyword: video diagnosis, Jabber, H.264, Plant Diseases and Insect pests.

0 Foreword Plant diseases and insect pests is one of the pivotal factors which influence yield and quality of crop. Because of limit of knowledge, farmers are difficult to judge exactly the disease and insect pests of plant. But farmers can transmit symptom of disease and insect pests to expert and talk face to face by long-distance video. Experts give the answer and help farmer to prevention. This diagnosing mode can accelerate tradition agriculture to modern agriculture and improve agricultural modern construction. Vision compression technique and transmission technique are the keys of long-distance video. Now there are two organizations who have established vision compression standard. One is International Organization for Standardization (ISO) who established MPEG-1, MPEG-2, MPEG-4 etc. The other is International Telecommunications Union (ITU-T) who established H.261, H.263, H.264 etc. the chief advantage of H.264 is high compression ratio. In the condition of coordinative images, compression ratio of H.264 is about 1.5 2 times of MPEG4. Therefore long-distance vision diagnosis will take advantage of H.264[1,2]. Because TCP/IP can’t offer real-time vision service, vision transmission ordinarily adopt RTP/RTCP in internet. But there are disadvantages of RTP/RTCP, such as more bottom technology, complicated programming, penetrating through firewall faintly. Jabber is an open source software which was developed by Jeremie Miller. Jabber bases on XML and uses JabberID which is similar to email. So it has good expansibility, standardization, capability to penetrate through firewall. As

～

*

Zhang Wei (1978 - ), Hu Nan Province, assistant researcher, E-mail: [email protected]

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 615–622, 2011. © IFIP International Federation for Information Processing 2011

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well as Jabber provided with character communication, online judgement and white board etc. Furthermore, it was easy to communicate with Instant Messaging software. This thesis has studied long-distance video diagnosis based H.264 and Jabber.

1 Principle of Long-Distance Video Diagnosis in Plant Diseases and Insect Pests Based on Jabber Framework of Jabber differs from the single-framework of ordinary instant messaging software. Jabber is multi-framework which likes email. All instant messages must be transmitted by server. The Jabber userID is called JID which is similar to email address. For example, in the JabberID [email protected], li was the user account, aaa.com is the server of user account. The process of A: [email protected] sends the message to B: [email protected] can be described as follow. The first, user A send the message to sever aaa.com. Then sever aaa.com sends the message to server bbb.com of user B. The last, server bbb.com transmits the message to user B. Message exchange between server A and sever B depend on “Etherx” module. The process as Fig.1.

Fig. 1. Message transmission process of Jabber

Jabber bases on XMPP which is one of XML. So it is easy to custom the application and increase functions. And it is easy to penetrate through firewall. The essential concepts of XMPP are element, name, attribute, namespace. One Jabber conversation is composed of two parallel XML. One XML is from client to server. The other is from server to client. XML steam begins with <stream> tag and end with tag. In the stream lifecycle, the initialized entity can send large numbers of XML which can build and exchange structure information. The supreme advantage of H.264 is high compression ratio. So it adapt to internet vision transmission. Now there are three mainstream open source coders of H.264 which are JM, X264, T264. JM is official test source code. But coding is terribly

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complexity and low practicability. X264 was an open source item which was originated by students from center graduate shool of Ecole Centrale Paris. In the condition of playing down coding capability not obviously, X264 is much simple than JM. T264 was similar with X264 which was developed by Chinese vision coding free organization. So T264 suited Chinese environment. This thesis used T264 to compress vision.

2 Design and Realization 2.1 Two Data Transmission Modes Jabber system uses XML transmit Data between entities. XML can transfer abundant information. So character consultation, file transmission, white board, voice communication, vision communication are designed in this system. The information of voice and vision is great which will use up lots of resource. Other information such as character information, control information use up little resource. There are two transmission modes for them. (1) Server transmitting mode Server transmitting mode is used for non voice or vision data such as character information, control information etc. This mode can transfer and save information, estimate users on line or not. The security can be strengthened via adding firewall, data encrypt, identify etc. Sever transmitting mode as fig.2.

Fig. 2. Server transmitting mode

(2) Non server transmitting mode Because voice and vision data was great which will use up lots of resource. If server transmitting mode is used to transmit them, there will be great pressure in server. It will influence the net transmission capability so much as paralysis. So non server transmitting mode is used to transmit voice or vision data. In non Server transmitting mode, control information of voice and vision are transferred by sever. Voice and vision data are transmitted by UDP directly. Non server transmitting mode as fig.3. 2.2 Two Diagnosis Communication Flow There are two communication flows in practical application of long-distance vision diagnosis. One is one to one diagnosis flow. In this flow expert only communicate with

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one user and user can only see one expert. The other is one to multi diagnosis flow. Experts can communicate with multi users and user can see multi experts.

Control data

Control data

server

User 1

User 2 Voice and vision data Fig. 3. Non sever transmitting mode

One to one diagnosis flow as fig.4. off

leave words

A

Login

online？

Non voice or vision data

Sever transmit

B

on

Control data Voice or vision data

Vioce or vision data

Fig. 4. One to one diagnosis flow

One to multi diagnosis flow as fig.5. Non voice or vision data A

Login

Server transmit

Control data Voice or vision data Vioce or vision data

Fig. 5. One to multi diagnosis flow

B C D . . .

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2.3 System Realization 1. Client realization Client realization uses layers and module theory. There are three layers in client which are network layer, core layer and GUI layer. Net layer is the bottom layer which severs for communication between Jabber client and Jabber server. Net layer depends on UDP protocol. Core layer is the foremost layer which takes charge the Jabber protocol realization. GUI is the figure layer facing to user. Users take GUI layer to communicate. Layers design as fig.6.

Fig. 6. Three layers design of client

According to layers design, five modules are designed for client. Modules frame figure as fig.7.

Jabber client

XML

XML Stream

Link disposal sever

XML analysis

XML

Jabber element message disposal

Event disposal User input

GUI

Fig. 7. Modules frame figure of client

update

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Link disposal module is used to create TCP socket to Jabber server. XML information exchange between client and server will use this socket. XML analysis module takes charge to analyze import XML information. Message disposal takes charge to dispose XML element. Different XML element has different disposal program. For example, <message/>, <presence/>, respectively have corresponding disposal program. GUI module presides over user communication. Event disposal module transfers events to XML elements. These XML elements send to sever via link disposal module, such adding friendship, sending instant messaging, update user current state etc. 2. Server realization Sever developed with Java Language. Sever is the core of control and transmission. Jabber common modules were used in the process of developing sever. These Jabber common modules include conversation management, client-sever communication, sever-sever communication, DNS parsing, user authentication, user enroll, database query, off-line users storage, filtration enactment, group chat, system log etc.

3 Experiment In the process of development, two experiments have been executed. One is the communication experiment. Its aim is to test the communication function of character, voice and vision. The other experiment is loading experiment. Its aim is to test the loading capability. Fig.8 is the result of communication experiment.

Fig. 8. Communication experiment

Fig.9, Fig.10 is the result of loading experiment. In the loading experiment, there are 41 landing users and 7 online visions. The results of experiment indicate that the theory of this thesis can transmit character, voice and vision availably. Furthermore the system has good loading capability.

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Fig. 9. Impression of loading Capability experiment

Fig. 10. Net capability of loading capability experiment

4 Conclusion According to the characteristics of transmission and diagnosis of vision, this thesis uses combination of Jabber and H.264 to realize long diagnosis of plant diseases and insect pests. The experiment results have showed that the communication capability and loading capability were good. Corresponding software were given. Jabber is an open source software. Excellent instant message software can be developed based on Jabber. Moreover Jabber uses XMMP protocol which can communicate with existing instant message software. Getting along with development of 3G, integration of 3G vision diagnosis and Internet vision diagnosis is the centre of next study.

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References [1] Li, H., Sheng, S.Y.: Video Tranmnission Technology Based on H. 264. Computer & Digital engineering 34(2), 44–46 (2006) [2] Zhang, J., He, Y.: Performance and complexity joint optimization for H.264 video coding. In: Proceedings of the 2003 International Symposium on Circuits and Systems, vol. 13(7), pp. 20–23 (2003) [3] Kai, M.: XMPP security system analysis. Communication technique 5(2), 101–105 (2003) [4] http://baike.baidu.com/view/188363.htm [5] http://www.jabber.org [6] XMPP Standards Foundation. Jabber Enhancement Proposals: File Transfer, JEP – 0096 (October 2006) [7] Wenger, S.: H.264 / AVC over IP. IEEE Transactions on Circuits and Systems for Video Technology 13(7), 645–656 (2003) [8] Kim, C.: Real-Time Video Coding. IEEE Transactions on Consumer Eletronics 32(4), 417–426 (1999) [9] Xiaohu, M.: Multimedia data compress standard and realization, pp. 126–130. Tsinghua University Press, BeiJing (1996) [10] Sullivan, G.J.: Rate-Distortion Optimization for Video Compression. IEEE Signal Processing Magazine 15(6), 74–90 (1998)

Study on Pretreatment Algorithm of Near Infrared Spectroscopy Xiaoli Wang and Guomin Zhou Agricultural Information Institute of CAAS [email protected], [email protected]

Abstract. Pretreatment of near-infrared spectral data is the basis of feature extraction, quantitative and qualitative analysis and establishment of models, it plays a significant role in obtaining the data and get reliable results. The purpose of the paper is compared the advantages and disadvantages of the S-G, derivative and multiple algorithm methods of spectral preprocessing through the example of apple leaves. S-G algorithm can smooth the data relatively better, but we must according to the specific circumstances of the case while chose the width of the window and the order of polynomial; Kernel smoothing is better than S-G in two-ends data processing, but its processing speed is slower than S-G. Derivative algorithm can get more stable reflectance, but it is sensitive to noise, so it need to be used with the smoothing algorithm. Multiple scatter correction can be used effectively to eliminate the translation and offset of baseline. All of above algorithms have been applied in the system of near infrared spectroscopy processing system of leaves and satisfactory result was obtained. Keywords: Near Infrared Spectroscopy, Spectral pretreatment, Smoothing algorithm, Derivative algorithm, Multiple scatter correction algorithms.

1 Introduction Near infrared spectroscopy is electromagnetic waves whose wavelength is between 780 ~ 2500nm, and it is intermediate between visible light and infrared, NIR refers primarily to low-energy electronic transitions and the clusters containing hydrogen atoms. Various objects’ components information can be obtained through the near-infrared spectroscopy. However, when we survey and evaluate the components information of the objects, the measurements may be quite different for the difference of the weather, environment, humidity, temperature and human factors, as well as the existence of dark current. Near infrared spectroscopy is mainly the spectral information of the measured object, but also includes all sorts of interference and noise from outside. Therefore, the spectral data must be preprocessed before establish the model to reduce noise and interference information, lay the roots for simplifying the operational process of modeling and improve the accuracy of the analysis. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 623–632, 2011. © IFIP International Federation for Information Processing 2011

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2 Materials and Methods 2.1 Experimental Materials We used FieldSpec 3 portable spectroradiometer as the instruments which can be applied to many areas; it’s spectral range: 350-2500nm; data interval: 1nm; wavelength accuracy: + /-1nm; It works through the measurement of light reflectance, transmittance, emissive or radiation rate. Samples are collected from fruit Research Institute of Liaoning Xingcheng, 60 apple leaves are collected and its spectral information are collected, in the dark room with black cloth as the background, artificial lighting 40 samples are used to calibrate and 20 samples used to predict.

Fig. 1. Near infrared spectroscopy of apple leaves

2.2 Data Smoothing Algorithm 2.2.1 Savitzky-Golay Smoothing Algorithm The main idea of Savitzky-Golay [1-3] (later referred to as S-G) filter algorithm is that use higher order polynomial to approximate fit the fixed number of data (also called the active window). For each point i, use the least square method, fit all the data of this activity window to a polynomial expression. Then replace the original value with the value of polynomial (gi) at point i. When the window moves to the next point i+1, we use least-squares fitting algorithm to fit the original data rather than the data of the polynomial in the new window. Using the S-G algorithm requires that horizontal axis xi have a uniform spacing (xi+1-xi≡ Δ x). The core formula of the S-G algorithm:

gi =

nr

∑c

n= − nl

f

n i+n

，

Study on Pretreatment Algorithm of Near Infrared Spectroscopy

cn = eM +1 ( AT A) −1 AT T

（− n

l

625

≤ n ≤ nr );

Here fi is the original data, gi is the data after smoothing, eM +1 is M+1 dimensional unit vector, nr is the number used to the right, i.e., later than i, nl is the number of points used to the left of a data point i, i.e., earlier. ⎡ ( − nl ) m ⎢ m ⎢− (nl − 1) ⎢ # ⎢ A=⎢ 0 ⎢ # ⎢ m ( n − ⎢ r 1) ⎢ nm ⎣ r

(− nl ) m −1 " − nl 1⎤ ⎥ m −1 − (nl − 1) " − (nl − 1) 1⎥ # " # #⎥ ∈ R ( nl + nr +1)×( M +1) ⎥ 0 " 0 1⎥ # " # #⎥ ⎥ m −1 ( nr − 1) " (n r − 1) 1⎥ m −1 nr " nr 1⎥⎦

2.2.2 Kernel Smoothing Algorithm [4] Same as S-G algorithm, suppose the data after smoothing is g(i), window width is h, weighted function is S(t), then the expression of fitting value in time t (such as the spectrum reservation time) is: n

g (t ) = ∑ S j (t ) y ( j ) j =1

The weighted function proposed Gasser and Muller is: t *( j )

1 u −t S j (t ) = ker n( ) ∫ h t* h ( j −1)

In the two formulas above, kern (x) function is the core of smooth; it has three different weighted functions: Uniform function:

⎪⎧0.5 ker n( x ) = ⎨ ⎪⎩0

x ≤1 otherwise

Quadratic function:

⎧⎪0.75(1 − x 2 ) ker n( x) = ⎨ ⎪⎩0

x ≤1 otherwise

Gaussian function:

ker n( x ) = (2π )−1/ 2 exp(−u 2 / 2)

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2.3 Derivative Algorithm [5]

Usually, derivative spectrum use different methods to approximate calculate: First derivative spectrum:

ρ ' (λi ) =

[ ρ (λi +1 ) − ρ (λi −1 )] 2Δλ

Second derivative spectrum:

ρ '' ( λ i ) =

[ ρ (λi + 2 ) − 2 ρ (λi ) + ρ (λi − 2 )] 4Δλ2

2.4 Multiple Scatter Correction [6]

The concrete account step of multiple scatter correction is: First of all, calculate the average spectrum of all samples as a standard spectrum; secondly, fit the spectral data for each sample with standard spectra through one-dimensional linear regression, and then get the offset (regression coefficient) and translational (regression constant) of each spectrum relative to standard spectrum; finally, the original spectra data of each sample minus regression constant then divided by regression coefficient and you get spectrum after correction. Specific process is as follows: (1) calculate the average spectrum: n

Ai , j =

∑A

i, j

i =1

n

(2) the linear regression:

Ai = ai Ai j + bi (3) multiplicative Scatter Correction:

Ai ( msc ) =

( Ai − bi ) ai

3 Experiment and Analysis We use MATLAB software to process data. After programming and debugging, spectral data of apple leaves were processed and compared and we obtained the following results:

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3.1 Smoothing Figure 2 is the original spectral and the spectrum which was treated by different S-G smoothing of parameters (except the original spectrum, in order not to overlap spectrum, ordinate value was added by 0.1 in proper order). We find that the original spectrum, in the 1000nm and 2000-2500nm, has relatively large noise. The spectrum also has large noise after S-G (9, 9, 1) processing. After S-G (99, 99, 1) processing, the smoothing effect is obvious, but many spectral information is lost. With the increase of the fitting polynomial order, the noise is also increased. (55, 55, 1) is the relatively ideal parameter. In practice, parameter selection is very important and we have to analyze specific issues.

Fig. 2. S-G smooth

Fig. 3. Kernel smooth

Figure 3 is the spectrum which was processed by kernel smoothing (except the original spectrum, in order not to overlap spectrum, ordinate value was added by 0.1 in proper order). We find, compared with the S-G algorithm, kernel algorithm has a better smooth result at the both ends of the data (Note the smoothing effect in about 400nm in Figure 2 and Figure 3). The disadvantage of kernel algorithms is the high time complexity. When the data is big, the processing speed is slow.

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Standard deviation (Std Dev) is a measure of the degree of data dispersion, indicated the deviation of the data from the mean, it is also a target of variable stability. Range is the difference between the maximum and minimum data which described the dispersion of data. The range is bigger, the dispersion of data is bigger. Table 1. The comparison of the raw data and the data which was processed by smoothing algorithm the raw data

（55,1）

S-G

kernel(55, 'qua')

kernel(55, 'uni')

Std Dev

0.2937649

0.2919143

0.2887471

0.2861456

Range

0.8410000

0.8410000

0.0833749

0.8071000

By the comparison of Table 1, we can find, the discreteness of the data after S-G smooth is smaller than the original data; by the same smoothing window, kernel smoothing effect is better than the S-G smoothing; the effect of smooth is better when the kernel weighting function is not uniform but quadratic; and after repeated treatment, we find that variance and range change a little after 55 smoothing window. Thus, we selected kernel smoothing algorithm (the window width is 55, the polynomial weighted function is quadratic function) as the smoothing method of apple spectral data. 3.2 Derivatives Figure 4 and Figure 5 are the derivative spectrum of original spectrum, it has obvious absorption peaks, but without any smoothing, and very sensitive to noise, so, we have to smooth the spectral data before or after derivative. Figure 6 and Figure 7 are data which are smoothed by kernel after derivative, the spectral peaks are more obvious, and so it can do more complex spectral analysis work, and provide a good basis for the establishment of the stable model for the future. And compare with second derivative spectra, we find that the first derivative is relatively less sensitive to the noise impact

Fig. 4. The first derivative spectra of the original data

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Fig. 5. The second derivative spectra of the original data

Fig. 6. Kernel smoothing after the first derivative

Fig. 7. Kernel smoothing after the second derivative

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Fig. 8. The original near infrared spectroscopy of apple leaves

Fig. 9. After S-G(55,55,1)

Fig. 10. After multiple scatter correction

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than the second derivative. Therefore, the first derivative can be used for spectral preprocessing. 3.3 Multiple Scatter Correction Figure 8 is a multiple spectrum of apple leaves, the impact of the apple leaves water, measuring environment and so on makes the baseline’s translation and offset of the spectrum more serious and after processed by the S-G algorithm the data can be smoothed but can not be corrected(figure. 9). Figure 10 is spectral image after corrected by the multiple scatter correction, we find that the data after corrected by the multiple scatter correction have relatively consistent baseline, so it can improve the accuracy of the follow-up modeling.

4 Discussion and Outlook After the application of the above algorithm to the processing system of Near Infrared Spectroscopy in leaves, we found that: 1. S-G smoothing can be better to smooth the spectral data, while the window width and degree of polynomial selection is very important, the more data points within the window, the more seriously the spectral resolution drops, also the more severely the spectrum distorted; But if too few data points, the smoothing result is not satisfied; the higher polynomial fitting degree, the larger data noise and smoothing effect is not very good, but the low-order polynomial fitting can not fit the data very good; so, the selection of the window width and the degree of polynomial need to be based on the actual situation. There, we choose the (55, 55, 1) as the smooth parameters for apple spectral data. 2. As the same parameters, after the kernel smoothing algorithm processing and then modeling analysis, the predicted result is more accuracy than S-G, but the time complexity is higher, therefore, we should select the smoothing algorithm according to the size of the data in practice. 3. Derivative algorithm processing results are not very satisfactory. A cause of this phenomenon is that the absorption peaks are clear, but its relatively narrow peaks are not conducive to model diagnosis. 4. Multiple Scatter Correction can improve the modeling accuracy better, and the accuracy of the results can greatly improve. In summary, we can get the following conclusions: The suitable pretreatment for apple leaf spectral data was found, and provided an important reference for the establishment of models. But for the difference of the apple leaves’ physical and chemical properties, spectroscopic may show some differences, so when we establish the models, we have to change the data preprocessing method at any time according to the different data.

References [1] Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (2002)

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法の大ざっぱな説明

[2] Savitzky-Golay [EB/OL], http://www.empitsu.com/pdf/sgd.20080718.pdf (July 18, 2008) [3] Gorry, P.A.: General Least-Squares Smoothing and Differentiation by the Convolution (Savitzky-Golay) Method. Anal. Chem. 62(6), 570–573 (1990) [4] Wand, M.P., Jone, M.C.: Kernel Smoothing. Chapman and Hall/CRC (December 1994) [5] Ni, Z., Hu, C.-q., Feng, F.: Progress and effect of spectral data pretreatment in NIR analytical technique. Chin J. Pharm. Anal. 28(5), 824–829 (2008) [6] Lu, Y.j., Qu, Y.l., Feng, Z.q., Song, M.: Research on the Method of Choosing Optimum Wavelengths Combination by Using Multiple Scattering Correction Technique. Spectroscopy and Spectral Analysis 27(1), 58–61 (2007)

Study on Rapid Identification Methods of Transgenic Rapeseed Oil Based on Near Infrared Spectroscopy Shiping Zhu*, Jing Liang, and Lin Yan College of Engineering and Technology, Southwest University, Chongqing, China [email protected], [email protected], [email protected]

Abstract. A rapid identification method of transgenic rapeseed oil based on near infrared spectroscopy was proposed. As transgenic food attracted more and more attention and in order to meet the requirement about labeling transgenic food, an accurate, fast, easy, efficient and low cost detection technology is needed. In this study, 117 rapeseed oil samples (including 64 non-transgenic rapeseed oil and 53 transgenic rapeseed oil samples) were used as test material. Principal component analysis (PCA) and discriminant partial least squares (DPLS) were applied to classify transgenic and non-transgenic rapeseed oil. Firstly, the paper studied the plot of principal component scores for the NIR spectra of transgenic and nontransgenic rapeseed oil. The result was that Non-transgenic and transgenic samples can be broadly distinguished in principal component space. Secondly, 35 samples were used to build DPLS identification model, and the other 82 samples as prediction set samples were identified by the model. The overall correct identification rate acquired was 96.34%, in which the correct identification rate of non-transgenic rapeseed oil was 95.56% and the one of transgenic rapeseed oil was 97.30%. The results showed that the attempt to discriminate transgenic rapeseed oil using NIR is feasible and excellent classification can be obtained by combining DPLS method. Keywords: Near infrared spectroscopy, DPLS, Transgenic food, Rapeseed oil, Rapid identification.

1 Introduction Transgene means that implant the gene which extracting from another organism into an organism via transgenic biotechnology. Transgene involves animal and plant, whereas transgenic plant is discussed more at present. The food adopted transgenic plant as raw material is called Transgenic food [1] or Genetically Engineered food [2]. With the rapidly development of biotechnology, transgenic plant have been cultivated in abundance and transgenic food, such as soybean oil, rapeseed oil and maize oil, have entered the daily life of people. According to reports, 90 percent of the edible oil sold on markets is transgenic oil at present and the consumption of oil per capita every year is up to 13kg in some metropolises in China. Consequently the security of *

Corresponding author. E-mail address: zhu_s_p @sina.com

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 633–640, 2011. © IFIP International Federation for Information Processing 2011

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transgenic food has increasingly captured the highly attention from governments, non-governmental organizations, academia and even ordinary people [3]. Since 1900, European Union issued over 10 laws about safety administration of GMO (short for genetically modified organism), and required labeling GMO as well as its production. Beginning in 1998 EU prescribed the mandatory label for GM food. In China, for protecting lawful rights of consumer, State Council issued Biosafety Administration Regulations on Agricultural GMO in 2001 and Ministry of Agriculture issued Labeling Administration Regulations on Agricultural GMO in 2002[4]. With the increasing demand of labeling transgenic food, the techniques for detection and identification of transgenic food are improving constantly and the methods are various. At present, the qualitative and quantitative detection for transgenic elements in most of food are available. The detection methods of transgenic food include two broad categories, viz. the qualitative detection for exogenous gene and foreign protein. The detection methods for exogenous gene include Polymerase Chain Reaction PCR, semi nested PCR, nested PCR, Multip-lex PCR, DNA blotting etc. The quantitative detection methods for exogenous gene include quantitative PCR, PCR-ELISA method, gene chip technique. The detection methods for foreign protein include Western blotting, enzyme-linked immunosorbent assay (ELISA), Latera Flow, Lateral Flow Strip etc. [5-6] The various methods above have different traits. But with the intensified research for transgenic produce at home and abroad as well as the increasing detection demand for transgenic produce, a strong demand for detection technique which is more accurate, rapid, easy to use, efficient and of low cost, has generated. In the field of quality testing of agricultural products, near infrared spectroscopy analytical technology, as a new method, can make rapid and accurate analysis or detection for the element or quality of chemical products, medicament, food, agricultural products etc. Compared with traditional chemical analysis methods, it has advantages such as rapid (analytic time <1min), accurate, no chemical pretreatment for samples, nondestructive and so on. At present, introducing NIR analytical technology into rapid detection of transgenic food is the cutting edge topic on detection method study of transgenic food. [6.10.11] S. A. Roussel et al in Grain Quality Lab of Lovwa Sate University distinguished Roundup Ready soybeans from conventional soybeans using NIR. 95% accurate classification for 20 samples of conventional soybeans and 84% accurate classification for 19 samples of Roundup Ready soybeans were obtained [12]. Ronald J. F. J. Oomen et al studied the discrimination of transgenic potato using NIR principal component space [13]. Interiorly, Rui Yukun et al identified transgenic maize and their parents using back propagation (BP) algorithm by NIR [14]. Xie Lijuan, Ying Yibin et al applied NIR spectrometer to discriminate transgenic tomatoes and their parents [15-17]. In 2008, Xie Lijuan, Ying Yibin et al used Vis/NIR spectrum diffuse reflection method to perform rapid and nondestructive qualitative analysis for transgenic tomato leaves and non -transgenic ones. The accurate classification was 89.7% [18]. In spite of Rui Yukun et al had measured the erucic acid and Glucosinolate content of transgenic rapeseed and their parents by NIR spectrometer [19], they hadn’t identified transgenic rapeseed and their parents. Besides, at home and board there isn’t any report on the research that whether rapeseed oil edible is transgenic. Therefore, it has

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theoretical significance and application value that developing rapid discrimination method for transgenic rapeseed oil based on NIR. The objective of this study is to apply NIR spectroscopic techniques for detection of transgenic rapeseed oil. The specific goals were to (1) investigate the feasibility of near infrared spectroscopy in detecting transgenic rapeseed oil, (2) combine the PCA for classifying NIR rapeseed oil spectra into two groups, non-transgenic ones and transgenic ones and (3) establish the DPLS model to discriminate the transgenic samples. This research can provide discrimination of transgenic rapeseed oil as well as other transgenic farm produce with new method and technical support.

2 Materials 2.1 Samples 117 rapeseed oil samples were collected from seven brands such as Jin Long Yue, Li Yu, Hong Qingting, Xiao Mifeng, Lu Hua, including 64 non-transgenic rapeseed oil samples and 53 transgenic ones. The volume of every sample was 100ml. 35 samples (19 non-transgenic rapeseed oil samples and 16 transgenic ones) were used for calibration set, and the rest 82 ones for validation set. All the samples chose for calibration and validation sets were randomly. 2.2 Spectral Measurement The NIR spectra of every rapeseed oil sample were collected by MATRIX-F Fourier transform infrared spectrometer of BRUKER in the spectral regions of 11997~3999cm-1. Spectrometer parameters setting, spectral data collecting were performed via software

Fig. 1. NIR spectra of 117 rapeseed oil samples

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OPUS. The number of scanning was 32, the resolution was 4cm-1 and the spectral points were 4148. Fig 1 showed the NIR spectra of 117 rapeseed oil samples.

3 Methods 3.1 PCA Principal component analysis (PCA) is a multivariate modeling and analysis technique commonly used in chemometric studies. It can reduce the number of dimensions without much loss of information based on their similarities and differences to emphasise their similarities and differences. It can define the ‘‘principal components” (PCs) which describe independent variation structures in the data. These PCs are orthogonal, so that the data set presented on these axes are uncorrelated with each other. Visualizing the PCs by various plotting systems can view interrelationships between different variables. [20] 3.2 DPLS The PLS algorithm has been widely used as a chemometric tool for near-infrared spectral analysis. Ii is a regression method based on characteristic variable. Its advantage is that it decomposes not only matrix X, but also matrix Y. While decomposing matrix Y it uses the information of matrix X; while decomposing matrix X it uses the information of matrix Y. Through that the regressive results become better. The DPLS method applies partial least squares (PLS) regression to binary classification problems, in which the dependent variabley codifies the class of each sample. In this study, the integer 1 codifies the sample as belonging to non-transgenic rapeseed oil and the integer 2 codifies the sample as belonging to transgenic ones. [21-22]

4 Results and Discussion 4.1 PCA PCA can indicate relationships among groups of variables in a data set and show relationships that might exist between objects. All the spectra of 64 non-transgenic rapeseed oil and 53 transgenic ones are used for PCA. The spectra were original, without any optimizing or pre-treatment. According to MATLAB software, the score plots of principal components can be obtained. Fig 2 showed the three-dimensional score plot of NIR principal components (PC2 PC3 PC4) for 117 rapeseed oil samples. Fig 3 showed two-dimensional score plot of principal components PC2 and PC4. In this figure, non-transgenic rapeseed oil were represented by “ ”, and transgenic ones by “ ”. From these figures, it can be found that non-transgenic rapeseed oil and transgenic ones can be discriminated roughly in their score plot of principal component, but the dividing line was not obvious.

、、

△

〇

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、、

Fig. 2. Three-dimensional Score plot of three principal components (PC2 PC3 PC4)

Fig. 3. Two-dimensional Score plot of two principal components (PC2 and PC4)

4.2 DPLS The software OPUS was used to establish DPLS (Discriminant partial least squares) model on original NIR spectra of calibration set. Then the model was used to predict validation set. The prediction result was that R2=0.7487 and RMSEP value of 0.249. Fig 4 showed the predictions of rapeseed oil varieties in the calibration set using the DPLS model with the original NIR spectra. Non-transgenic rapeseed oil were represented by “ ”, and transgenic ones by “ ”. Non-transgenic rapeseed oil with predicted values ranged form 0.5 to 1.5 and transgenic samples from 1.5 to 2.5 were all

△

〇

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Fig. 4. Plot of actual vs calculated values of transgenic and non-transgenic rapeseed oil

Fig. 5. True value of 82 prediction categories vectors

Fig. 6. Discriminate value of 82 prediction categories vectors

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considered to be broadly correctly classified by the model. Fig 5 showed the true value of 82 prediction categories vectors .The prediction values within the range from 0.5 to 1.5 were identified as “1” and the ones from 1.5 to 2.5 were identified as “2”. The discriminate value acquired was showed in fig 6. From fig 6, it can be found that there were 3 inaccurate decision samples. Two non-transgenic samples that labeled as “1” were discriminated as “2” and one transgenic sample that labeled as “2” was discriminated as “1”. So the general discrimination accurate rate was (82-3)/82=96.34%; the discrimination accurate rate of non-transgenic samples was (45-2)/45=95.56%; the one of transgenic samples was (37-1)/37= 97.30%.

5 Conclusion The study showed that the attempt to discriminate transgenic rapeseed oil using NIR is feasible. The DPLS model obtained the prediction result was that R2=0.7487, RMSEP value of 0.249 and the discrimination accurate rate of 96.34% which is broadly satisfactory result using DPLS model on the original spectra. The results showed that excellent classification can be obtained by combining NIR and DPLS method. Introducing NIR into rapidly discrimination of transgenic food can provide new method and technical support for the detection of transgenic food. However it is still in the infant and exploration stage. There are some limitations consisting in the methods that used to establish models. Thus the research about optimizing model should be concerned in the filed of rapid detection for transgenic food using NIR.

Acknowledgments The authors gratefully acknowledge the financial support provided by the National Natural Science Foundation of Chongqing (No. CSTC2008BB1091).

References 1. Jeong, S.-C., Pack, S., Cho, E.-Y., et al.: Molecular analysis and quantitative detection of a transgenic rice line expressing a bifunctional fusion TPSP. J. Food Control. 18(11), 1434–1442 (2007) 2. Batista, R., Oliveira, M.M.: Facts and fiction of genetically engineered food. J. Trends in Biotechnology 27(5), 277–286 (2009) 3. Chen, Y., Wang, H.: The safety of Gentic engineering and genetically food. J. Food Science 23(12), 145–149 (2002) 4. Liu, J., Wei, C., Luo, Y., et al.: Detection of transgenic ingredient in food. J. Journal of Northeast Agricultural University 35(05), 634–638 (2004) (in Chinese) 5. Ahmed, F.E.: Detection of genetically modified organisms in food. J. Trends in Biotechnology 20(5), 215–223 (2002) 6. Zhou, S.: Progress in Study of Genetically modified food detection. J.Fujian Science and Technology of Rice and Wheat 25(1), 43–45 (2007) (in Chinese)

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7. Lu, W., Yuan, H., Xu, G., et al.: Modern Near Infrared Spectroscopy Analytical Technology. Beijing (2000) 8. Uddin, M., Okazaki, E., Fukushima, H., et al.: Nondestructive determination of water and protein in surimi by near-infrared spectroscopy. J. Food Chemistry 96(3), 491–495 (2006) 9. Zhu, S., Wang, Y., Zhang, X.: Design and implementation of near infrared spectroscopy analysis software system for agriculture product quality detection. J. Transaction of the Chinese Society of Agriculture Engineering 19(04), 175–179 (2003) (in Chinese) 10. Zhou, P., Wu, R., Zhang, J.: The advances in the detection method of GMO foods. J. Chinese Journal of Food Hygiene 16(3), 254–258 (2004) (in Chinese) 11. Liu, Z., Xia, D., Lu, L., et al.: Expectationof methods of detection geneetically modified foods. J. Food Research and Development 28(5), 161–164 (2007) (in Chinese) 12. Roussel, S.A., Hardy, C.L., Hurburgh, C.R., et al.: Detection of Roundup ReadyTM Soybeans by Near-Infrared Spectroscopy. Jq. Applied Spectroscopy 55(10), 1425–1430 (2001) 13. Oomen, R.J.F.J., Tzitzikas, E.N., Bakx, E.J.: Modulation of the cellulose content of tuber cell walls by antisense expression of different potato (Solanum tuberosum L.) CesA clones. J. Phytochemistry 65(5), 535–546 (2004) 14. Rui, R., Luo, Y., Huang, K., et al.: Application of near-infraraed diffuse reflectance spectroscopy to the detection and identification of transgenic corn. J. Spectroscopy and Spectral Analysis 25(10), 1581–1583 (2005) (in Chinese) 15. Xie, L., Ying, Y., Ying, T.: Combination and comparison of chemometrics methods for identification of transgenic tomatoes using visible and near-infrared diffuse transmittance technique. J. Journal of Food Engineering 82(3), 395–401 (2007) 16. Xie, L., Ying, Y., Ying, T., et al.: Discrimination of transgenic tomatoes based on visible/near-infrared spectra. J. Analytica Chimica Acta 584(2), 379–384 (2007) 17. Xie, L., Ying, Y., JinYing, T.: Classification of tomatoes with different genotypes by visible and short-wave near-infrared spectroscopy with least-squares support vector machines and other chemometrics. J. Journal of Food Engineering 94(1), 34–39 (2009) 18. Lijuan, X., Yibin, Y., Tiejin, Y., et al.: Application of Vis/NIR Diffuse Reflectance Spectroscopy to the Detection and Identification of Transgenic Tomato Leaf. J. Spectroscopy and spectral analysis 28(5), 1062–1066 (2008) (in Chinese) 19. Rui, R., Huang, K., Wang, W., et al.: Detection of Erucic Acid and Glucosinolate in Intact Rapeseed by Near-Infrared Diffuse Renectance Spectroscopy. J. Spectroscopy and Spectral Analysis. 26(12), 2190–2192 (2006) (in Chinese) 20. Shin, E.-C., Craft, B.D., Pegg, R.B., et al.: Chemometric approach to fatty acid profiles in Runner-type peanut cultivars by principal component analysis (PCA). J. Food Chemistry 119, 1262–1270 (2010) 21. Milidiu, R.L., Renteria, R.P.: DPLS and PPLS: two PLS algorithms for large data sets. J. Computational Statistics & Data Analysis. 48, 125–138 (2005) 22. Botella, C., Ferré, J., Boqué, R.: Classification from microarray data using probabilistic discriminant partial least squares with reject option. J. Talanta. 80, 321–328 (2009)

Study on Regional Agro-ecological Risk and Pressure Supported by City Expansion Model and SERA Model - A Case Study of Selangor, Malaysia Xiaoxia Shi1, Yaoli Zhang1, and Cheng Peng2 1

Beijing Key Laboratory of Logistics Systems and Technology, Beijing, 101149; School of Logistics, Beijing Wuzi Universtiy, Beijing, 101149 2 National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China

Abstract. This study revealed the influence of city expansion on the agro-ecological risks through the analysis and prediction of city expansion in different periods and study on the change of risk and pressure on the regional agricultural eco-environment. The city expansion of Selangor, Malaysia (as a case) was predicted based on relevant spatial and attribute data as well as simulation prediction models of city expansion. Subsequently, the ecological risk and pressure in the study area as well as on regional agricultural land use was assessed through the realization of factors of SERA Model. The results showed that the risk and pressure on agricultural land use was consistent with the level of the urbanization. With the expansion of urban area, the ecological risk on agricultural land use in the study area became greater. The risk and pressure on agricultural land use with city expansion was well analyzed with SERA model. Keywords: City expansion, Ecological risk, Remote sensing, SERA model.

1 Introduction Urbanization aggravation leads to a series of ecological problems, such as land degradation, climate change, etc. The major manifestation of urbanization is the spatial urbanization. With the continuous expansion of urban area, much useable land of other types is occupied. It results in the despoliation of the space and resources of eco-environment, the destruction of the regional eco-environment and brings risks to the regional agro-ecological. The in-depth study on relations between the city expansion and the regional eco-environment, along with the further study on the risks and pressure on agricultural eco-environment would lay the theoretical foundation to improve the agricultural eco-environment and to realize the sustainable development of agriculture. Agricultural eco-environment is a part of the regional eco-environment. However, the relationship between the agricultural eco-environment and the city expansion has not yet been put forward directly. Based on the neural network, some scholars made quantitative analysis on the cultivated land security with the support of the spatial D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 641–649, 2011. © IFIP International Federation for Information Processing 2011

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information technology and the study didn’t include the time characteristics (Qian Yurong, 2009). The study on the relationship between environment and city expansion was a qualitative description originally. For instance, Shi Dengrong et al. made qualitative analysis on the ecological problems like atmospheric environment, soil quality, land resources, etc. as well as the social issues caused by urbanization(Shi Dengrong, 2001). However, this qualitative study neither meets the needs of study on city expansion, nor provides assessment methods and tools in the urban planning of the regional environmental sustainability for the planners and policy makers. The development of the remote sensing technology provides more effective ways and means to obtain the data of city expansion. Thus, the quantitative study on relationship between city expansion and eco-environment was paid attention to by more and more researchers on the urban remote sensing and the eco-environment. The study on relationship between city expansion and eco-environment was divided into three aspects: study on relationship between city expansion and eco-environment based on the spatial heat environment, which mainly studied the influence of the urban heat island on eco-environment (Lo CP, 1997; CHEN YUNHAO, 2002); study on relationship between city expansion and eco-environment with the introduction of the ecoenvironmental index based on the remote sensing data (Wilson, 2003; YUE WENZE, 2006); study on relationship between the spatial form of city expansion and ecoenvironment, which was mainly from the perspective of spatial patterns with the support of spatial information technology. Study on relationship between the spatial form of city expansion and ecoenvironment was based on the theory of land use change to study the influence of the city expansion spatial form on eco-environment through the variation characteristics of urban spatial form. It gave a certain division standard of evaluating grade (Ma Ronghua, 2001). Some scholars adopted Ecological Risk Index (ERI) Model and determined the intensity parameters of the ecological risk to complete the digital simulation of the ecological space distribution (Wei Shichuan, 2008). However, the experts’ judgment about the intensity parameters of ecological risk in the study increased human disturbance degree in risk value, and the performance of the risk value in space can not be refined. And most of the study on city expansion and regional ecoenvironment based on GIS and RS was study on spatial change on the ground of the administrative boundaries; some scholars began the regional ecological risk assessment according to the landscape units, but the interaction among ecological units as well as the pressure mechanism on the eco-environment imposed by city expansion was not taken into account in this pattern of environment assessment (MA Shu-ting, 2004). Li Jinggang et al. built the ecological risk assessment model for natural/seminatural landscape space under the urbanization based on the meso-scale according to the principle of "pressure, exposure and stability". An ecological risk assessment of the urbanization process was conducted in Beijing. Its model was described as follows (Li Jinggang, 2008):

V = A+ F − I − N .

(1)

In the formula, V refers to ecological risk of landscape space; A , F , I , and N refers to 4 factors of the external accessibility of the landscape, the external pressures, the internal stability and the neighborhood stability respectively.

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From the current remote sensing study on relationship between city and ecoenvironment, it can be seen that taking remote sensing as the support, the ecological assessments from the perspective of the regional spatial pattern do not reflect the relationship between the spatial urbanization and the eco-environment clearly; the ecological risk assessments with single scale do not meet the needs of study any more; as to the eco-environment assessments based on the means of remote sensing, most of them have adopted the single factor assessment model, because the multifactor assessment model of the eco-environment is too simple; as to the remote sensing analysis of eco-environment with the introduction of the eco-environment assessment model under city expansion, the assessment model is too complex and involves too many socio-economic indicators difficult to obtain. The assessment factors like the biomass dynamic changes and the net primary productivity, etc. obtained by the remote sensing methods are not applied effectively; meanwhile, the ecoenvironmental risk assessment under city expansion based on the remote sensing is not proposed directly. Taking Selangor, Malaysia as the case, through the analysis of relationship between city expansion and eco-environment, with the support of RS technology, the study was based on the remote sensing assessment model of ecological risk, namely, SERA model, under city expansion and analyzed the influence on the agricultural eco-environment imposed by city expansion to determine the risk and pressure on the agricultural eco-environment imposed by city expansion. It would provide technical support to improve agricultural eco-environment.

2 Methods The ecological risk and pressure of the study area based on SERA model was simulated in this study. Then, the ecological risk and pressure on agricultural land use in the period of study was analyzed through the ecological risk and pressure imposed of the entire study area. 2.1 Overview and Data of the Study Area The study area includes Selangor, Malaysia and the capital Kuala Lumpur and the administrative capital Putrajaya, with a total area of approximately 8,214km2. The study area is located at 101°41′E, 3°09′N on the southwestern coast of Malaya near the equator; the northwest, north, and northeast areas of the study area are occupied by hills and mountains; Klang River and its tributaries Gombak River converge in the city and flow into from the southwest of the Malacca Strait. The climate there is tropical rainforest climate with high temperature and rainy throughout the year; its average temperature is 25 -30 ; the rainfall is abundant, and the annual average rainfall is 2000mm-2500mm; the rainy months are from March to April and from September to November. There is ample sunshine with high temperature during the day and relatively cool in the afternoon and evening in the study area. This area is often attacked by a sudden storm. The main cash crops in the area are oil palm, rubber trees etc.

℃ ℃

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The data adopted in this study included spatial data, socio-economic data and field survey data. As the area of Selangor, Malaysia and the capital Kuala Lumpur is large, and 2-scene data covering the entire state was required, the spatial data included 12scene Landsat TM/ETM images of numbering 127/57 and 127/58 in six periods of 1990, 1991, 1994, 1996 , 1998 and 2002, vectorgraph of the entire administrative division, river network, road network, digital elevation model (DEM), the land use type map in 1996; the socio-economic data originated mainly from the "Malaysia Plan" made by the Malaysia Statistical Bureau, including basic socio-economic indicators; the field survey data included the coordinate points of unchanged regions and the economic center regions required for radiometric normalization of remote sensing image supported by DGPS. This study classified the land use based on segmentation results of the Mean-shift algorithm. 2.2 Prediction of City Expansion in the Study Area The urbanization intensity index ( UII ) is one of the important measurement indicators reflecting the expansion characteristics of urban land use, the formula is described as (2):

UII i ,t ~ t + n = [(ULAi ,t + n − ULAi ,t ) / n] × 100 / TLAi Where,

UII i ,t ~ t + n ULAi ,t + n ,

and

ULAi ,t

.

respectively refers to the

(2)

UII of the spa-

t ~ t + n and the area of the urban land use in the year t + n and t . TLAi refers to the total area of the spatial unit i (Liu Shenghe, 2000).

tial unit i within the period of

The data of Table 1 was obtained from the classification results of land use type. It can be seen from Table 1 that city expansion of Selangor from 1990 to 2002 was extremely rapid. From 1994 to 1998, the city expansion area was 331.02km2 and the annual average expansion intensity was close to 10%. The study area was in the phase of high-speed expansion from 1990 to 2002. The high-speed expansion of land use for urban construction in the study area imposed great pressure on agricultural ecoenvironment. It became one of the important factors of the risk and pressure of agricultural eco-environment. The prediction of land use for urban construction would provide scientific and effective theoretical basis for the risk and pressure assessment on agricultural land use in the study area. By building CEM (City Expanding Model in Metropolitan Area) model of the entire Selangor State, namely, city expansion in metropolitan area, the city expansion was predicted in Selangor in this study. An expanded model of the CA model, CEM model was proposed by He Chunyang et al. which combined the system dynamics with the traditional CA model to simulate city expansion in metropolitan area. Considering the relevant image data, spatial data and attribute data of Selangor, Malaysia and the capital Kuala Lumpur, CEM model was set to predict the city expansion of Selangor (Chunyang He, 2008; Shi Xiaoxia, 2008). The city expansion of Selangor in 2020 was predicted with the residential map layer of Selangor in 2002 as the original map layer. The predicted results are shown in Fig. 1.

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Table 1. City expansion intensity in the study area from 1990 to 2002

Expansion area (km2) Annual average expansion intensity (%) Annual expansion area (km2) Expansion type

1990-1994 153.41 5.50

1994-1998 331.02 9.72

1998-2002 188.26 3.98

38.35 High-speed

82.75 High-speed

47.06 High-speed

Fig. 1. Land use situation of Selangor State for city building and the predicted results

in 2020 2.3 SERA Model The pressure of city expansion on regional eco-environment is reflected by SERA model. The remote sensing model of the spatial ecological risk assessment, namely, SERA model, in this study is the assessment model based on landscape units ( x , y ). The ecological risk and pressure

P on meso-scale landscape units ( x , y ) is

PA , the spatial driving force of the landscape unit, PAI , the spatial influencing factor of macro-scopical region driving force, PR , the spatial risk resistance on the landscape unit ( x , y ), PRI , the spatial influencing factor of macro-scopical regional resistance, PE , the characterization factor of ecological risk based on the remote sensing of landscape unit ( x , y ), PSI , the comprereflected by the interaction of

0

hensive influencing factor of the regional ecological risk assessment, and P , the adjustment coefficient of model. The ecological risk and pressure P is described as in formula (3):

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P=

( PA PAI − PR PRI ) + PE ⋅ PSI ⋅ P 0 . 2

(3)

PA , the spatial driving force of the landscape unit, includes the interrelations of DR , the relations between the landscape unit and the nearest road, DG , the relations between the landscape unit and the geometrical gravity centre of main plaque of construction land, FL , the relations between the landscape unit and construction land, and DW , the relations between the landscape unit and the spatial heat environment. DR and DG the influence of distance and co-react in spatial landscape drive, and

FL and DW , the relationship of distance and area and co-react; PAI includes

SA , the patch size index of city construction land in the study area from a macroperspective and SF , the patch shape index of city construction land in the study area. PR includes SD , the patch density index in the landscape unit, namely, its own stability of the landscape unit and SI , the slope; NS , the stability of neighborhood of a landscape unit. PRI is determined mainly by FN , the influence coefficient of fragmentation of ecological landscape. PE is mainly reflected as the eco-environment index based on remote sensing. The comprehensive influencing factor of the model is reflected as UZ , the pressure on eco-environment imposed by human activities as well as lot of waste produced with the growth of economy. Accordingly, the remote sensing assessment model of ecological risk and pressure on the landscape was described as formula (4):

P = ( PA PAI − PR PRI ) ⋅ PSI ⋅ P 0 =

1⎡ 1 ⎢( 2 ⎣⎢ nAi

⎡ ⎢( 1⎢ = 2⎢ ⎢ ⎣⎢

n

∑ PAi ) ⋅ ( i =1

1 nAIj

n

∑ PAIj ) − ( j =1

1 nRi

n

∑ PRi ) ⋅ ( i =1

1 nRIj

n

∑P j =1

RIj

⎤ ) + PE ⎥ ⋅ PSI ⋅ P 0 ⎦⎥

⎤ 1 ⎡1 1 ⎤ 1 ( DR + DG ) + ( FL + DW ) ⎥ × [ SA + SF ] − ⎥ ⎢ 2 ⎣2 2 ⎦ 2 ⎥ ⋅ (UZ ) ⋅ P 0 ⎥ 1 1 ⎥ ( SD + SI + NS ) × FN ) + ( LA + CW ) 3 2 ⎦⎥

(4)

. Where,

P>0

3 Assessment Results of Agro-ecological Risk in the Study Area Based on the theory of the CA model and the landscape ecology, each factor in the model were realized concretely by combining with the actual situation and data in the study area. Based on SERA model and with the landscape unit area of 300m×300m

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the spatial ecological risk of the study area in 1990, 1991, 1994, 1996, 1998 and 2002 was assessed. The results range was 0~1 and was classified into 5 grades according to 0~0.2, 0.2~0.4, 0.4~0.6, 0.6~0.8 and 0.8~1. Fig.2 (a) and (b) refers to spatial risk assessment results of cultivated land in the study area in 1990 and 2002 respectively. Table2 refers to the ecological risk grade variation of the cultivated land in the study area in 1990, 1994 and 2002.

Fig. 2. The ecological risk variation in the study area in 1990 and 2002 Table 2. Ecological risk grade variation of the cultivated land in the study area (the

number of landscape units)

Year 1990 1994 2002

Total Primary Percentage Secondary Percentage Tertiary Percentage cultivated risk (%) risk (%) risk (%) land 4805161 4636729 96.49 168432 96.49 0 0 4491520 2894320 64.44 1575904 64.44 21296 0.47 4030752 288464 7.16 3546752 7.16 195536 4.85

(1) It could be seen from Fig. 2 and Table 2 that ecological risk grade of the cultivated land in the study area in 1990 was obviously low and was mainly primary risk without the occurrence of tertiary risk area. The secondary risk dominated in 2002 with the occurrence of tertiary or higher risk areas in different regions. The risk value of cultivated land and the risk grade were ever-increasing. (2) It could be seen from Table 3 that the pixel number in remote sensing images of city construction land in the study area increased from 775,368 in 1990 to 1,522,789 in 2002. Its percentage increased from 8.49% to 16.67%. Meanwhile, cultivated land

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was ever-shrinking. The pixel number decreased from 4,763,258 in 1990 to 4,103,960 in 2002. Its percentage reduced by 7.22%. Table 3. Variation of residential land and cultivated land from 1990 to 2002

Residential land Year 1990 1991 1994 1998 2002

Area (pixel number)

Percentage (%)

775368 1073515 945818 1313614 1522789

8.49 11.75 10.36 14.38 16.67

Cultivated land Area Percentage (pixel (% ) number ) 4763258 52.15 4326125 47.36 4505771 49.33 4365608 47.80 4103960 44.93

(3) Through the prediction results of city expansion, it could be known that there would be a further expansion of city construction land by 2020. With the constant expansion of city construction land, agricultural land would be occupied continuously, which led to the constant decrease of cultivated land. The agricultural land in study area would be threatened seriously both on its quantity and eco-environment.

References 1. Qian, Y., Li, J., Wang, W.: Quantitative Analysis of Cultivated Land Safety in Zhangjiagang City based on BP-MC Networks. Transactions of the CSAE 25(19), 299–305 (2009) 2. Shi, D., Qiu, J., Yan, Z.: Comparative Study on Air Quality of Several Cities in the Yangtze River Delta. Urban Research (3), 55–57 (2001) 3. Lo, C.P., Quattrovhi, D.A., Luvall, J.C.: Application of High-Resolution Thermal Infrared Remote Sensing and GIS to Assess the Urban Heat Island Effect. International Journal of Remote Sensing 18(2), 287–304 (1997) 4. Chen, Y., Li, X., Shi, P., et al.: Study on Spatial Pattern of Urban Heat Environment in Shanghai City. Scientia Geographica Sinica 22(3), 317–323 (2002) 5. Wilson, J.S., Clay, M., Martin, E., Risch, K.V.: Evaluating Environmental Influence of Zoning in Urban Ecosystems with Remote Sensing. Remote Sensing of Environment 86, 303–321 (2003) 6. Yue, W., Xu, J., Xu, L.: An Analysis on Eco-Environmental Effect of Urban Land Use based on Remote Sensing Images: A Case Study of Urban Thermal Environment and NDVI. Acta Ecologica Sinica 5(26), 1450–1460 (2006) 7. Ma, R., Hu, M.: Assessment of the Eco-Environment in Hainan Island based on RS & GIS. Tropical Geography 21(3), 198–201 (2001) 8. Wei, S., Feng, K., Xing, Y., et al.: Digital Simulation of Land Use Change and Ecological Security of Resources-based City. Transactions of the CSAE 24(9), 64–68 (2008) 9. Ma, S., Tang, J., Lin, N.: Synthetic Assessment of Eco-Environment in Western Jinlin Province Based on Multi-Source GIS and RS Spatial Information. Resources Science 26(4), 140–145 (2004)

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10. Li, J., He, C., Li, X.: Landscape Ecological Risk Assessment of Natural / Semi-natural Landscapes in Fast Urbanization Regions — A Case Study in Beijing, China. Journal of Natural Resources 23(1), 33–47 (2008) 11. He, C., Norio, O., Qiaofeng, Z., et al.: Modelling Dynamic Urban Expansion Processes Incorporating a Potential Model with Cellular Automata. Landscape and Urban Planning 86, 79–91 (2008) 12. Shi, X., Li, J., Chen, Y.: Analysis and Prediction of Urban Expansion in Kuala Lumpur Based on Remote Sensing and CA Model. Bulletin of Soil and Water Conservation 28(2), 121–126 (2008)

Study on Relationship between Tobacco Canopy Spectra and LAI Hongbo Qiao1,Weng Mei1, Yafei Yang1, Wang Yong2, Jishuai Zhang2, and Yu Hua1,* 1

College of information and management science, Henan agricultural university, P. R. China 2 Sanmenxia Branch of Henan Tobacco Company, Sanmenxia, P. R. China [email protected]

Abstract. N nutrition is one of the most important limitation factors for crop growth and yield. Precise and timely monitoring and detection of crop N nutrient conditions is necessary for improving the efficiency of N nutrition using and crop management, reducing environmental pollution caused by over nitrogen fertilizer application. In this paper the canopy reflectance spectra during the whole growth period on tobacco field plots treated with different nitrogen levels were periodically and continually measured. LAI of tobacco of several important growth periods was meanwhile measured. The results showed that tobacco canopy spectral reflectance of different growing periods changed regularly with the increase of nitrogen fertilizer application The canopy spectral reflectance increased in 7l0 1000 nm while decreased in 460 680 nm. There was high correlations between NDVI and LAI. The research supply theoretical foundation for tobacco N management.

～

，

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Keywords: Tobacco; spectra; LAI.

1 Introduction Crop canopy spectrometry is a harmless remote determination technique. It is a current effective remote monitoring method of crop growth state. The reflectance spectral curve of plant has significant characteristics. For same plant, spectral curve varies with fertility levels and development stage [1]. Thus, spectral curve characteristics could reflect plant growth state. Biochemical parameters of plant canopy estimated through remote technique can understand the productivity of plant and the effectiveness of soil nutrients [2, 3]. Svetlana et.al. [4] determined the chlorophyll content in leaf of wheat, maize, beet and grape, established the first-order differentiation of canopy spectrum and the regression equation model of chlorophyll content, which could estimated the chlorophyll content in different crop species. Daughtry et.al. [5] determined the canopy spectrum of maize at 8 nitrogen fertilizer levels, and the screened MCARI and NIR/Green vegetation index *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 650–657, 2011. © IFIP International Federation for Information Processing 2011

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were significantly related to the chlorophyll content in leaf. The chlorophyll content in canopy was very sensitive to nitrogen reserve in soil [3]. Thus, chlorophyll content could be used to monitor the abundance and deficiency of nitrogen in soil. Elizabeth et.al. [6] estimated the chlorophyll content in potato through PROSAIL model, which could predict the nitrogen content in soil at late growth stage. LAI is one of most basic parameters to reflect the structure of vegetation canopy, and is also key determination factor of biomass and yield [7]. Wu et.al. [8] established the correlation model of vegetation index with maize/potato LAI based on QuickBird satellite image, and modified soil adjusted vegetation index (MSAVI) was more accurate (R2=0.63 and R2=0.79). in the study of Daughtry et.al. [5], in maize at different nitrogen fertilizer levels, the reflectance varied with the increase in LAI and chlorophyll content (at maximum extent near red edge 715nm), and moved toward long wave. Hung et.al. [9] established the partial least square regression model of rice reflectance spectrum with canopy LAI (R 2>0.79) to better evaluate the growth state of rice mainly within near-infrared, visible light and red edge 707nm range. Above studies verified the estimation of chlorophyll and LAI through remote technique, mainly for wheat and maize, but seldom for tobacco. Tobacco is harvested through leaf, and it is significant to monitor its LAI and chlorophyll content. This study tested tobacco under different fertilization conditions, defined the change law of chlorophyll content/LAI/canopy spectral characteristics, determined the correlation of vegetation index with chlorophyll content and LAI at different waveband combinations, confirmed whether canopy spectrum could be used to estimate LAI and chlorophyll content, and provided technological means for quick harmless large-area obtaining of information on the growth state of tobacco.

2 Materials and Methods 2.1 Experiment Design 5 levels of nitrogen fertilizer: N0 (0kg/hm2), N1 (22.5kg/hm2), N2 (45kg/hm2), N3 (67.5kg/hm2) and N4 (90kg/hm2). 3 levels of organics: Low (225 kg/hm2), middle (375kg/hm2) and high (525kg/hm2). No base fertilizer, repeat each treatment for 3 times, and randomly divide into 45 blocks (12m×6m). Qin tobacco #96, at a row spacing of 1.2m and a plant spacing of 0.6m. Experiment 1: At National Modern Tobacco Demonstrative Area in Mianchi County, Sanmenxia City, Henan, China (north latitude 34º37´52.96″ and east longitude 111º44´28.24″). Surface layer 0~20cm, soil organics 12.3g/kg, total nitrogen 0.74mg/kg, effective phosphorus 9.7mg/kg, quick-acting potassium 120mg/kg, and soil pH8.2. Experiment 2: At Xuedian Village, Zhuyang Town, Lingbao City, Henan, China (north latitude 34º17´13.56″ and east longitude 110º44´29.04″). Surface layer 0~20cm, soil organics 11.9g/kg, total nitrogen 0.8mg/kg, effective phosphorus 7.2mg/kg, quick-acting potassium 132mg/kg, and soil pH7.9.

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2.2 Spectral Data Collection This study made spectrometry at characteristic growth stage of tobacco: Seedling stage (June 20, 2009), resettling growth stage (July 19, 2009), vigorous growth stage (August 11, 2009), early-maturity stage (September 5, 2009) and maturity stage (September 23, 2009). Canopy spectrometer adopted portable hyperspectral radiometer of American ASD Co. (HandHeld), spectral range 325~1050nm, spectral resolution 1.4nm, field angle 25º. Make spectrometry in 3 tobacco plants of same growth state at each treatment, determine canopy spectral reflectance at 10:00~14:00 in sunny windless climate, with sensor probe vertically downwards and at 60cm vertically away from canopy top. Calibrate standard white plate before each determination, record data in 20 groups, and regard their mean as spectral reflectance value of this treatment. 2.3 Determination of Leaf Area and Chlorophyll Content LAI=Length×width×0.6345 [10] Chlorophyll content: SPAD-502 chlorophyll detector of Japan MINOLTA Co. was used to determine spectrum and chlorophyll content in tobacco, i.e. Specialty Products Agricultural Division (SPAD). According to the absorptivity of leaf chlorophyll to colored light, SPAD-502 chlorophyll detector determined the chlorophyll content in leaf by determining the intensity of emission light at a certain wavelength and that of light through leaf. SPAD-502 chlorophyll detector had 2 emission light sources to emit red light of maximum 650nm and infrared light of maximum 940nm respectively. Chlorophyll absorbed red light of 650nm, but did not absorb infrared light of 940nm which was emitted and received to mainly eliminate the influence of leaf thickness on determination results. After reaching leaf, some of red light was absorbed by leaf chlorophyll, and the remaining passed through leaf and was converted into electric signal through receiver. The chlorophyll content in leaf was determined by comparing the intensity of emission light with that of light received by receiver (SPAD unit): SPAD = Klg

⎡ IRt / IR 0 ⎤ ⎢⎣ Rt / R 0 ⎥⎦

Wherein, K: Constant; IRt: Intensity of received 940nm infrared light; IR0: Intensity of emission infrared light; Rt: Intensity of received 650nm red light; and R0: Intensity of emission red light. 2.4 Data Analysis This study analyzed the correlation of several spectral parameters with chlorophyll content and LAI in tobacco canopy. This study selected significantly-related sensitive waveband and spectral parameters, established chlorophyll content and LAI monitoring model through regression analysis, and optimized the equation through estimated standard error (SE) and determination coefficient (R2). Spectral parameters were shown in Table 1.

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Table 1. Hyperspectral parameters used in this study Spectral parameter

Algorithm formula and definition RVI=NIR/RED NDVI=(NIR-RED)/(NIR+RED) NDVIgreen=(NIR-Green)/(NIR+Green) SAVI=1.5*(NIR-RED)/(NIR+RED+0.5) OSAVI=(NIR-RED)/(NIR+RED+0.16)

RVI NDVI NDVIgreen SAVI OSAVI MSAVI

MSAVI = (1/2)[2 * NIR + 1 - (2 * NIR + 1)

2

- 8 * (NIR - RED)] MCARI=[(R700-R670)-0.2*(R700-R500)]*(R700/670)

MCARI

3 Results and Analysis 3.1 Raw Spectral Characteristics of Tobacco Canopy Tobacco grew slowly, rapidly and then slowly at early, middle and late growth stage respectively. For example, the leaf area and plant height of tobacco increased slowly at <30, 30~60 and >60 days after the transplantation respectively, i.e. overall like S-shaped growth curve. At different growth stages, spectral curve of tobacco canopy changed at both same and different tendency, i.e. same at overall tendency but greatly different at local waveband. As shown in Fig.1, at seedling stage, the LAI and chlorophyll content was very small, and the reflectance of visible light (450~760nm) was higher than that at other growth stages due to mulching film. Thus, the canopy reflectance of visible light and near-infrared light was mainly influenced by the content of chlorophyll a/b, carotenoid and xanthophylls, and the optical characteristics of leaf tissue respectively. With growth progress, there was a continuous increase in tobacco biomass and chlorophyll content, and an increase in LAI. Canopy spectral reflectance decreased to a certain extent within visible light, increased rapidly within near-infrared area, peaked at vigorous growth stage, and gradually decreased at maturity stage with partial leaf harvest. 0.7 0.6

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Fig. 2. Canopy spectral characteristic curves of tobacco at different N levels(Pre-mature stage)

As shown by data at 4 growth stages, the spectral characteristics of tobacco canopy changed roughly at same tendency, but at significant difference for different nitrogen fertilizer levels (and very significantly even at early-maturity stage (Fig.2), i.e. P>0.05 at t test (760nm, 800nm, 850nm and 960nm respectively). 3.2 Correlation of Vegetation Index with Chlorophyll Content and LAI As shown by Table 2, LAI was very significantly related to RVI and NDVI (green); and significantly related to NDVI, SAVI and RDVI respectively. NDVI (green) was a spectral parameter closely related to leaf chlorophyll content and LAI. 3.3 Regression Equation of NDVI (Green) with Chlorophyll Content and LAI According to the NDVI (green), regression equation was established for NDVI (green) with LAI. As shown by regression equation with an independent variable of NDVI (green) (Table 3), a regression equation could be established for LAI. Determination coefficient (R2) was 0.568 significantly for the regression equation of LAI, and

Table 2. Correlation analyses between vegetable index and chlorophyll, LAI (n=90) Item

RVI

NDVI

NDVI(green)

SAVI

OSAVI

MSAVI

RDVI

0.426**

0.222*

0.484**

0-.227*

-0.062

-0.125

-0.242*

Associated Probability

0

0.018

0

0.08

0.575

0.147

0.023

Variation Coefficient

0.546

0.003

0.007

-0.004

0

-0.002

-0.005

Correlation Coefficient LAI

Table 3. Regression equations of chlorophyll and LAI by NDVI(green)

Dependent LAI

Regression eqution Y=0.689+0.035X1

* Significant(P 0.05)

Independent X1

The name of independent NDVI(green)

Regression coefficient Sig 0.024

R2 0.568*

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regression coefficient was also significant. Thus, the established regression equation was effective, and NDVI (green) could be used to estimate LAI.

4 Conclusion and Discussion Nitrogen is necessary for the growth, development, quality and yield of tobacco. However, excessive nitrogen caused spindly growth, formed black tobacco leaf, and greatly lowered tobacco quality. LAI is important index of tobacco photosynthesis and metabolism, and closely related to final yield and quality. Thus, the fertilization state of field and the growth information of tobacco must be obtained rapidly to support in time the decision on production management. Hyperspectral remote technique is a determination technique to rapidly obtain the growth state of crop. This study preliminarily described the spectral characteristics of tobacco canopy under different fertilities at different fertilization levels at different growth stages, analyzed the correlation of vegetation index with LAI based on canopy spectrum, and established estimation model. Under different fertilization conditions, the spectral reflectance of tobacco canopy was significantly different, NDVI (green) was significantly related to LAI, and the regression coefficient, association probability and determination coefficient were all significant for established regression. Thus, the screened variable of hyperspectral characteristics was reliable, and relevant model could be used to estimate chlorophyll content and LAI. In the past, many studies were made on wheat, maize and rice to screen corresponding characteristic variable and statistical model [5, 11, 12] . The ratio of near-infrared light to green/red light could significantly improve the sensitivity to parameters of canopy structure. The information of near-infrared light could standardize the established vegetation index, eliminate the influence of canopy structure as far as possible (such as leaf structure, leaf direction and radiation angle), and effectively highlight the information of sensitive waveband (such as green/red light) [13]. In this study, the screened NDVI (green) was also based on vegetation index of near-infrared and green lights, and proven also effective for tobacco. In wheat study of Aparicio et.al. [14], the correlation of LAI with vegetation index was not affected by planting measures, species or planting area. But such results on tobacco should be verified through further test. In this study, SPAD-502 chlorophyll detector was used to rapidly harmlessly determine the chlorophyll content in tobacco. In soybean/maize study of John et.al. [15], the actual amount of chlorophyll extract was very significantly related to the detected data through SPAD chlorophyll detector (R2=0.94). Such results appeared in the study of Carlos et.al. [16]. In the study of Zeng Jianmin et.al. [17], tobacco chlorophyll was extracted through 95% water solution of acetone and anhydrous ethanol (2:1 volume); the chlorophyll content in leaf and SPAD value were at very significantly positive correlation; and the chlorophyll content in leaf was estimated through SPAD value to further track the nitrogen nutritional state in tobacco. In this study, canopy hyperspectral monitored the chlorophyll content and LAI tobacco, rapidly effectively monitored the growth state of tobacco, and provided technological support for tobacco planting and fertilizer management. Of course, the fitting

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equation should be further verified through the tests at different ecosystems and different species, so as to popularize the model and provide theoretic support for large-scale monitoring.

Acknowledgements This work was financially supported by the National Key Technology Research and Development Program of China (No. (2006BAD08A01)) and Henan Tobacco Company Projects Grant (HNKJ200814).

References 1. Shen, G.R., Wang, R.C.: Review of the application of vegetation remote sensing. Journal of Zhejiang University (Agric. &Life Sci. ) 27(6), 682–690 (2001) (in Chinese) 2. Curran, P.J.: Remote sensing of foliar chemistry. Remote sensing of environment 30, 271–278 (1989) 3. Hinzman, L.D., Bauer, M.E., Daughtry, C.S.T.: Effects of nitrogen fertilization on growth and reflectance characteristics of winter wheat. Remote sensing of environment 19, 47–61 (1986) 4. Svetlana, M.K., Taras, A.K.: Changes in the first derivatives of leaf reflectance spectra of various plants induced by variations of chlorophyll content. Journal of Plant Physiology 12(3), 1648–1655 (2007) 5. Daughtry, C.S.T., Walthall, C.L., Kim, M.S., et al.: Estimating Corn Leaf Chlorophyll Concentration from Leaf and Canopy Reflectance. Remote Sensing of Environment 74(2), 229–239 (2000) 6. Elizabeth, J.B., Brigitte, L., Bernie, Z., et al.: Non-destructive estimation of potato leaf chlorophyll from canopy hyperspectral reflectance using the inverted PROSAIL model. International Journal of Applied Earth Observation and Geoinformation 9(4), 360–374 (2007) 7. Bouman, B.A.M.: Linking physical remote sensing model with crop growth simulation models, applied for sugar beet. International of Remote Sensing 13(2), 2565–2581 (1992) 8. Wu, J.D., Wang, D., Marvin, E.B.: Assessing broadband vegetation indices and QuickBird data in estimating leaf area index of corn and potato canopies. Field Crops Research 102(1), 33–42 (2007) 9. Hung, T.N., Byun, W.L.: Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regress. European Journal of Agronomy 24(4), 349–356 (2006) 10. Liu, G.S.: Tobacco Cultivation, pp. 38–39. China Agriculture Press, Beijing (2003) 11. Feng, W., Yao, X., Zhu, Y., et al.: Monitoring Leaf Nitrogen Concentration by Hyperspectral Remote Sensing in Wheat. Journal of Triticeae Crops 28(5), 851–860 (2008) (in Chinese) 12. Vaesen, K., Gilliams, S., Nackaerts, K.: Ground-measured spectral signatures as indicators of ground cover and leaf area index: the case of paddy rice. Field Crops Research 69(1), 13–25 (2001) 13. Xue, L.H., Cao, W.X., Luo, W.H., et al.: Relationship Between Spectral Vegetation Indices and LAI In Rice. Acta Phytoecologica Sinica 28(1), 47–52 (2004) (in Chinese)

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14. Aparicio, N., Villegas, D., Araus, J.L., et al.: Relationship between growth traits and spectral veg2etation indices in Durum wheat. Agronomy Journal 42, 1547–1555 (2002) 15. John, M., John, C.O., Jennifer, L.M.: Calibration of the Minolta SPAD-502 leaf chlorophyll meter. Photosynthesis Research 46, 467–472 (1995) 16. Carlos, C., Lianne, M.D., Pierre, D., et al.: Inter-relationships of applied nitrogen, SPAD, and yield of leafy and non-leafy maize genotypes. Journal of Plant Nutrition 24(8), 1173–1194 (2001) 17. Zeng, J.M., Yao, H., Li, T.F., et al.: Chlorophyll Content Determination and its Relationship with SPAD Readings in Flue-cured Tobacco. Molecular Plant Breeding 7(1), 56–62 (2009) (in Chinese)

Study on Spatial Scale Transformation Method of MODIS NDVI and NOAA NDVI in Inner Mongolia Grassland Hongbin Zhang1,2,3, Guixia Yang1,2,3, Qing Huang2,3, Gang Li1,2,3, Baorui Chen1,2,3, and Xiaoping Xin1,2,3,* 1

Hulunber Grassland Ecosystem Observation and Research Station, Beijing, 100081, China 2 Key Laboratory of Resource Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing, 10008, China 3 Chinese Academy of Agricultural Sciences Institute of Agricultural Resources and Regional Planning, Beijing, 100081, China [email protected]

Abstract. Based on MODIS NDVI and NOAA NDVI datum, covering the primary grassland types of Inner Mongolian in growing seasons from 2000 to 2003, this paper analyzes annual variation rule of the relationship between MODIS NDVI and NOAA NDVI datum. We use the theory of statistics to discuss the spatial scaling methods between different resolutions images of remote sensing in large-scale spatial extent. At the same time, we build spatial scaling model by MODIS NDVI and NOAA NDVI datum of July and August in 2002, and apply it to the 2003’s NOAA NDVI datum, then take the survey datum in field to validate the precision of the model. The result indicates that this spatial scaling method is effective, and the model could be applied to other times. This method makes it scientific and effective to analyze and compare the result of monitor by NOAA NDVI and MODIS NDVI of different times in grassland. Keywords: Spatial scaling, grassland remote sensing, MODIS, NOAA.

1 Introduction At present, remote sensing technology has already become the mainly acquisition method and important study means for macroscopic ecology subject. Because satellite sensor imaging has instantaneity and periodicity features coupled with weather factors (such as cloud, rain, snow and so on) affecting sensor imaging, any sensor can’t supply the enough image datum to covered the whole grassland in any times (Li Xin et al., 2007). That’s say, shortage of time series image in the same region or spatial *

Corresponding author. Address: Chinese Academy of Agricultural Science Insititute of Agricultural Resource and Regional Planning, No.12 Zhongguancun South St., Haidian District, Beijing 100081, China. Tel:+86-10-82109622-138, [email protected].

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image data in the same time for macroscopical ecology system study. Especially in long time serial and large spatial scale ecology system monitoring study, this problem is the most serious. So how to effectively integrate multiple-source remote sensing datum and different resolution (spatial resolution, temporal resolution) image datum for macroscopical ecology monitoring study has become difficulties in current. Especially in recent years, many experts and scholars at home and abroad have already developed search. Mayaux and Lambin adopted two-steps transformation method to make vegetation area scale transformation based on four spatial exponential relations of TM and AVHRR in 1995(Mayaux P et al., 1995). Wang Kaicun and so on analyzed shortwave albedo of MODIS and AVHRR data and compared the differences between them in details in 2004(Wang kaicun et al., 2004). Kevin Gallo and so on analyzed differences of NOAA NDVI and MODIS NDVI in details and built up NOAA NDVI and MODIS NDVI data relational model of types of farmland, grassland, evergreen broad-leaf forest, shrubbery, city in 2005(Kevin Gallo et al., 2005). Bao Pingyong and so on analyzed surface albedo production of same time’s ETM+ and MODIS data and compared differences of surface albedo of forest land, farmland, grassland, water, city, river shoal, exposed soil and exposed rock in 2007(Bao PIngyong et al., 2007). Zhang Wanchang and so on took on scale transformation study based on LAI index inverted from ETM+ data and attempted to bring forward a new more effective scale transformation method based on NDVI unmixed pixels in 2008(Zhang Wanchang et al., 2008). Wang Peijuan adopted ETM+ and MODIS data to investigate spatial scale transformation method of forest coverage net primary productivity in Changbai mountain nature reserve (Wang Peijuan et al., 2007). This paper took Inner Mongolia grassland as an example to study spatial scale transformation method among large scale grassland different resolutions remote sensing data. Because MODIS and NOAA data have lower spatial resolution features (Huang Jiajie et al., 2003; Potte, C et al., 2003; Steven, M. D et al., 2003 ), and survey region is too large, it’s difficult obtain high precision land utilization type map and different types of vegetation pure pixels and so on parameters. So we used the theory of statistics to build spatial scaling model of MODIS NDVI and NOAA NDVI datum, and applied the model to time scale application.

2 Survey Region Overview Inner Mongolia grassland is located in hinterland of Eurasian Continent. It is a vast land and has rich and colorful grass types occupying 22% of our country grassland whole area. at the same time it spans “Sanbei distraction” in our country. It’s important animal breeding production base and a natural green protective screen for north china, it takes the role of protection and improvement our country ecology environment. According to Inner Mongolia autonomous region 1:10000000 grassland type map, whole grassland area is 794239.00 km2. The survey region makes up of Lowland meadow, up-land meadow, temperate steppe, steppe desert, temperate meadow steppe, desert grassland, desert steppe and so on seven types of grassland and whole area is 781587.10 km2 accounting for 98.4% of Inner Mongolia grassland whole area. It is the main body of Inner Mongolia grassland autonomous region.

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Fig. 1. The Grass type of Inner Mongolia

3 Study Method 3.1 Data Resources This paper used the first and second band of MOD09Q1 production provided by NASA. Because MOD09Q1 is 8 days reflectivity production composed by everyday L2G surface reflectivity and affected by weather easily, it is very hard to get the remote sensing data which was not shaded by cloud in a short time (Yan Jianwu et al., 2008; Zhang Lianyi et al., 2008). So this paper applied the maximum value compounding method to obtain monthly NDVI vegetation index data to analyze. The NOAA data we used is 8 km resolution NOAA/AVHRR monthly maximum value compounding NDVI production provided by Global Inventory Monitoring and Modeling Studies. 3.2 MODIS NDVI and NOAA NDVI Data Relation Stability Analysis Because this study is involved with spatial scale transformation model applied in time scale, it is necessary to analyze stability of correlation between MODIS NDVI and NOAA NDVI data in long time scale. It is the basic premise to ensure stability and effectiveness of time scale transformation model. This paper selected monthly maximum compounding NOAA and MODIS NDVI data from April to October, in 2000 to 2003, and use ArcGIS 9.0 to calculate correlation of different months NDVImax between two types of data. Compare fluctuation of correlation in same term and study whether there is stable correlation between them in research area. The results as shown as below:

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Fig. 2. Analysis of the correlation between NOAA NDVI and MODIS NDVI, 2000-2003

From fig 2, we can see correlation of NOAA NDVI and MODIS NDVI data changed with regularity and the correlation was stable in long time scale between them in growing season from 2000 to 2003 except 2001. 3.3 Spatial Scale Transformation Method of MODIS NDVI and NOAA NDVI Because MODIS sensor is superior to AVHRR sensor in spatial and spectral resolution, MODIS NDVI can reflect grassland vegetation condition more truly. So this paper selected down-scale transformation method that from NOAA NDVI to MODIS NDVI and built scaling model. The detailed method is as below. Apply systematic sampling to select study samples in MODIS NDVI and NOAA NDVI remote sensing images. Sampling interval is 40 km. Sampling range of MODIS image was an circular region with 4km radius, take NDVI mean value as MODIS data sampled value. The Sampling range of NOAA image was a pixel (8km*8km), so we can obtain 533 MODIS and NOAA sample datum every month in research area.

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I0.7 V D0.6 N S0.5 I D0.4 O M 0.3

y = 0.9557x + 0.0684

0.7 I V 0.6 D N 0.5 S I D O M 0.4 0.3

0.2

R = 0.8449

y = 0.9679x + 0.0491

0.2

2

0.1

0.1

0.0

0.0

2

R = 0.8726

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 NOAA NDVI

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 NOAA NDVI

Fig. 5. Comparison of NOAA NDVI and Fig. 6. Comparison of NOAA NDVI and MODIS NDVI in Jun MODIS NDVI in Jul 0.9 0.8

1.0 0.9 0.8 I0.7 V D N0.6 S0.5 I D0.4 O M0.3 0.2 0.1 0.0

0.7 0.6 VID N 0.5 SI ODM 0.4 0.3 0.2

y = 0.9906x + 0.0525

y = 0.9905x + 0.044 R2 = 0.8512 0.1 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 NOAA NDVI

2

R = 0.8776

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 NOAA NDVI

Fig. 7. Comparison of NOAA NDVI and Fig. 8. Comparison of MODIS NDVI in Aug MODIS NDVI in Sep

NOAA NDVI and

0.8 0.7 0.6 I V0.5 D N S0.4 I D OM0.3 0.2

y = 1.1524x + 0.0191 R2 = 0.7438

0.1 0.0 0.0

0.1

0.2

0.3

0.4 0.5 NOAA NDVI

0.6

0.7

0.8

Fig. 9. The Comparison of NOAA NDVI and MODIS NDVI in Oct

From fig 3 to 9, MODIS NDVI and NOAA NDVI data of grassland vegetation in April to October have well linear correlation which increase start at April, reached a peak in August and decrease start at September. As correlation was concerned, April was at lowest, May and October were almost the same, but October was slightly higher than May; June and September were almost the same, but September was

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slightly higher than June; July and Aug were almost the same, but Aug was slightly higher than July; May and October were significantly higher than April; June to September were significantly higher than April, May and October; July and August were significantly higher than June and September. The change trend of linear correlation between MODIS NDVI and NOAA NDVI data was truly in response to grassland vegetation condition in research area (Xu Bin et al., 2007). The grassland began to growing in April and at this time, the grassland vegetation coverage and biomass were the lowest in growth season. So NDVI index of grassland was very small, when the influence of soil background noise etc on AVHRR and MODIS sensors monitoring relatively the largest, more mistakes resulted. Especially NOAA NDVI data, because of uncertainty of calibration parameter, rang of mid-value and low value excelled 20%. All of the facts disturbed MODIS NDVI and NOAA NDVI data to obtain the parameters reflecting grassland vegetation condition to a great degree. So NDVI data has too much noise in April and correlation is the worst. In May grassland vegetation coverage and biomass were better than April. The influence of factors of soil background noise etc on AVHRR and MODIS sensors monitoring began to weaken. So correlation of MODIS NDVI and NOAA NDVI rose notably compared with April. In June, grassland vegetation coverage and biomass had a further marked increase and correlation continued to strengthen. July and august were the two months in which grassland vegetation condition were the best in the whole growth season. So correlation of July and August were higher than the other months and grassland vegetation condition in July was better than that in August. But because saturation effect of AVHRR and MODIS sensor bothered to obtain NDVI data reflecting truly grassland vegetation condition, correlation of August was a little higher than that in July. In September, grassland vegetation coverage and biomass began to decrease, but because there was a lot of hay in research area which decreased the influence of soil background noise on AVHRR and MODIS sensor monitoring, association relationship of September was just a little higher than June. In like manner, correlation of October was just a little than May.

4 Spatial Scale Transformation Method Application and Verification The NDVI values of July and August represented the maximum value all the year round in research area. So NDVI value of July and August were applied the most widely in biomass yield estimation. Because of shortage of ground surface survey data except July and August, this paper mainly validated the precision of spatial scale transformation model between MODIS NDVI and NOAA NDVI data of July and August in 2002, according to the spatial scale transformation discussed in 3.3, linear spatial transformation model was built up and extrapolated to NOAA NDVI data of July and August in 2003. Taken use of MODIS NDVI and NOAA NDVI data and survey data to validate and verify spatial scale transformation precision when extrapolated to time scale and practicability. Specific process as follows: Adopt systematic sampling method, draw 533 samples in 8km resolution NOAA NDVI and 250 mm resolution MODIS NDVI of July and August in 2002. According to linear correlation, built up spatial scale transformation model of NOAA NDVI and

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MODIS NDVI in July and August (table 1). Take advantage of this model and Erdas 9.0 to resample pixels (to 250 m) and linear process to obtain transformed 250m NOAA NDVI data of July and August in 2003. Use survey datum of July and August in 2002 and MODIS NDVI data of July and August in 2002 to build up regression model to get the regression formula: Y=11.2620+179.6915X (Y: dry biomass; X: MODIS NDVI). Input above transformed 250m resolution NOAA NDVI data to model and generate grassland vegetation biomass distribution map of July and August in 2003 separately. Use survey datum of July and August in 2002 and untransformed 8km resolution NOAA NDVI data to build up regression model to get the regression formula: Y=25.9050+129.6058X(Y: dry biomass；X:NOAA NDVI vegetation index). Input above uncorrected 8km resolution NOAA NDVI data in 2003 to model and get ground biomass distribution map in research area of July and August in 2003. Table 1. Spatial Scaling Model of MODIS NDVI and NOAA NDVI in 2002

Spatial Scaling model

R2

July

Y=0.028442+0.975085X

0.93

Sum of Squared Residuals 2.22

August

Y=0.027654+0.983479X

0.93

2.29

：Y:MODIS NDVI X:NOAA NDVI

Note

At last, take advantage of survey datum of July and August in 2003 (29 ground surface samplings in July and 52 ground surface samplings in August) to verify biomass distribution map and analyze precision difference. The results as follows: Table 2. Result of Verify Spatial Scaling Model of NOAA NDVI and Biomass, Jun and Jul 2003 Mean relative bias Δ

NOAA NDVI Untransformed July data in 2003 Transformed July data in 2003 Untransformed August data in 2003 Transformed August data in 2003 Untransformed July and August data in 2003 Transformed July and August data in 2003

：mean relative bias： Δ

Note

=

∑

d

/ x

i = 1

V

=

0.36 0.35 0.28 0.27

bias V 38.87 40.44 47.34 42.08

0.31

44.49

0.29

41.75

n

(

∑

i = 1

i

) / n

；mean absolute bias：

2

n

(

Mean absolute

d

) / n

，x is measured value，n is sampling quantity

d is the difference of predicted value and measured value

Results of verify in table 2 indicated that NOAA NDVI in 2003 transformed from 2002 by spatial scale transformation model had very high monitoring precision to

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grassland biomass and had improved compared with untransformed data. So the spatial scale transformation model can extrapolated in time scale.

5 Conclusions and Discussion 1. On the whole, MODIS NDVI was more than NOAA NDVI in value, and they both had high relativity in growth season. The relativity mainly affected by vegetation growth climate condition. That is because when climate condition was better, high coverage of grassland can reduce noise influence of soil background on AVHRR and MODIS sensors imaging. On the contrary, when grassland vegetation condition was bad, noise influence of soil background on AVHRR and MODIS sensors imaging would be increased, resulted correlation of MODIS NDVI and NOAA NDVI decreased. So in research area, correlation of July and August were the highest and April was the lowest. 2. Linear spatial transformation model built up based on MODIS NDVI and NOAA NDVI was stable in time dimension in some degree. After time scale transformation model was extrapolated application, the transformed NOAA NDVI data kept higher monitoring precision of grassland biomass to indicate the model could be extrapolated in time scale. Because vegetation condition in July and August were the best, association degree between MODIS NDVI and NOAA NDVI data were the highest. So stability of spatial scale transformation model built up based on MODIS NDVI and NOAA NDVI in July and August was the best when extrapolated in time scale. 3. This paper based on statistics method, making use of association relationship of 250m resolution MODIS NDVI and 8 km resolution NOAA NDVI data, spatial transformation model between MODIS NDVI and NOAA NDVI data was built up. But because this method self did not take 8 km resolution NOAA NDVI data big pixel inner heterogeneity in to account. So relative precision of 250mm resolution small pixel of NOAA NDVI data in 8 km large pixel region was not prompted and this was the shortcoming of the method. In so large spatial scale survey region, conflicted by so many basic data serious limits (such as high precision land utilization map shortage, big mistake in spatial adjustment of low resolution data, different type vegetation pure pixel shortage) It was difficult to adopt other scale transformation method taken spatial heterogeneity into account. Under the premise of ensuring total scale transformation precision, the method adopted by this paper was a scale transformation feasible scientific method in large space range different resolutions remote sensing data.

Acknowledgements This work is supported by Special Fund Project for Basic Science Research Business Fee, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences and The National Science & Technology Program (Grant No. 2006BAC08B0404 、 2007BAC03A10) and Project 863 of China: (Grant no.2007AA10Z230) and National Natural Science Foundation of China (Grant No: 30770327) and Commonweal Industry Scientific Research Special Funds Project (GYHY200906029-2).

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References 1. Li, X., Huang, C., Che, T., et al.: Progress and Foresight of Chinese land Data Assimilation System Study. Nature Science Progress 17(2), 163–173 (2007) 2. Mayaux, P., Lambin, E.F.: Estimation of tropical forest area from coarse spatial resolution data: A two-step correction function for proportional errors due to spatial aggregation. Remote Sensing of Environment 53(1), 1–15 (1995) 3. Wang, K., Liu, J., Zhou, X., et al.: Retrieval of the surface albedo under clear sky over China and its characteristics analysis by using MODIS satellite date. Chinese Journal of Atmosphere Sciences 28(6), 941–949 (2004) 4. Gallo, K., Ji, L., Reed, B., Eidenshink, J., Dwyer, J.: Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data. Remote Sensing of Environment (99), 221–231 (2005) 5. Bao, P., Zhang, Y., Gong, L., et al.: Study on consistency of land surface albedo obtained from ETM+ and MODIS. Journal of Hehai University (Natural Sciences) 35(1), 67–71 (2007) 6. Zhang, W., Zhong, S., Hu, S.: Spatial scale transferring study on leaf area index derived from remotely sensed data in the Heihe river basin, China. Acta Ecologica Sinica 28(6), 2495–2503 (2008) 7. Wang, P., Xie, D., Zhang, J., et al.: Spatial scaling of net primary productivity based on process model in Changbai Mountain natural reserve. Acta Ecologica Sinica 28(8), 3215– 3223 (2007) 8. Huang, J., Wan, Y., Liu, L.: The character and application of MODIS. Geospatial Information 1(4), 20–28 (2003) 9. Potte, C., Tan, P.N., Steinbach, M., Klooste, S., Kumar, V., et al.: Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology (9), 1005–1021 (2003) 10. Steven, M.D., Malthus, T.J., Baret, F., Xu, H., Chopping, M.J.: Intercalibration of vegetation indices from different sensor systems. Remote Sensing of Environment (88), 412–422 (2003) 11. Yan, J., Li, C., Yuan, L., Chen, Q.: Application summary of EOS-MODIS data in the monitoring of grassland resources. Pratacultural Science 25(4), 1–9 (2008) 12. Lianyi, Z., Gang, W., Luru, B.: Temporal changes of MODIS-NDVI vegetation index and forage biomass in Xilinguole grassland- taking the change from April to September in 2005 as a sample. Pratacultural Science 25(3), 6–11 (2008) 13. Xu, B., Tao, W., Yang, X., et al.: Monitoring by remote sensing of vegetation growth in the project of grassland withdrawn from grazing in countries of China. Acta Prataculturae Sinica 16(5), 13–21 (2007)

Study on Storage Characteristic of Navel Orange Based on ANN Junfang Xia and Runwen Hu College of Engineering and Technology Huazhong Agricultural University, Wuhan 430070 China

Abstract. In order to predict storage life of navel orange, The model for the variable regularity of total soluble sugar, total acidity, vitamin C, soluble solids, the sugar-acidity ratio in navel orange according to storage time was established based on BP artificial neural network . The results show that the multi-factor BP artificial neural network model has better predicted effect than single-factor one. When the number of the hidden layer neuron is 8, the multi-factor BP artificial neural network model of total soluble sugar, total acidity, vitamin C, soluble solids, the sugar-acidity ratio according to storage time was the most accurate, the correlation coefficient R between prediction and true value of storage time reached 0.98, the prediction and true value of the model was 0.99. As a result, the multi -factor BP artificial neural network model could be used to predict the navel orange storage life. Keywords: Navel orange; storage life; BP artificial neural network.

1 Introduction The quality of navel orange such as total soluble sugar, total acidity,VC,soluble solids, the sugar-acidity ratio will change along with storage. Grasping this regulation, finding out notable quality index related to storage and establishing prediction model about quality can provide the theoretical basis for predicting storage life of navel orange, control internal quality effectively, prevent deterioration of fruit, preserve the value of navel orange and make appropriate market decision in time. Variation of orange quality is nonlinear according to the time of storage, and the traditional linear regression model is difficult to build such a nonlinear system. Artificial neural network method is to simulate the thinking way of human being based on the working principle of human brain cells, so it can fully approach any complicated nonlinear relationship, and has powerful potential to solve the system control of highly nonlinear and serious uncertainty [1-6]. In order to establish artificial neural network model, Rumelhert et al presented BP Algorithm Used in Multilayer Neural Network[7], BP artificial neural networks are mostly applied in artificial neural network, The algorithm got very extensive application in nonlinear regression, Most of studies are about the BP artificial neural network [8] . For example, Liu Jianxue et al[9] used BP neural network method to establish protein D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 667–673, 2011. © IFIP International Federation for Information Processing 2011

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prediction model of different types and sizes of rice samples, investigated the prediction ability of the model, and reported that there were good correlation (with the correlation coefficients above 0.9) between the predictive value and the value of chemical analysis using traditional methods. Lin Ming,Zhao Chen,Liu Xuesong,Bai Yingkui,Tang Yanfeng,Yang Nanlin et al determined the contents of corn, Cordyceps amino acids, Amino Acids in Cordyceps Sinensis,vanilla activity, VC Yinqiao Tablets and Anthraquinone in Rhubarb by the method of Artificial Neural Network- near-infrared spectroscopy. The results showed that the method was an effective and practical method of non-linear correction method, and the standard deviation of their forecasts are better than the processing results by principal component regression and partial least squares regression linear models[10-15]. In this study, the variety model of quality of navel orange along with storage time is based on artificial neural network, which helps to forecast the storage time and storage life by the change of quality of navel orange.

2 Materials and Methods 2.1 Materials The test samples are 200 mature navel oranges picked randomly from many trees in Dingnan in Jiangxi province in December 7, 2007, encapsulated by plastic film Bag, refrigerated in the artificial climate box with the temperature of 5°C and humidity of 70%. At interval of 10 days, randomly choosed 10 navel oranges were determined total soluble sugar, total acidity, vitamin C, soluble solids and the sugar-acidity ratio, the calculated average value and the storage time(day) were uesd as input vector P and output vector T of neural network respectivly. 160 of the 200 samples were used as training set and 40 as validation set. 2.2 Chemical Analysis The measurement of the soluble solids content in navel oranges was carried out by WYT-1 handhold saccharimeter; the contents of total soluble sugar were determined by phenol sulfuric acid method, the total acid contents were detected by aid-base titration method, and the method of 2,6-dichlorophenol indophenol was used to measure the contents of vitaminC. 2.3 Determination of Storage Life The observation of the stored orange showed that Mildew spots appeared on the skin of a small number of orange samples at 170 days. At 180 days, black spots appeared on the skin of about 20% of the navel oranges, this phenomenon indicated that the navel oranges had been deteriorated and could not be used as food. Therefore, the critical value of storage life of navel orange should be 170 days. This article will find index of internal quality related with the storage time significantly and estabilish the correlation

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model of characteristic of navel oranges to storage time so as to predict the storage time of navel orange. the continued storage time could be got by critical value of storage time subtracts the storaged time, that is storage time. 2.4 Structure of BP Artificial Neural Network The multi-player feed-forward neural network of directional transfer algorithm was applied to BP artificial neural network which contained input layer, hidden layer and output layer. In this study, both of the structural input layer and output layer of BP artificial neural network model for quality characteristics of navel orange to storage time (single factor) are unit-neuron. Input layer neuron is the internal quality index, the output layer neuron is the storage time. As shown in Fig.1,The input layer of BP artificial neural network model of multi-factor quality of navel orange according to storage time is 5 neurons, output layer is single neuron,as shown in Fig.2.

Fig. 1. The BP artificial neural network of single Fig. 2. The BP artificial neural network of factor multi factor

2.5 Network Training Parameter In this study, Levenberg-Marquardt algorithm was used. S-transfer function was adopted to hidden layer neurons, the transfer function was tansig function; The output layer was cell neuron, linear transfer function was used in neuronal function and trainlm function was used in training function to generate the neural network of initialization. Before the training, the sample must be ascertained and should includ input P and corresponding output T. During the training procedure, weight and threshold should be adjusted constantly to make the average variance mas of neural networks output and expection output vector to be the minimum. The definition of network training

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parameters: the max training times epochs was set as 200; the training iterative process show as 100; this training required the accuracy as 1e-10; learningrate lr was demanded 0.01;the min gradient as 1e-6; the max failure times must be under 5[7].

3 Data Processing and Analysis 3.1 Establishment of Single-Factor Artificial Neural Network Model Internal quality indexes such as total soluble sugar content, total acidity, vitamin C, soluble solids and the sugar-acidity ratio of navel orange was taken as the input vector P and the storage time as expected output vector T of neural network. According to the programming and neural network training of definite network training parameters in the software MATLAB6.5, the quality to storage time of single-factor BP artificial neural network model for navel orange was established. The number of hidden layer neuron was initiativly set as 5 with the method of trial and error. Begainning with smaller neuron,we trained and inspected the capability of internet. As the number of neuron increased gradually, the capability of internet was trained and inspected again untill the the related coefficient Rc of output and expect value of artificial neural network model reached the ideal value. Right now, the number of the neuron of hidden layer is perfect. The characteristics of total soluble sugar content, total acidity, vitamin C, soluble solids and the sugar-acidity ratio of the 40 untraining samples were put into the neural network model established to predict the storage time. the conservasion of the established model will be analysed through comparing with true value. The best hidden layer in the BP artificial neural network model for total soluble sugar content, total acidity, vitamin C, soluble solids, the sugar-acidity ratio of navel orange according to storage time using was in table 1 as well as the effect on prediction and the proven results. In table 1,N is the number of best hidden layer, Rc is the related coefficient of predicted value of calibration model and measured value, Rp is the related coefficient of predicted value of validation model and measured value, Wp is the relative error of predicted value of the validation model and measured value. Table 1. Predictive results of the single factor BP artificial neural network model

Total soluble sugar Total acidity Vitamin C Soluble solids Sugar acid ratio

N 60 50 50 30 60

Rc 0.864 0.984 0.82 0.933 0.89

Rp 0.88 0.9814 0.8648 0.9343 0.9

Wp(%) 7.34 0.84 7.99 7.32 9.01

The model’s training research indicated that the number of neura in hidden layer of artificial neural network model had much influence to model’s accuracy and was accordng to different quality index of hidden layer of artificial neural network model.

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The model’s training time is related to the number of hidden layer neuron, the more of the number of hidden layer neuron,the longer of the training time; Among all model trainings,when the training time epochs was about 20,the mean square error of output layer reached the target error that training parameters demanded,and kept stable, not changed with epochs’s increasing. In the single-factor BP artificial neural network mode of navel orange’s internal qualities, total soluble sugar, total acidity, vitamin C, soluble solids and the sugar-acidity ratio according to storage time in BP artificial neural network mode were respectivly 60, 50, 50, 30, 60. The prediction accuracy and stability of the navel orange’s internal qualities to storage time in BP artificial neural network model with signal factor from high to low in sequence were total acidity BP artificial neural network model→soluble solids BP artificial neural network model→sugar-acid ratio BP artificial neural network model→total soluble sugar BP artificial neural network model→BP artificial neural network model of vitamin C. The result showed that the most obvious criterion of quality related to storage time in signal factor is total acidity.When the quantity of hidden layer neuron is 50,the total acidity to storage time of BP artificial neural network model accuracy is perfect,the related coefficient Rc can reach 0.984,the related coefficient Rp of validation model’s predicted value and measured value is 0.9814,the average value of relative error is 0.84%,the predictive effect is much better, so the neural network model established is stable, and can satisfy the prediction requirment of navel orange’s storage life. 3.2 The Establishment of the Multi-factor BP Artificial Neural Network The five indexes such as total soluble sugar content, total acidity, vitamin C, soluble solids, sugar-acid ratio of navel orange are taken as input vector P of neuron network, the number of input layer neuron is 5 and the storage time is used as expected output vector T. According to the definite network training parameters, programming and network training in the software MATLAB6.5, we established the multi-factor BP artificial neural network model. The method of trial and error was adopted to ascertain the optimum value of the hidden layer neuron’s quantity. After input the multi-factor indiex of the 40 navel oranges’ internal qualities untraining into the established neural network model and predict its storage time, the stability of the established model was analysed according to the comparison with measured value. The reaserch of model training indicates that the multi-factor BP artificial neural network dynamic model by the 5 internal qualities of total soluble sugar content, total acidity, vitamin C, soluble solids, sugar-acid ratio of navel orange according to the storage time is established ,when the number of hidden layer neuron is 8, the model accuracy is much high, the correlation coefficient Rc reach 0.98,the training time is shorter, the correlation coefficient of validation model’s predicted value and measured value Rp is 0.99,the average value of relative error is 6.701%, predictive effect is much better,the accuracy and stability of the established neural network model is better than single factor model which indicates that multi-factor changes can better reflect the variation of navel orange’s internal qualities to the storage time.using the multi -factor

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BP artificial neural network model to forecast the storage time and storage life of navel orange is appropriate.

4 Conclusion The variety model of total soluble sugar, total acidity, vitamin C, soluble solids, the ratio of total soluble sugar to total acidity in navel orange according to storage time was established based on BP artificial neural network.. In the training of BP artificial neural network model, through changing the quantity of hidden layer neuron, the optimized number of the hidden layer neurons of the internal qualities of navel orange of BP artificial neural network with single-factor and five multi-factor was N, and verify the established model.The research results as follows. (1) The best forecast accuracy and stability of navel quality to storage time in single-factor BP artificial neural network model is total acidity of BP artificial neural network model , Therefore,in the single factor ,the index of quality which highlightly related to storage time is the total acidity of navel orange.And when the quantity of hidden layer neuron is 50,the accuracy of total acidity and storage time’s BP artificial neural network model is much higher, the correlation coefficient Rc reach 0.984,the correlation coefficient Rp of validation model’s predicted value and measured value is 0.9814,the average value of relative error is 0.84%. (2) When the number of hidden layer neuron is 8, the BP artificial neural network dynamic model which based on the five quality characteristies of total soluble sugar, total acidity, vitamin C, soluble solids and sugar acid ratio has the best prediction. The correlation coefficient Rc can reach 0.98, correlation coefficient Rp of validation model’s predicted value and measured value is 0.99, the average value of relative error is 6.701%. (3) The association between multi-factor changes and storage time is most notable,the predictive validity and stability of multi-factor BP artificial neural network model along with storage time is better than single-factor model. it indicates that multi-factor changing effects can best reflect navel orange’s qualities’ variety. In the practical application, multi-factor BP artificial neural network model can be used to predict the storage time and storage life of navel orange.

References [1] Guolin, W., Congming, H.: Application of neural network ensemble based on contractive algorithm in NIR analysis. Chinese Journal of Spectroscopy Laboratory 22(3), 473–476 (2005) [2] Yanbin, W.: Application of artificial neural network in NIR analysis and Dark-colored oil analysis. Research Institute of Petroleum Processing A Dissertation for PH.D (August 2000) (in Chinese) [3] Yong, Y., Ming, Q., Yunhong, L., et al.: Identification of alcohol quality via neural network based on genetic algorithms. Transactions of The Chinese Society of Agricultural Machinery 34(6), 104–106 (2003) (in Chinese)

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[4] Guangjun, Z.: Applications of artificial neural network to opto-electric measurements. Journal of Beijing University of Aeronautics and Astronautics 27(5), 554–568 (2001) (in Chinese) [5] Changhong, D., Matla, B.: Applications of neural network. National Defence Industry Publishing, Beijing (2005) (in Chinese) [6] Fuqiang, L., Wencui, Z., Ge, L., et al.: Nondestructive quality control of rutin pharmaceuticals by near infrared reflectance spectroscopy and artificial neural network. Chemical Analysis and Meterage 12(3), 11–13 (2003) (in Chinese) [7] Fei Sike Technology R & D Center. MATLAB 6.5 Neural Network Analysis and Design of Auxiliary. Electronics Publishing, Beijing (2005) (in Chinese) [8] Xiaoming, Q., Luda, Z., Xiaolin, D., et al.: Quantitative analysis using NIR by building PLS-BP model. Spectroscopy and Spectral Analysis 23(5), 870–872 (2003) (in Chinese) [9] Jianxue, L., Shouyi, W., Ruming, F.: Determination of protein content of rice by near infrared spectroscopy based on neural networks. Journal of Jiangsu University 25(3), 196–198 (2004) (in Chinese) [10] Ming, L., Jin, L.: Determination on components of corns based on neural networks and near infrared spectrum. Infrared Technology 26(3), 78–81 (2004) (in Chinese) [11] Chen, Z., Haibin, Q., Yiyu, C.: A new approach to the fast measurement of content of amino acids in cordyceps sinensis by ANN-NIR. Spectroscopy and Spectral Analysis 24(1), 50–53 (2004) (in Chinese) [12] Xuesong, L., Haibin, Q., Yiyu, C.: Determination of Active Components in a Natural Herb with Near Infrared Spectroscopy Based on Artificial Neural Networks. Chem. Res. Chinese. U 21(1), 36–43 (2005) [13] Bai, Y., Shen, X., Ding, D.: Two-component nondestructive analysis of VC Yinqiao tablets with NIR and bp neural network. Laser & Infrared 34(5), 354–356 (2004) (in Chinese) [14] Yanfeng, T., Zhuoyong, Z., Guoqiang, F.: Identification of official rhubarb samples based on NIR spectra and neural networks. Spectroscopy and Spectral Analysis 24(11), 1348–1351 (2004) (in Chinese) [15] Nanlin, Y., Yiyu, C., Haibin, Q.: Quantitative determination of mannitol in cordyceps sinensis using near infrared spectroscopy and artificial neural networks. Chinese Journal of Analytical Chemistry 31(6), 664–668 (2003) (in Chinese)

Study on the Differences of Village-Level Spatial Variability of Agricultural Soil Available K in the Typical Black Soil Regions of Northeast China Weiwei Cui and Jiping Liu* Tourism and Geographical Science College of Jilin Normal University, 136000, Siping, China [email protected], [email protected]

Abstract. The spatial variability of soil nutrient is very important to the application of fertilizer, the sampling density of precision agriculture, and the sub-area of residence management of precision agriculture etc. With the examples of the agricultural soils of No.13 Village of Gongpeng Town and Xiguan Village of Enyu Town in Yushu City which are the typical black soil regions of Northeast China, applying semi-variance model and spatial autocorrelation model combined with GIS technology, a research on the differences of village-level spatial variability of agricultural soil available K is carried out in the thesis, which shows that the differences of averages and coefficients of variation in the same village are small, but large between different villages. The values of nugget and sill are close to the maximum ranges in No. 13 Village, but they are relatively different between different villages. The spatial autocorrelation of No. 3 and No. 7 lands in No. 13 Village are stronger than that of Xiguan Village. Keywords: Available K; Spatial Variability; Village-level Differences; Black Soil Regions of Northeast China; Precision Agriculture.

Researches on spatial availability of soil nutrient and management technology of precision agriculture have been very hot and have progressed rapidly in recent years [1]. With the putting forward and development of precision agriculture, the research on spatial variability of soil nutrient has already become one of the hotspot concerning modern soil sciences [2, 3]. The spatial variability of soil nutrient is very important to the application of fertilizer, the sampling density of precision agriculture, and the subarea of residence management of precision agriculture, which is the most primary and foremost issue for the research of precision agriculture and has a great scientific meaning and research value. There are fairly a number of researches on spatial variability of soil nutrient related to small-scale lands of plot-level and large-scale lands (e.g. countylevel) [2, 4 -15], but rather few related to the lands of village-level [12], and even fewer *

Corresponding author. Foundation Item: Jilin Province Science and Technology Support Program (20080207); Jilin Normal University Graduate Innovative Research Projects (S09010125).

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 674–681, 2011. © IFIP International Federation for Information Processing 2011

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related to the lands under the same natural conditions both at home and abroad. Taking the spatial variability of soil available K as an example, by applying statistical analysis technique and geographic information system (GIS) technology, the differences of village-level spatial variability of agricultural soil nutrients are researched which supplies a scientific basis for the management and rational fertilizing of precision agriculture, meanwhile, the research on the spatial-temporal variability of soil available K lays a foundation for the scientific management of soil available K and rational fertilizing [16]. Through the spatial statistical analysis which takes sampling point as basic information resource, a theoretical optimized soil sampling density is put forward, and then, combined with an economic and rational consideration, a feasible soil sampling density will be made, which is one of the effective methods for designing a soil sampling plan [17]. Geo-statistics is a relatively good method to study the spatial variability characteristics of soil property, and has been widely applied in recent years [18]. An analysis combined by applying GIS can further clarify the situation of soil available K of the land, reflect the law of spatial variability of soil available K, and offer necessary means to explain the spatial distribution characteristics of soil available K.

1 Research Regions and Methods 1.1 An Introduction to the Region Yushu City is a typical black soil region of Northeast China, which, thus, is selected as the research region in the thesis. Yushu City lies in the North Central Jilin Province, which is the center of the triangle district formed by three big cities: Changchun, Jilin and Harbin, with 30 towns, 4 sub-district offices, 388 villages, a population of 122 ten thousand, a total area of 4722 km2, and an agricultural acreage of 290,700 hm2 covering 68% of the area of the whole city, and is a typical black soil region. Three agricultural plots in Gongpeng Town and Enyu Town of Yushu City are taken as research objects (Figure 1). Gongpeng and Enyu towns are two neighboring towns which have similar natural conditions. The research regions belong to the temperate zone which is sub-humid and mild, with clear four seasons, sufficient sunlight (percentage of sunlight amounts to 60%) and an average annual sunshine of 2800h, an effective accumulated temperature of 2800 , the annual lead wind direction of southwest with a maximum speed of 3m/s, and an average annual rainfall of 620mm, which offer preferable weather conditions to the growth of crops and cash crops.

℃

1.2 Research Methods Sampling is carried out in No. 3 and No. 7 lands of No. 13 Village in Gongpeng Town and in No. 9 Land of Enyu Town in the thesis (Figure 1), and the sampling interval is 40m×40m, according to Quincunx Sampling Method, 72 samples are taken from each land (12 rows time 6 columns), and there are 216 samples taken in total. And then, extractions with the help of ammonium acetate—flame photometry will be applied to test the content of available K in these samples. A routine statistical analysis will be made on the data of soil available K of each plot through SPSS15.0, the

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Fig. 1. Location Diagram of Research Regions

semi-variance model of each plot will be worked out by applying GS+ software, and then a contrastive analysis will be carried out, with the help of GeoDa software, the overall Moran’s I index of soil available K of all plots will be figured out and then be checked.

2 Results and Analyses 2.1 Analysis of the Characteristics of Descriptive Statistics A descriptive statistical analysis is carried on towards the soil available K of each plot by applying SPSS15.0 in the thesis (Table 1). The average value of soil available K changes a lot between different plots, but the average value of No. 3 Land is not much different from that of No. 7 Land in No. 13 Village, but there is a great difference between these two averages and that of No. 9 Land of Xiguan Village. The variation coefficient reflects the relative degree of variation of the variable quantity, which is small between No. 3 Land and No. 7 Land in No. 13 Village, but there is a great difference between them and that of No. 9 Land of Xiguan Village. Soil belongs to a natural continuum, and spatial variability is a kind of natural attribute of it [19]. The variation degree of soil depends on its forming process and its balance between time and space. Due to the influences of some factors like human activities, soil available K has the nature of spatial variability. For the

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reason that peasants still fertilize according to their experience, and the peasants in the same area, due to the influence of primary farming experience, fertilize the same quantity and type of fertilizer in the same land, which leads a relatively great difference between different villages. Table 1. Descriptive Statistical Analysis of Each Plot Item

No. 9

No. 3

No. 7

72

72

72

Max

295.3

167

171

Min

215.8

136

137

Average

253.943

152.36

157.22

Std.

19.3104

6.488

6.059

Mea.

0.29

-0.378

-1.147

Kurtosis

-0.105

-0.45

1.899

Cov.

0.076

0.043

0.039

No.of Samples

(Std.: Standard Difference;Mea.:Measure of Skewness;Cov.: Coefficient of Variation) 2.2 Selection and Analysis on Semi-variance Model The semi-variance model of each plot is worked out by applying GS+ software (Table 2, Figure 2). By adopting the semi-variance structure of soil available K calculated through GS+ software, and analyzing on the residual errors of all models, we finally choose the optimal one to further launch an analysis on spatial variability. With respect to the determination coefficient (Table 2), those of the two mentioned lands in No. 13 Village are much larger than that of No. 9 Land of Xiguan Village. The ratio between nugget and sill indicates the degree of spatial variability, and if the ratio is high, the degree of spatial variability caused by the stochastic part is relatively great; otherwise, the degree of spatial variability caused by spatial autocorrelation part is relatively great [1]. The relatively high ratio between nugget and sill in Xiguan Village illustrates that the degree of spatial variability caused by the stochastic part is relatively great, which shows that human factors play a leading role in the influences on the spatial variability of soil available K; while, the relatively low ratios between nugget and sill of No. 3 Land and No. 7 Land in No. 13 Village illustrate that the degree of spatial variability caused by spatial autocorrelation part is relatively great, and natural factors have much greater effects on No. 13 Village. The maximum correlation, namely distance refers to the variable function; from which we can see that the models of No. 3 Land and No. 7 Land in No. 13 Village are relatively close to each other, while the range of Xiguan Village is relatively different from those of the two lands in No. 13 Village.

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Plot

Model

Nug.

Sill

No. 3

Glo.

13.7

46.98

No. 7

Exp.

9.42

No. 9

Lin.

299.8

Nug.

Max

C De.

RE.

0.29

199.3

0.984

10.25

34.71

0.27

139.8

0.837

20.5

430.22

0.70

396.13

0.558

8987

(Glo.: Linearity;Exp.: Exponential; Lin.:Globular; Nug.:Nugget ; Nug.S:Nugget/Sill; C De.:Coefficient of Determination ;RE.: Residual Error).

Fig. 2. Diagram of Semi-variance Model

2.3 Spatial Autocorrelation The spatial autocorrelation characteristics of each plot are figured out by applying GeoDa software (Table 3). It can be seen from Table 3 that the significant levels of spatial overall autocorrelations of the two mentioned lands in No. 13 Village are

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higher than that of Xiguan Village, which demonstrates that the constitutive property of No. 13 Village is relatively stronger, but the randomness of Xiguan Village is relatively stronger. It can be also seen that, the two mentioned lands of No. 13 Village takes high-high aggregations as the main, aggregate type, while those in Xiguan Village are comparatively well-distributed, which indicates that the differences within a same village are small, but large between villages. Comparisons are made between spatial autocorrelation of various soil available K, the d uniformity degree of soil available K between regions and between plots is fairly different. The spatial distribution of soil available K is directly related to the peasants’ fertilizing habits, types of crops and management level. When the sampling interval does not surpass the maximum range of spatial autocorrelation of available K, the sampling and analysis towards various available K are relatively reliable and can be taken as the foundation for precision fertilizing. Table 3. Spatial Autocorrelation and Its Aggregate Type

Sap.

Sig.

Moran’I

H-H

L-H

L-L

H-L

No. 9

0.02

0.1011

22

17

19

13

No. 3

0.001

0.3830

32

11

20

7

No. 7

0.005

0.1555

33

12

12

15

(Sap.:Sampling Plot ;Sig.:Significant Level ;H-H: High-high Aggregation ;L-H :Lowhigh Aggregation ;L-L :Low-low Aggregation ;H-L: High-low Aggregation).

3 Conclusion With the example of spatial variability of soil available K, through comparing the differences between averages, coefficients of variation, ratios of nugget and sill, maximum ranges, spatial autocorrelations and aggregate types of soil available K of the plots in the same village and between different villages, the differences of villagelevel spatial variability of agricultural soil nutrients are studied, which shows that, through the long-term human influences on farmland, the difference of available K between the two mentioned lands of No. 13 Village is small, while, the difference of soil available K between No. 13 Village and Xiguan Village is relatively larger, which illustrates that human activities have changed the natural differences of soil of No. 13 Village, but have not changed those of soil between different villages. In the past researches, only changes of soil nutrients in the same plot or village have been considered, while, the differences of soil nutrients between different villages had not been taken into account. The research on spatial variability degree of soil available K in the thesis and the research on spatial variability of soil available K through collecting and analyzing soil samples can effectively supply practical

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assistance for the fertilizing of precision agriculture, and further ensure smooth agricultural production. Because the sample-taken areas are only selected plots in two villages, the number of sampling data is small; while only the character of data themselves is considered during the process of selecting models instead of the essential character of the matter, therefore, other software would be selected to solve similar problems in future researches.

References 1. Jiang, C.: Research on the Law of Spatial Variability of Soil Available K and Its Management Technology under Different Operative Mechanism. Graduate Institute of Chinese Academy of Agricultural Sciences (June 2000) 2. Haneklaus, S., Ruehling, I., Schroder, D., Schnug, E.: Studies on the Variability of Soil and Crop Fertility Parameters and Yields in DifferentLandscapes of Northern Germany. In: Stafford, J.V. (ed.) 1st European Conf. Precision Agriculture, vol. lII, pp. 785–792. BIOS Scientific Publishers Ltd., Braunschweig (1997) 3. Jin, J.Y.: Precision Agriculture and Its Application Prospects in China. Plant Nutrition and Fertilizers Science 4, 1–7 (1998) 4. Mallarino, A.P.: Spatial Variability Patterns of Phosphorus and Potassium in No-tilled Soils for Two Sampling Scales. Soil Sci. Soc. Am. J. 60, 1473–1481 (1996) 5. Hu, Z.Y., Silvia, H., Liu, Q., Cheng, K., Cao, Z.H., Ewald, S.: Small-scale spatial variability of phosphorus in a paddy soil. Communications in soil Science and Plant Analysis 34, 2791–2801 (2003) 6. Wang, X.F., Zhang, H.: Spatial Variability of Soil Organic Matter. Soils 27(2), 85–89 (1995) 7. Zhou, H.Z., Gong, Z.T., Lamp, J.: Research on Spatial Variability of Soils. Acta Pedologica Sinica 33, 232–241 (1996) 8. Li, J.M., Li, S.X.: Spatial Variability of Some Nutrients in Soil. Research on Agriculture of Arid Region 16, 58–64 (1998) 9. Hu, K.L., Li, B.G., Lin, Q.M., et al.: Characteristics of Spatial Variability of Farmland Nutrients. Agricultural Engineering Journal 15, 33–38 (1999) 10. Yang, Y.L., Tian, C.Y., Sheng, J.D., et al.: Anthropogenic-alluvial Soil Organic Matter. A Primary Exploration on Spatial Variability of Full Dose N, P and K.J. Research on Agriculture of Arid Region 20, 26–30 11. Yang, L.P., Jiang, C., Jin, J.Y.: A Primary Exploration on Precision Management towards Cotton Field Nutrients. Scientia Agricultura Sinica 33, 67–72 (2000) 12. Bai, Y.L., Jin, J.Y., Yang, L.P.: Characteristics and Management of Soil Nutrients Variability of Different Scales. In: Jin, J., Bai, Y. (eds.) Precision Agriculture and Management of Soil Nutrients, pp. 51–57. Land Publisher of China, Beijing (2001) 13. Zhang, Y.S., Lin, Q.M., Qin, Y.D.: Quantified Analysis on Spatial Variability of Soil Nutrients in Large-scale Region. Acta Agriculturae Boreali-Sinica 13, 122–128 (1998) 14. Guo, X.D., Fu, B.J., Ma, K.: Research on Characteristics of Spatial Variability of Soil Nutrients Based on GIS and Geo-statistics—Taking Zunhua City of Hebei Province as an Example. Chinese Journal of Applied Ecology 11, 557–663 (2000) 15. Huang, S.W., Jin, J.Y., Yang, L.P., et al.: Spatial Variability of County-level Grain Field Nutrients. Chinese Journal of Soil Science 33, 188–193 (2002)

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16. Gao, Y.M., Tong, Y.A., Hu, Z.Y., et al.: Research on the Characteristics of Spatial Variability of Village-level Agricultural Soil Nutrients in Yellow Soil Region. Acta Pedologica Sinica 37, 1–6 (2006) 17. Qi, W.H., Xie, G.D., Ding, X.Z.: Research on Soil Sampling Density of Precision Agriculture—Taking Test and Demonstration Base of Shanghai Precision Agriculture as an Example. Chinese Journal of Eco-Agriculture 11, 48–52 (2003) 18. Peng, Z.L., Ze, Y., Li, Z.Y., et al.: The Characteristics of Spatial Variability of Agricultural Soil Available K of Karst Mountainous Areas under Village-scale. Guizhou Agricultural Sciences 36(5), 81–84 (2008) 19. Yang, Y.L., Shi, X.Z., Yu, D.S., et al.: Research on Spatial Variability of Region-scale Soil Nutrients and Its Influencing Factors. Geographical Science 28, 788–792 (2008)

Study on the Management System of Farmland Intelligent Irrigation* Fanghua Li1,2, Bai Wang2, Yan Huang2, Yun Teng2, and Tijiu Cai1,** 1

Forestry College, Northeast Forestry University, Harbin Heilongjiang, P.R China 2 Heilongjiang Water Conservancy Institute, Harbin Heilongjiang, P.R China [email protected], [email protected]

Abstract. To achieve the unification of precise irrigation management, the system integrated GSM wireless communication technology, sensor technology, computer technology, automatic monitoring, and control technology in the study process. The system included the acquisition subsystem, the intelligent decision subsystem, and automatic control subsystem, with the advantages of the GSM internet, such as wide coverage area, powerful property of antiinterference, the remote data transmission was realized. The application of Java development platform and SQL Server 2000 achieved processing of real-time data. By testing and applicating, the system improved management level of agricultural water-saving irrigation for different accumulated temperature region, planting crops, soil type in Heilongjiang Province. Keywords: Wireless data transmission, intelligent decision-making, irrigation automatic control, farmland moisture management.

1 Introduction With the increasingly intensifying water resources scarcity and imbalance between supply and demand, countries actively explore effective water-saving ways and measures in the world [1]. Irrigation agriculture is one of the biggest water consumer in the world, the optimal management of agriculture irrigation can save large amount of water resources. The water resource of China is very deficient and unevenly distributed, which is especially more severe in north regions, therefore, it is imperative to develop water-saving agriculture [2]. Intelligent decision support system used in agriculture started in mid and later period of 70’s in the 20th century, it has become mature gradually after 30 years development[3]. Irrigation automatic control mode has many advantages such as saving-water, energy and labor saving and so on, also it can eliminate adverse effects caused by human factor in the process of irrigation, improve the accuracy of operation, it is beneficial to managing scientific the irrigation process and extending the advanced technologies. The exploration and * **

Foundation project：national science and technology support project(2007BAD88B01). Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 682–690, 2011. © IFIP International Federation for Information Processing 2011

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application of irrigation decision support system and water-saving irrigation professor system not only can provide scientific decision foundation and decision scheme for scientific management and optimal allocation of water resource, improve water utilization rate, dull the supply and demand contradiction of water resource, lighten the peasants’ work intensity[4], but also promote the development of agricultural information and science technology industry, it is very important for the realization of water-saving irrigation automation. Agricultural intelligent irrigation management system is accurate irrigation technology, which synthesizes the science and technology achievement of modern agriculture irrigation, establishes the water utilization management measures of agriculture irrigation, and realizes the irrigation regulate operation as the center. The system integrates GSM ( Global System for Mobile Communication) network wireless communication technology, sensor technology, computer technology, and agriculture irrigation automatic control technology, carries on calculation, analysis and decision-making based on collected information such as soil moisture, crop drought, and meteorology factors and so on, makes out irrigation forecast and decision, determines the time and amount of water irrigation, uses the decision results to automatically control and monitor the irrigation equipments simply and rapidly, carries out timely and suitable dynamic management of intelligent automatic irrigation for the field-crops, sets up the precision amount control field intelligent irrigation management system with remote monitoring, wireless transmission, rapid diagnosis, intelligent decision, and precision control functions, realizes modernization and automation of the irrigation water management means.

2 The Basic Structure and Characteristic of the System Field intelligent irrigation management system is divided into central monitor layer and substation monitor layer according to control grade. Central monitor layer timely monitors the field situation of each area, directly sends out instructions to control executive element work by controller, plays a general monitor role, the system is managed by the central monitor layer. Substation monitor layer is placed at the controlled field, each substation has independent control system, responsible for executing the commands of central monitor layer and users, and the control of the equipments and the management of information of the station. Central monitor layer and substation monitor layer are composed of acquisition subsystem, intelligent decision subsystem, and automatic control subsystem. The system collects the information by monitoring acquisition subsystem, the information through GSM network transfers timely or real-time to the monitor central host according to users’ requirements. The monitor central host connects with the computer, which makes scientific irrigation task list by intelligent decision system, the control center host gives out irrigation commands through GSM network, automatic control subsystem automatically performs the irrigation work according to the instruction of decision support system.

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Fig. 1. The basic structure of field intelligent irrigation management system

2.1 Acquisition Subsystem 2.1.1 The Basic Function of the System Acquisition subsystem collects the basic information of the field by all kinds of equipments, the basic information such as soil water, atmospheric temperature, atmospheric humidity, the flow of water meter in flooding and so on. Acquisition system has the GSM network as the basic support for data transmission, has the Short Message Service short message as communication carrier[5][6][7], realizes the remote wireless bidirectional data communication from one point to multi-point. Data acquisition and transmission are the basic link of the whole system decision and control, the data acquisition and transmission, which has a lot of characteristics such as the low operation cost due to low on short messages cost, the stable reliability, the higher rate of data transmission, low error code rate and so on, perform timely and real-time data acquisition, stresses the timeliness and dynamic of data monitor, provides accurate and timely information guarantee for realizing water monitor and decision of large-area field. 2.1.2 Working Principle of the System According to the requirement of area characteristic and acquisition factors, by the operation on the interface between the man and the computer, user transfers the instruction to the host in the acquisition monitor center to acquire indexes through COM port, the host of acquisition monitor center uses the GSM wireless communication equipments, sends the setting command to the terminal of data acquisition in the form of short message to go on initialization. According to the requirement of received acquisition instruction, acquisition monitor substation collects the information from the soil water sensor, temperature sensor, humidity sensor and flow sensor in the field. Monitor substation based on solar energy battery can realize timely and real-time reception and transmission of information collected, which is highly effective. Monitor substation sends the information collected to the host of acquisition monitor center in the form of short message, users can master best

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Fig. 2. Work principle flowchart of data acquisition system

and comprehensive data information of field water moisture and meteorological factors by means of the fixity of timing acquisition and the random of real-time acquisition, then stores the data to database. The work principle is shown as in Fig.2 . 2.2 Intelligent Decision Subsystem 2.2.1 Basic Function of System Intelligent decision subsystem is a total integration system, which uses database, people and computer interaction to go on multi-mode organic combination, it assists decision-maker to realize scientific decision[8]. Intelligent decision system with the meteorological data and water moisture data as the basis, using the water balance model, combines with the theoretical basis and characteristic of crop water requirement in the system knowledge base, soil properties and irrigation methods, makes reasoning computation according to some rules, makes out water saving irrigation decision for the crop planted in one area or one field, there are some irrigation reference factors, that is, making sure the crop need to irrigate or not, and when it needs to irrigate, comprehensive soil water content can reach the lower limit index suitable for crop growing or not, weather forecast and the growing stage of the crop is in the stage suitable for regulated deficit irrigation or not. It takes protective irrigation alarming mode as soil moisture content diagnose system, timely makes out accurate irrigation decision, sends out concrete operation instruction to control subsystem through the interface of interaction between man and computer. 2.2.2 Work Principle of the System Field monitor substation sends acquisition information collected by soil water sensor, temperature sensor, humidity sensor and flow sensor through GSM network to the host of monitor center, decision system takes database to receive collected information as the basis, has mode base as the support of system calculation and statistics, has the method base as the theoretical guidance of mode base calculation and statistics, has the knowledge base as the support for system theory and experience operation, makes calculation, statistics, analysis of the information, then makes out irrigation decision, makes effective and water-saving irrigation plans, realizes scientific guidance to area irrigation. The work principle is shown as in Fig.3.

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Fig. 3. Work principle flowchart of intelligent decision subsystem

2.3 Automatic Control Subsystem 2.3.1 Basic Function of the System Field intelligent irrigation automatic control subsystem realizes scale and automation of production, raises labor productivity[9]. Control subsystem is the remote automatic irrigation monitor system of GSM wireless communication, it transmits irrigation decision scheme instruction of intelligent decision subsystem, via the host of system control to control substations by GSM network. Control substations guide the irrigation controller to open or close according to the received irrigation decision instruction and transmit irrigation water parameters collected to computer via the host of control centre, in order to achieve the aim of wireless automatic irrigation. 2.3.2 Work Principle of the System Field monitor substation sends information collected by soil water sensor, temperature sensor, humidity sensor and flow sensor by GSM network to the host of monitor center. According to mode base, method base, knowledge base and database, decision system makes out irrigation decision. Control subsystem sends irrigation decisions via GSM network to field control substation by the host of control center. Control station realizes precision irrigation control according to single control electromagnetic valve’s open or close for irrigation decision task. The work principle is shown as in Fig.4.

Fig. 4. Work principle flowchart of automatic control subsystem

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3 Database Design Database is a data storage space for storing irrigation management decision and model operation, an application software integral with the unified management of data which is massive, complex in structure, abiding and sharing as goal, meanwhile it is independent, comprehensive, universal, sharing and easy in maintenance[4][10]. The level of database structure design directly effected both the efficiency of the system and the effects of implementation[11]. Database design includes basic database, real-time database and spatial database design, it is mainly composed of graphic library, all kinds of acquisition information and attribute information of all kinds of ground objects [12][13]. In the field intelligent irrigation management system, Database Management System is used mainly to manage and maintain the data in database, makes the information storage and reading systematic, standardized and automatic, including browsing, query, renewal, adding, backup and recovery of data and so on. Database of system used the efficient relational database to combine with Windows NT/2000 and windows 9x,the characteristics of operating system were adequately used. Combinning with GSM wireless communication technology, the management system of farmland intelligent irrigation was developed by the test function and the graphic user interface of Java virtual platform, which was based on virtual instrument technology. Established database includes six functions as follows: (1) Meteorology database. It can store, query and modify the basic information of meteorology factors, such as accumulated temperature, rainfall, atmospheric temperature and relative humidity etc. (2) Crop information database. It can store, query and modify the basic information of crop growth and development, including crop growth stage, days of growth stage, the depth of effective water absorption layer of root in each growth stage, suitable water upper and lower limits in each growth stage etc. (3) Soil information database. It can store the basic soil water dynamic information of management regions, including field capacity and bulk density (The type of soil is divided into black soil, chernozem, sandy loam soil, meadow soil and saline soil and so on ), and can query and modify the basic dynamic information of soil water. (4) Irrigation regional database. It can store, query and modify the basic information of irrigation regions, including the crop, soil, equipment, picture and characters etc in the regions. (5) Irrigation elements database. It can store, query and modify the basic information of the ways and quota of irrigation. The ways of irrigation include sprinkler irrigation, drip irrigation and furrow irrigation. The quota of irrigation includes the quota of different accumulated temperature zones, different crops and different growth stages. (6) Irrigation alarming database. It includes irrigation time, irrigation quantity and alarm limit of soil water etc.

4 Application and Analysis of the System The management system of farmland intelligent irrigation was tested and applied between the year 2009 and the year 2010 in some areas of Heilongjiang Province,

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such as Qinggang, Dumeng, Zhaodong, meanwhile, was improved and upgrade. The system used field sensors to achieve the collecting work, the measurement accuracy of soil water sensor reached ±2% (m3/m3) in 0-50% (m3/m3 reached stable after electrifying. The solar cell of power supply system had some advantages to avoid hindering collection of information with insufficient electricity under rainy weather condition, the system could run normally, such as non equalization charging, small self-discharge, without the leakage of liquid, without overflow of acid gas. By processing the information, acquisition monitor substation timely or real-time transmitted digital signal to the host of acquisition monitor center according to users’ requirements. Acquisition monitor substation contained acquisition circuit, gate circuit, ATM processor, cpu acquired soil water by sensor, transfered the analog into digital information, the remote transmission of signal to the host of acquisition monitor center could be achieved by GSM wireless module. The host of acquisition monitor center was composed of programmable array multiplexer, constant current source, isolation transmit amplifier, 12 bit A/D converter, using 32 bitARM processor, operating system was MC/OS-II, storage capacity was 2 M byte. The host of acquisition monitor center received the GSM information from acquisition monitor substation and stored monitoring data for 9 months. The host of acquisition monitor center connected with computer, the computer intelligent irrigation management software calibrated the information of database with the support of mode base, methods base, knowledge base, the processing physical quantities were displayed software interface, processing results were stored the relative database, according to the soil moisture and growth condition of plant , decision system gave out the irrigation time, irrigation water, and other relevant information after analyzing and calculating, then, sent irrigation command to the host of control center. The host of control center was composed of programmable array multiplexer, constant current source, isolation transmit amplifier, using 32 bit ARM processor, operating system was MC/OS-II. The control substation not only were acquisition terminal but also had irrigation control performance, it was composed of GSM communication module, data acquisition module, control module, power module. The control substation received the irrigation command from the host of control center, achieved irrigation tasks with irrigation controller, then, sent executed information of irrigation tasks to the host of control center. Field intelligent irrigation management system sets up design features with area characteristics, besides some functions such as irrigation decision support system, water-saving irrigation expert system and artificial intelligence irrigation management system. (1) The system has different accumulated temperature zones, different crops and different soil types in Heilongjiang province as the basic indexes, establishes the comprehensive database combined with crop growth information and soil information essential for field irrigation, satisfies actual demand for irrigation decision and guidance under the condition of different planting structures and different characteristic regions, and also can modify, newly add parameters to the database and has better expansibility. (2) The system has the GSM network and solar energy component power supply system as support, realizes timing or real-time reception and remote wireless

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transmission of various acquisition information in field. Real-time monitor of data information provides powerful guarantee for irrigation decision. (3) The system establishes water diagnosing index system which is based on field moisture content of crop physiological characteristics and soil physical characteristics, monitored by sensor, using man and computer interaction system design, The system performs the alarming function to soil water deficiency or sufficiency, implements scientific irrigation management and system monitor maintenance. (4) The system regional management design in different layers overcomes inhomogeneity in spatial distribution on small scale, implements dynamic monitor of point to point moisture content in field divisions, satisfies the hierarchy of time distribution on large scale, realizes concentration and integration of automatic irrigation in management regions, sets up precision and effective monitor and irrigation management technique modes which satisfy regional characteristics.

5 Conclusions Field intelligent irrigation system is prepared mainly on the basis of aggregating large amount of knowledge information, in connection with characteristics of the soil properties of main crops, the law of crop water requirements, and crop evapotranspiration etc a series of data indexes of soybean, sunflower and sugarbeet, etc, planted in different accumulated temperature zones in Heilongjiang province. With the support of computer, the system uses remote monitoring, realizes the monitor of soil moisture and timely monitors the amount of water consumption and requirement in field crop growth period, gives accurately values of crop water requirement, makes out irrigation decisions, and the irrigation control system carries out automatic irrigation according to decision content, which makes dispersed agriculture facilities and management areas a whole body, improves the operation efficiency of agriculture, reduces the cost of production, provides proper moisture growth environment for crops, realizes the objects of efficiency, water saving and yield increase.

References 1. Ma, J.: The Study of Accurate Irrigation and Fertilization Automatic Control System. Shanxi Agricultural University. Master Degree Dissertation, p. 5 (2005) 2. Yang, J.: The Application of Software Engineering in the Development of Agricultural Expert System. Journal of Agricultural Mechanization Research 3, 224 (2005) 3. Deng, J.: The Design and Implementation of Intelligent Decision System for Apply Fertilizer and Irrigation. Central China Normal University. Doctorate Dissertation, p. 2 (2006) 4. Cui, J., Ma, F., Zheng, Z., et al.: Study on Field Water Irrigation Management and Automatic Control System Based on GSM. Water-saving Irrigation 5, 30–31 (2005) 5. Zhang, Z., Zhao, P., Yang, Z., et al.: System of the Hydrology Information Remote Collection Based on GIS and GSM. Journal of Anhui Agricultural Science 36, 6585–6586 (2008)

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6. Shu, Y.: Real Time Collection System of Soil Moisture Based on Short Message. Guizhou University. Master Degree Dissertation, pp. 6–7 (2006) 7. Li, N., Liu, C., Li, Y., et al.: Development of remote monitoring system for soil moisture based on 3S technology alliance. Transactions of the Chinese Society of Agricultural Engineering 26, 169–172 (2010) 8. Zhang, W., Liu, X., Gu, H.: Development of Real-time Irrigation Decision Support System Software of Irrigation Regional. Yellow River 29, 54 (2007) 9. Hao, W., Peng, X., Geng, Q., et al.: Intelligent Irrigation Control System Based on ARM. China Rural Water and Hydropower 5, 24 (2006) 10. Shang, H., Wang, Z., Chai, P.: Research and Development of Water-saving Irrigation Database and Its Management System. Research of Soil and Water Conservation 9, 97–98 (2002) 11. Yang, J.: Research on Decision Making Support System for Precise Irrigation of Wheat in the Hexi Oasis Irrigation Areas. Gansu Agricultural University. Master Degree Dissertation, pp. 42–43 (2007) 12. Sun, M., Cai, D.: Research on Irrigation Areas Water Resource Management Based on Ground Information System. Water Saving Irrigation 1, 43 (2007) 13. Chen, L., Huang, J.: Integration of Irrigation Management Model and GIS and Its Application. Journal of Irrigation and Drainage 3, 29–30 (2003)

Extracting Winter Wheat Planting Area Based on Cropping System with Remote Sensing* Xueyan Sui1, Xiaodong Zhang1, Shaokun Li2,3,**, Zhenlin Zhu1, Bo Ming2, and Xiaoqing Sun1 1

Institute of Agriculture Sustainable Development/Shandong Academy of Agriculture Sciences, Jinan 250100, China Tel.: 0531-83179362 [email protected] 2 Institute of Crop Sciences, Chinese Academy of Agriculture Sciences/ Key Laboratory of Crop Physiology and Production Ministry of Agriculture, China, Beijing 100081, China Tel.: 010-82108891 [email protected] 3 Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crops/ The Center of Crop High-yield Research, Shihezi 832003, China

Abstract. Winter wheat is one kind of important crop in China. It’s planting area is one key element to explain yield change. To obtain winter wheat planting area as soon as possible can provide scientific reference for our country’s making related policy. Basing on the cropping system in Shandong province, winter wheat is divided into two kinds “winter wheat sowed by machine-maize” and “people broadcast winter wheat-rice”. Using MODIS data, NDVI characters of winter wheat, garlic, greenhouse vegetable, from sowing till overwintering stage were analyzed. Together with NDVI characters of former stubble crops in middle September, extracting requirements were set up for winter wheat planting area which was sowed by machine this year. In view of the spectrum similarity between rice wheat and greenhouse vegetable from sowing stage till overwintering stage, rice wheat planting area of former year was extracted relying on the character of biomass rapid growth at jointing stage. Because of the “people broadcast winter wheat-rice” cropping system is very fixed in Shandong province, then the rice wheat planting area of former year can take the place of the rice wheat planting area this year. Two kinds of winter wheat area were merged, and tested by 284 groups of located spots data, the accuracy reached 94.01%. The result showed that it is feasible to extract winter wheat area before overwintering stage, and the time is 4 months earlier than using jointing stage NDVI. Keywords: Shandong province, winter wheat, rice wheat, area, remote sensing.

*

Fund projects: National Science & Technology Pillar Program (2007BAH12B02); Shandong Academy of Agricultural Sciences innovation program (2007YCX026); Shandong Province Science & Technology development projects (2009GG10009007). ** Corresponding author. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 691–699, 2011. © IFIP International Federation for Information Processing 2011

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1 Introduction Shandong Province located in the Huang-Huai Plain is the major winter wheat producing area in China. It is important to grasp winter wheat area timely and accurately for the country's food security. In the past, winter wheat area mainly relied on primary agricultural production sectors’ artificial survey and then statistics level by level. Limited resources and subjectivity led to poor data, which brought some impact to government’s decision on agriculture production. With the development of science and technology, In 1980s, remote sensing had become one tool of agriculture resource investigating and crop growth monitoring with characters of obtaining data objectively, accurately and timely [1].The key to extract winter wheat area is the data resource and the choice of phase. In 1990s, using NOAA satellite data, Maoxin Wang set up regression equation of winter wheat area and the pixel number whose NDVI (Normal Differential Vegetation Index) difference between November and October was greater than zero [2]. A large population but less land area, complex cropping system and NOAA satellite data’s lower spatial resolution resulted that the extracting accuracy of winter wheat was not high. In the 21st century, with the food strategy advancement, some regions extracted winter wheat area with TM and spot data [3, 4] which holds higher spatial resolution than NOAA data. Extracting methods included visual interpretation [5], supervised classification [6], unsupervised classification [7, 8] and pure pixel identification based on spectral library [9]. Although resolution being enhanced and data processing technology being improved have made the accuracy reach over 90%, While TM data has lower time resolution and easily influenced by weather, what’s more the cost is high, then TM data can only be used to invest winter wheat areas of small regions but not large areas. Compared with NOAA data and TM data, MODIS data has higher time and middle spatial resolution. The data sharing service has made MODIS data be used to remote sensing monitoring winter wheat more and more [10-13]. Yigang Jing set models and extracted winter wheat area with accuracy of over 91% using the NDVI changes of March, May and June [14]. Jinqiu Zou extracted area using the EVI (Enhanced Vegetation Index) difference between May and October, and the error rate was -0.04% [15]. Wenpeng Lin used NIR, RED, BLUE and ESWIR 4 bands MODIS data of October and December to class 6 kinds of surface features with the method of fuzzy ARTMAP, and the accuracy reached 80.3% [16]. The main method of comparing spectral changes in the key growth periods after sowing for extracting winter wheat area was used frequently using MODIS data [17]. Over time, the amount of information increased, and the accuracy enhanced [18-20]. The paper studied one method with high accuracy to extract winter wheat area at early periods, combining with the former stubble crops based on cropping system in Shandong province.

2 Data Acquisition Winter wheat is sowed in middle to late October in Shandong province, and tillers in early to middle December. In middle to late October, most vegetables in greenhouse are transplanted, and grow fast after rejuvenation in December, and the change of NDVI is similar with that of winter wheat, which influences the extracting accuracy of winter

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wheat area [16]. Shandong province is also the major producing area of garlic. It is sowed in late September to early October, the growing condition is very similar to winter wheat in winter, and thus it is another important obstacle. In order to exclude the interferences of greenhouse and garlic, discussion group located 284 spots of six kinds of surface features which included winter wheat, greenhouse vegetable, garlic, village, uncovered cotton filed, tree, in Shandong province, in late October, 2008. Downloaded MOD09Q1 data of the 284 spots of middle January, middle April, middle September, middle October and middle December, form MODIS data sharing platform-ftp://e4ftl01u.ecs.nasa.gov/.

3 Data Analysis 3.1 This Part Analyzed Surface Features’ NDVI Sequence, and Set Up Identification Conditions to Extract Winter Wheat Area Preliminarily We calculated the located spots’ NDVI and the average of middle September, middle October and middle December for six kinds of surface factures separately, and then drew line chart (fig. 1). The NDVI of cotton filed dropped as the season went on, and it was similar to village and tree. Among the three periods, the NDVI in September was the highest for winter wheat, greenhouse vegetable and garlic, at that time, winter wheat filed and garlic filed are all planted maize, and greenhouse is still planted vegetable. The gain period of garlic is in late May, which is earlier than winter wheat. After garlic the filed is planted with early mature maize, and the maize is gained in middle to late September. While winter wheat is gained in middle June, then the filed is planted with maize. Winter wheat field’s maize is at filling period, so the NDVI average 0.71 in middle September is higher than that of garlic filed. Till middle October, maize is gained, and winter wheat is sowed. Garlic is sowed in late September to early October, and the time of emergence is earlier than winter wheat. Greenhouse

0.8

winter wheat

0.7 greenhouse vegetable

0.6 NDVI

0.5

garlic

0.4 tree

0.3 0.2

cotton

0.1 village

0 September

October

December

month

Fig. 1. NDVI line chart of 6 kinds of surface features in the three periods

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vegetable is transplanted, and grows faster than winter wheat and garlic with good conditions. Thus greenhouse vegetable’s NDVI is higher than garlic field’s, and garlic field’s NDVI is higher than winter wheat field’s. In middle December, winter wheat steps into tillering stage, and reaches growing peak before winter. Garlic’s biomass and greenhouse vegetable’s biomass all continues to increase. So in middle December, the three kinds surface features’ NDVI are all higher than that in middle October, but lower than in middle September. According to the analysis above all, preliminary identification conditions were set up to extract winter wheat area: NDVI middle September > NDVI middle December > NDVI middle October and NDVI middle September >0.5

，

。

3.2 Test on Identification Result Used the identification conditions to extract winter wheat area, and tested the result with 284 groups of located spots’ data (table 1). Table 1. The test results checklist The number of

The number of spots

The number of spots

spots to be tested

tested to be Winter Wheat

tested to be others

winter wheat

161

138

23

cotton

24

0

24

——

31

3

28

mixed pixels mixed pixels

Surface features

greenhouse vegetable

Wrong test reasons 7 mixed pixels 16 rice wheat

garlic

11

3

8

tree

26

1

25

mixed pixel

village

31

3

28

mixed pixels

Winter wheat area extracted Located spots Ji Ning

Fig. 2. Spots of rice wheat tested to be others

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Winter wheat area extracted Located spots

He Ze

Fig. 3. Mixed pixel of winter wheat tested to be others

The result was listed in table 1. Among 284 located spots, 23 winter wheat spots were identified to be other surface features. 10 other surface features spots were identified to be winter wheat, which included 3 greenhouse vegetable spots, 3 garlic spots, 1 tree spot and 3 village spots. Imported the 284 groups of located spots into ENVI, and inspected one by one, then discovered that the wrong identifications can be divided into two kinds of situations: one was located spots were mixed pixels of winter wheat with greenhouse vegetable, garlic, tree or village (fig. 2); the other was rice wheat spots were identified to be other surface features (fig. 3). A large population but less land area and complex cropping system in Shandong province make the phenomenon of mixed pixels inevitable. Rice wheat was not identified, showed the identification conditions didn’t contain rice wheat information. Only extracted “winter wheat sowed by machine-maize” area, so the main task remained was to extract “people broadcast winter wheat-rice” area. 3.3 The Extraction of Rice Wheat Area Calculated 16 rice wheat spots’ average NDVI of the 3 periods, compared with common winter wheat. As fig.4, fig5 showed, in September, rice is growing in rice wheat field, and maize is growing in winter wheat field, and the NDVI of rice is lower than that of maize. In middle October rice is gained, and rice wheat is at trefoil stage which was broadcasted not long ago, whose NDVI is higher than normal wheat. What’s more rice wheat field has adequate water supply, so rice wheat growing condition is better than normal winter wheat till middle December. During the analyzing progress, narrowing NDVI threshold of the 3 periods was tried to extract rice wheat area, but failed for the NDVI similarity between rice wheat and greenhouse vegetable. Winter wheat goes into overwintering stage In January, and jointing stage in April, growing condition is influenced by air temperature; rice wheat and normal winter wheat are similar to each other. So it is possible to extract all winter wheat area depending on NDVI fast increasing over the period from middle January to middle April, that is to say, from overwintering stage to jointing stag. This method is popular now.

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0.8 0.7 0.6

rice wheat

NDVI

0.5 normal winter wheat

0.4 0.3

greenhouse vegetable

0.2 0.1 0 September

October

December

month Fig. 4. NDVI line chart of rice wheat, normal winter wheat, greenhouse vegetable in the three periods

Fig. 5. Winter wheat sowed by machine and people broadcast winter rice wheat in middle to late October

Affected by geographical environment and climate, “people broadcast winter wheat-rice” is one common cropping system in Yellow River Basin and the Southern Four Lakes rim, so the rice wheat field is relatively fixed. According to Statistical Yearbook, vector graph of rice planting region was set up in Shandong province with the help of ARCVIEW. The normal winter wheat area which has been extracted with data of middle September, middle October, middle December was named S1. The rice wheat area extracting conditions were set up, (NDVI middle April - NDVI middle January) > 0.19, NDVI middle January > 0.3, and NDVI middle September >0.5, using the data of 2008. In ENVI, S1 was masked, and among rice wheat field vector region, rice wheat area was extracted of 2007-2008, which were named S2 (fig. 6). The 16 rice wheat located spots were all extracted successfully.

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Fig. 6. The extracted area of rice wheat of 2007-2008

3.4 The Extraction of all Winter Wheat The two parts S1 and S2 of winter wheat were merged, which contained the normal winter wheat area extracted with the data of September, October, and December, 2008,

Fig. 7. The extracted area of winter wheat of Shandong province of 2008-2009

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and the rice wheat area extracted with the data of January, April, and September, 2008. The total winter wheat area of 2008-2009 was 4480666.67 hm2, tested by the located spots’ data the accuracy was 94.01%(fig. 7).

4 Result and Discussion Winter wheat in Shandong province can be divided into two kinds from a cropping system point of view, “mechanical sowing winter wheat-maize” and “people broadcast winter wheat-rice”. The paper studied NDVI characters of winter wheat, garlic and greenhouse vegetable from seeding time October to overwintering stage December. The study showed that 3 kinds of surface features’ NDVI changing tendencies are similar to each other, and it is difficult to extract winter wheat area. Together with the difference of the previous crop’s NDVI, the normal winter wheat of “mechanical sowing winter wheat-maize” was extracted. But the rice wheat NDVI of “people broadcast winter wheat-rice” is similar to greenhouse vegetable NDVI, so it is impossible to extract rice wheat area just depending on the change of NDVI. Based on the special growing environment of rice wheat, rice plant region vector in Shandong province was set up. Among this region, rice wheat area of the previous year was extracted using the fast NDVI increase at jointing stage than overwintering stage. The two parts winter wheat area were added up, and got the total area in late December. This study used middle spatial resolution and high time resolution MODIS data to settle the interference of garlic and greenhouse vegetable to winter wheat area extracting in Shandong province. The extracting time was 4 months in advance than using jointing stage NDVI character[21,22], and the accuracy reached 94.01%, so the method can meet the demand of large area. Natural environment decides cropping system, while the improvement of agricultural production technology, and many other economical factors’ all influence the reform of cropping system. So using the previous year rice wheat area to take the place of the current year will affect the accuracy of total winter wheat area. In future research, rice field’s spectrum should be studied, and assisted with NDVI change character of rice wheat from seeding to overwintering stage to extract rice wheat area, in order to enhance the total winter wheat area extraction accuracy.

References 1. Mei, A., Peng, W., Qin, Q., et al.: Introduction to Remote Sensing. Higher Education Press, Beijing (2001) 2. Wang, M., Pei, Z., Wu, Q., et al.: Winter wheat sown area estimation using NOAA AVHRR data. Transactions of the CSAE 14, 84–88 (1998) 3. Murakami, T., Ogawa, S., Ishitsuka, M., et al.: Crop discrim-ination with multitemporal SPOT/HRV data in the Saga Plains, Japan. Int J. Remote Sens. 22, 1335–1348 (2001) 4. Qi, L., Liu, L., Zhao, C., et al.: Selection of optimum periods for extracting winter wheat based on multi-temporal remote sensing images. Remote Sensing Technology and Application 23, 154–160 (2008)

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5. Gu, X., Pan, Y., Zhu, X., et al.: Consistency study between MODIS and TM on winter wheat plant area monitoring——a Case in small area. Journal of Remote Sensing 11, 350–357 (2007) 6. Feng, M., Yang, W., Zhang, D., et al.: Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data. Transactions of the Chinese Society of Agricultural Engineering 25, 103–109 (2009) 7. Huang, X., He, W., Zhang, Y., et al.: Monitoring on wheat area using TM in some areas of Jiangsu province. Jiangsu Agricultural Sciences, 85–87 (2003) 8. Li, W., Li, H., Wang, J., et al.: A study on classification and monitoring of winter wheat growth status by Landsat/TM image. Journal of Triticeae Crops 30, 92–95 (2010) 9. Chen, S.: Remote sensing method of pure crop pixel identification and planting area estimation based on spectral library. Chinese Academy of Science, Beijing (2005) 10. Yang, X., Zhang, X., Jiang, D.: Extraction of multi-crop planting areas from MODIS data. Resources Science 26, 17–22 (2004) 11. Wu, Y., Wang, Y., Zhang, J., et al.: Linear mixture modeling applied to remote sensing monitoring of winter wheat areas. Transactions of the Chinese Society of Agricultural Engineering 24, 136–140 (2009) 12. Yan, F., Wang, Y., Wu, J., et al.: Extracting winter wheat area using temporal sequence of Ts-EVI. Transactions of the CSAE 25, 135–140 (2009) 13. Qiao, H., Zhang, H., Cheng, D.: Application of EOS/MODIS-NDVI at different time sequences on monitoring winter wheat acreage in Henan Province. Journal of Anhui Agricultural Sciences 36, 11940–11941 (2008) 14. Jing, Y.: Study on extracting winter wheat area using EOS/MODIS data. Shanxi Journal of Agricultural Sciences (2), 95–98 (2008) 15. Zou, J., Chen, Y., Uchida, S., et al.: Method for extracting winter wheat area using Terra/MODIS data and its accuracy analysis. Transactions of the Chinese Society of Agricultural Engineering 23(11), 195–200 (2007) 16. Lin, W.: Study on crop information extraction based on MODIS spectral analysis. Chinese Academy of Science, Beijing (2006) 17. Zhang, M., Zhou, Q., Chen, Z., et al.: Crop acreage change detection based on phenology model. Transactions of the CSAE 22, 139–144 (2006) 18. Wang, Y., Shen, R., Tian, G.: Study on Extracting Winter Wheat Planting Area Based on MODIS Data by Spectral Mutation. Nei Menggu Weather, 18–21 (2009) 19. Wang, Y., Shen, R.: Study on the Planting Area Extraction of Winter Wheat Based on MODIS Data. Journal of Anhui Agricultural Sciences 37, 16694–16696 (2009) 20. Xu, W., Zhang, G., Fan, J., et al.: Remote sensing monitoring of winter wheat areas using MODIS data. Transactions of the CSAE 23, 144–148, 196 (2007) 21. Qin, Y., Zhao, G., Jiang, S., et al.: Winter wheat yield estimation based on high and moderate resolution remote sensing data at county level. Transactions of the CSAE 25, 118–123 (2009) 22. Chen, J., Liu, H., Huang, Y., et al.: Analysis on the Multi-temporal Features of MODIS/NDVI in the Growing Period of Winter Wheat and Its Application in Ground-object Identification. Journal of Anhui Agricultural Sciences 38, 3641–3643, 3667 (2010)

Study on the Rainfall Interpolation Algorithm of Distributed Hydrological Model Based on RS Xiaoxia Yang, Yong Liang, and Song Jia School of Information Science and Engineering, Shandong Agricultural University, Taian, Shandong Province, P. R. China, 271018 [email protected]

Abstract. Distributed hydrological model can be divided into two parts, as runoff and the convergence. Runoff calculation is the basis of distributed hydrological model, its results will determine the accuracy of the simulation results directly. Rainfall is an important input of runoff calculation, thus its accuracy has special significance to runoff calculation. For most small watershed, now mainly rely on rainfall stations to obtain rainfall information. In this case, the most effective approach is base on space correlativity principle, use the interpolation algorithm to obtain distributed rainfall data. Take XueYe reservoir 2000-2008 year of reality measures data as an example, we compared the apply condition of several kinds algorithm such as co-kriging interpolation, kriging interpolation, reverse-weighted interpolation, and so on, prove that co-kriging interpolation is most fit XueYe reservoir . Keywords: Distributed hydrological model, Association Kriging interpolation, Kriging interpolation, Reverse-weighted interpolation.

1 Introduction Distributed hydrological model has been a hot research field for nearly 20 years [1], what can be divided into two parts, as runoff and the convergence. Runoff calculation is the basis of distributed hydrological model, its results will determine the accuracy of the simulation results directly. Rainfall is an important input of runoff calculation, its spatial distribution characteristics is the main control factors of runoff and a series of other hydrological problems. The effective method to get accurate rainfall distribution characteristics is to set density rainfall station observation network [2],but now most small watershed has limited quantity of rainfall station and the distribution of rainfall stations are always not reasonable, so the data from these stations often cannot meet the requirements. Therefore, the spatial interpolation method according to the acquired data become a research hotspot. There are several interpolation methods [3] [4] [5] [6] [7], different method has different results, and no one is common interpolation method for optimum interpolation method [8] [9].Taking XueYe reservoir 2000-2008 year of reality measured data as an example the paper contrast cokriging interpolation, kriging interpolation, reverse-weighted interpolation and thiessen polygon interpolation, then analyses the interpolation method fit XueYe reservoir. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 700–705, 2011. © IFIP International Federation for Information Processing 2011

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Fig. 1. 11 rainfall stations’s scattergram of XueYe reservoir

XueYe reservoir is one of the large reservoir in ShanDong province, founded in 1959.XueYe reservoir has a total capacity of 2.21 billion cubic meters, water area of 1.8 million acres. It located in 117.54 ~ 117.64 degrees east longitude and latitude between 36.39~36.46 degrees, belong to temperate continental monsoon climate, the four seasons, the annual average temperature is between 11.0- 13.0 degrees Celsius, precipitation 760.9 mm. Figure 1 is about 11 rainfall stations’s scattergram of XueYe reservoir.

2 Rainfall Space Interpolation Method First, compare the relationship between the XueYe 2000-2008 reality measures rainfall data and longitude, latitude, elevation, found that rainfall data with latitude, longitude linear correlation is not obvious, the correlation coefficient R2 are -0.52 and -0.47.But the rainfall data with elevation is obvious, R2=0.6021. 2.1 Thiessen Polygon Interpolation Thiessen polygon interpolation is one of the most simple partial interpolation algorithm, put forward by the Dutch climatologists A.H. Thiessen. Its main idea is adjoin all rainfall stations by triangle, for each side of the triangle do vertical bisectrix, each station around several vertical bisectrix form a polygon, and the polygon contain only one station, the polygon called Thiessen polygon. Thus we can express the data of all the points in the polygon by the station’s reality measures data. Thiessen polygon interpolation is simple. In the case that there are enough rainfall stations, Thiessen polygon interpolation can be good at approximating the actual value. It’s shortcoming is the mutation on the boundary. It can’t fit the space characteristics that rainfall is gradually changing. Meanwhile Thiessen polygon interpolation neglect the influence of elevation, not suitable for data interpolation of XueYe reservoir.

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2.2 Reverse-Weighted Interpolation Reverse-weighted interpolation is one of the space geometric interpolation method. When we use the sampling points estimate the estimation point, often farther to the estimate point smaller the influence is, in other words farther sampling point has smaller weight. Thus, the value of eatimate point Z(x0) can be fitting by all the surrounding point’s linear weighted: n

1 Z ( xi ) P i =1 ( Di )

Z ( x0 ) = ∑

n

1

∑ ( Di) i =1

(1)

P .

Di is the distance from the i sampling point to eatimate point, Z(xi) is the value of i sampling point, index p is the power of distance, used to control the weight’s change speed with distance. Different value of p will influence the result of interpolation, the bigger the value of p the smaller weight of the far distance point. p can values 1,2,3.When p=2 is called the inverse distance square interpolation method. In this paper the value of p is 2.The strong point of reverse-weighted interpolation is it can adjust the result of spatial interpolation by weight, but the shortcoming is it also neglect the influence of elevation. 2.3 Kriging Interpolation Kriging interpolation is regarded as one of the main method of geological statistics, is raised by South Africa scientist D.G. Krige in 1951 and named by his name. Kriging interpolation fully absorb the idea of space statistics, think any space of continuous change attributes is very irregular, can’t imitation by simple smooth mathematical functions, but can described by random surface function. From the angle of interpolation Kriging interpolation is one of the method that is linear interpolation optimal, unbiased estimation for directional distribution data. Kriging interpolation is defined as: n

Z( x 0 ) = ∑ λi Z ( xi )

(2)

.

i =1

Z(xi) is the observed value of i point, Z(x0) is the value of estimation point,

λi

is the

power ,and we have n

∑λ i =1

i

=1

.

(3)

The linchpin of kriging interpolation is to fix the value of power λi ,it should make the value of Z(x0) unbiased estimation, that is less than any variance of linear combination observations of the variance. In the ordinary use half variance as a basis to fix the value of the power. So at the first we should fix the model of half variance, the common model of half variance are nugget, spherical surface, index, Gauss, power and linear model. In the paper we adopt spherical model.

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2.4 Co-kriging Interpolation The theory of co-kriging interpolation is similar with kriging interpolation. Co-kriging interpolation optimal the estimation through the way that consider moer than one variables and the relation between the variables. Use co-kriging interpolation,we can use the correlation between the variables, estimate the value of one or more variables, improve the accuracy and rationality of estimation. Co-kriging interpolation introduce the crossover-mutation function, that is the function that the correlation of two different variables change with the distance. The crossover-mutation function is defined as : rij(h)=1/2*E{[Zi(x+h)- Zi(x)][ Zji(x+h)- Zj(x)]}

(4)

Within XueYe reservoir the elevation is knowed ererywhere and stable,we introduce the elevation as a factor into co-kriging interpolation.

3 Analysis the Result of Rainfall Interpolation 3.1 Method of Calibration Adopt cross validation methods to analysis the result of interpolation: randomly extract 2 rainfall stations as docimastic station among all the 11 rainfall stations of XueYe reservoir, remove the data of the docimastic station, use data of the rest 9 rainfall stations to estimate the value of docimastic staion, and compute the error. Use mean absolute error(MAE) and root mean squared interpolation error(RMSIE) as the evaluation criterion to evaluate the result of interpolation. MAE is used to evaluate the error range, RMSIE can reflect valuations sensitivity and maximum effect of interpolation. n

MAE = ∑ Z ' ( xi ) − Z ( xi ) n

(5)

.

i =1

RMSIE =

n

∑[ Z ' ( x ) − Z ( x )] i

2

i

i =1

Table 1. The result of reverse-weighted interpolation

n

.

(6)

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Table 3. The result of co-kriging interpolation

Zi is the actual observed value of the i point, Z’i is the estimation value of it, n is the amount of docimastic station. Prepeocess the data will make the data tends to normal distribution and improve the accuracy of estimation. In this paper we adopt square root transformation to improve the accuracy of estimation. 3.2 Analysis the Result of Interpolation Paper use square root transformation, adopt reverse-weighted interpolation, kriging interpolation, co-kriging interpolation, get 6 results of interpolation. Compare the 6 results, find the best interpolation fits to XueYe reservoir. Analysised years of data we find that: most month have better interpolation accuracy after square root transformation. Compare with original data co-kriging interpolation after square root transformation has better interpolation accuracy in month 1,2,3,4,5,8,9,10,11,12.Compare with original data kriging interpolation after square root transformation has better interpolation accuracy in month 1,2,4,5,6,7,10,11,12. Compare with original data reverse-weighted interpolation after square root interpolatio has better interpolation accuracy in month 1,3,4,5,6,7,8,10,11,12. Compare with the other two interpolations after square root transformation cokriging interpolation has better interpolation accuracy in month 1, 3, 4, 5, 8, 9, 10, 12. Original co-kriging interpolation has better interpolation accuracy in month 7. After square root transformation kriging interpolation has better interpolation accuracy in month 2,6,11. The MAE of co-kriging interpolation after square root transformation is 3.33, RMSIE is 4.29; The MAE of original data co-kriging interpolation is 3.42,RMSIE is 4.38; The MAE of kriging interpolation after square root transformation is 3.41, RMSIE is 4.38; The MAE of original data kriging interpolation is 3.47, RMSIE is

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4.45; The MAE of reverse-weighted interpolation after square root transformation is 3.53, RMSIE is 4.61; The MAE of original data reverse-weighted interpolation is 3.64, RMSIE is 4.63. Through the analysis of data we find that, co-kriging interpolation after square root transformation has a higher precision than the other interpolation, is more suitable for the actual situation XueYe reservoir.

4 Conclusion Analysised years of data we find that:the rainfall of XueYe has relatively close relationship with elevation, so the interpolation irrelevant with elevation is not fit to this area. Because after square root transformation the data will tends to normal distribution, so before interpolation we adopt square root transformation, experimental data shows that after square root transformation most month have better result. After analysis several common interpolation, we get the following conclusion: co-kriging interpolation after square root transformation is fit to XueYe reservoir.

Acknowledgements This study has been funded by Office of ShanDong Government Flood Control and Drought Relief Headquarters. The project name is the research and development of the system of optimal scheduling about flood resource. It is supported by Shandong Agricultural University. Sincerely thanks are also due to XueYe reservoir for providing the data for this study.

References 1. Goovaerts, P.G.: Approaches for incorporating elecation into the spatital interpolation of rainfall. Journal of Hydrology (228), 1113–1291 (2000) 2. Band, C.: Forest ecosystem process at the warter scale:Basis for distributed simulation. Ecol. (56), 171–196 (1991) 3. Christopher, D., Waync, P.G., George, H.T., Gregory, L.J., Phillip, P.: A knowledge-based approach to the statistical mapping of climate. Climate Research (22), 99–113 (2002) 4. Dabid, T.P., Daniel, W.M., Ian, A.: A comparison of two statistical methods for spatital interpolation of Canadian monthly mean climata data. Agriculutural and Forest Meteotology (101), 81–94 (2000) 5. Dirks, K.N., Stow, C.D.: Highresolution studies of rainfall on Norfolk Island, Part II:interpolation of rainfall data. J. Hudrol. (208), 187–193 (1998) 6. Bartier, P.M., Keller, C.P.: Multivate interpolation to incorporate thernatic surface data using inverse distance weighting. Computer & Geoscience (22), 795–799 (1996) 7. Huiyi, Z., Shaofeng, J.: Uncertainly in the spatial interpolation of rainfall data. Progress in Geography (23), 34–41 (2004) 8. Houghton, J., Meira, L.G., Callander, B.A.: Change 1995: the Science of Climate Change. Journal of Hydrology (1996)

Study on Vegetable Field Evaluation Index System for Non-Point Source Pollution of Dagu River Basin Jinheng Zhang1, Junqiang Wang2, Yongliang Lv1, Jianting Liu1, Dapeng Li1, Zhenxuan Yao1, Xi Jiang1, and Ying Liu1 1 Institute of Eco-environment & Agriculture Information, School of Environment and Safety Engineering, Qingdao University of Science and Technology, Qingdao, Shandong 266042, China 2 Qingdao Station for Popularizing Agricultural Techniques, Qingdao, Shandong 266000, China

Abstract. This research combined data from soil, terrain and meteorological surveys, with quantitative data from soil and water field studies to determine the leaching processes of nitrogen and phosphorus contaminants from field vegetation into the Dagu River. Using the Gray correlation analysis method, precipitation and irrigation were established as the major driving force behind leaching losses from cultivated land. The type of fertilizers used, and the chemical and physical properties (porosity and bulk density) of soils in the region were set as parent sequences. COD values of nitrogen and phosphorus concentration were calculated from soil and water samples. Also, the depth of core samples was factored into the analysis. The result showed that the supply of soil nitrogen and phosphorus, which were mainly derived from fertilizers, were not generally high levels in Qingdao. According to fuzzy association degree, this investgation confirmed main influencing factors which affected COD, total nitrogen, total phosphorus of soil and groundwater, ammonia nitrogen and nitrate nitrogen of groundwater respectively. And according to these factors the initial vegetable field evaluation index system for non-point source pollution of Dagu River basin was constructed. Keywords: Gray correlation analysis, Nitrogen, Phosphorus, COD, Precipitation, Soil physical properties, Soil type, Index system.

1 Introduction Excessive nitrogen and phosphorus values used in fertilization will significantly decreases the economic benefits of enhanced crop production, as well as increases the risk of nitrogen and phosphorus contaminants in water reserves [1-3]. Approximately, 60% of water pollution is caused by non-point source NPS) pollution in America [4]. In the Northern Australia, NPS pollution flowing into water reserves is also the major source of nitrogen contamination [5]. In Denmark, 94% of the nitrogen load and 52% of the phosphorus load in 270 rivers is a result of non-point source processes [6]. The same effects can be seen in the Netherlands, where 60% or the total nitrogen contaminants and 40-50% of the total phosphorus contaminants are caused by NPS pollution [7]. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 706–715, 2011. © IFIP International Federation for Information Processing 2011

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In recent years, as agricultural development mirrors population growth in China, NPS pollution has become the main source of water pollution. Currently, China contains 1600 million hm2 of cultivated land, which amounts to 35 of the agricultural land on the planet [8]. Statistics measuring the amount of fertilizers, livestock and poultry manure and the cultivated land area from 2000 to 2006 in Qingdao were investigated. Subsequently, an index was established to evaluate the nitrogen and phosphorous content in livestock manure as NPS pollutants [9]. A Grey Correlation study to evaluate water pollution from vegetable fields in the Dagu River Basin of Qingdao[10]. Over the years, the Dagu River has become increasingly more important to the national economy and to the lives of local residents. However, as the region surrounding the Dagu River Basin is developed, pollution of the river has become increasing obvious. It is the fertilizers and pesticides that have aided the agricultural development that threaten the water reserves. The numerous and excessive applications of fertilizers and pesticides cause soil erosion and serious water pollution from increasing nitrogen and phosphorus concentrations. This study integrates data from soil, terrain and meteorological surveys of Dagu River basin, with quantitative analysis studies of thousands of soil samples to construct an indexing system of NPS pollutants entering the Dagu River. The Dagu River is located in the western Shandong Peninsula, between 120 ° 03 '~ 120 ° 25'E and 36 ° 10 '~ 37 ° 12'N and covers an area of 4631.3 km2 in Qingdao. The river runs through five districts in Qingdao, Laixi City, Pingdu, Jiaozhou, Chengyang, Jimo (see Figure 1). The annual precipitation in the region is about 685.3 mm and an annual runoff is about 6.311×10 m3 [11].

％

2 Materials and Methods 2.1 Sample Collection For this study, soil and water samples were collect in December of 2009 from cultivated land throughout the region (Figure 1). The samples of vegetable field soil were divided into two categories, surface soil (0-20 cm) and submerged soils (80-100 cm). Additionally, surface water and ground water samples were collect from the same areas. GPS was used to map the latitude and longitude of sample areas. All samples were analyzed in the laboratory, using quantitative methods. 2.2 Sample Analysis To determine the COD of water samples, they were heated in a strongly acid solution, containing potassium dichromate. A Euro Tech ET3150B multiple digester and an ET1151M COD Monitor were used for characterization. K2S2O8 digestion molybdenum blue colorimetry was used alongside the COD digester to analyze the total concentration of phosphorus in samples. Nitrite has a visible absorption at 220nm, so a 752SP UV was used to measure its concentration in aqueous samples. Nessler's reagent was added to pretreated samples, and, then, a spectrometer was used to measure their absorbance at 420 nm. Also, oxidation-ultraviolet spectrophotometry was used to

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determine the total nitrogen content of samples, after the addition of potassium sulpersulthephate at 120~124 . Soil samples were first treated with alkaline potassium persulfate. Then, UV spectrophotometry was used to measure each solutions absorbance at 220nm, 275nm and 700nm to measure their total nitrogen and phosphorus content. Potassium dichromate was added to soil samples before COD analysis.

Fig. 1. Soil samples distribute

3 Results and Analysis 3.1 The Main Source and Loss of Non-point Source Pollution Fertilizer, irrigation, and atmospheric deposition are the main sources of nitrogen and phosphorus in cultivated land within the basin. Among these, fertilizers are the most abundant source of the elements. There was a large quantity of NO3--N in samples, as it is in not readily absorbed by soil colloid [12]. Rainfall and irrigation contributed to the loss of accumulated NO3--N, via surface run-off (procedure C in figure 2) and/or through leaching (procedure A and B in figure 2) [13],[14]. 3.2 The Substance Basis of Fertilizer Leaching and Driving Force of Vegetable Field Soil Nitrogen and Phosphorus A classification system was developed from the analysis of more than 7000 soil samples, which were classified on the bases of total nutrient content. Results were classified into six categories based on nitrogen supply level from vegetables. The data is shown in table 1. We analyzed more than 1000 soil samples and classified the soluble phosphorus content. Results showed that very high soluble phosphorus supply level of vegetable land was only 7.83%, but general and lower supply level were 76.99% (Table 2).

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Table 1. Classification total nitrogen in soil (Data from Qingdao soil, edited by the Soil and Fertilizer Workstation of Qingdao City) field supply level high extremely high

Upland area acre % —

—

Vegetable land acre % ——

—

5938

0.07

10174

3.16

general

1060672

12.14

211928

65.78

lower

4830122

55.31

100042

31.06

low extremely low

2676373 160226

30.65 1.83

— —

— —

Table 2. Classification soluble phosphorus in soil (Datas from edited by the Soil and Fertilizer Workstation of Qingdao City) field Supply level

Upland area

Vegetable land

high higher general

acre — — 277110

% — — 3.17

acre — 25207 147990

% — 7.83 45.93

lower

1797677

20.58

129624

40.24

low

3419031

39.15

19323

6

extremely low

3239513

37.1

—

—

From the above statistics, we found that nitrogen and phosphorus supply level in Qingdao vegetable land were not very high. Additionally, fertilizer was the main source of nitrogen and phosphorus. Subsequently, it was reasonable to study fertilizer as a basic source of fertilizer leaching. Meteorological precipitation data combined with crop irrigation were overwhelmingly contributed to the leaching loss of nitrogen and phosphorus from soil. 3.3 Determine Leaching Loss Factors of Nitrogen and Phosphorus according to Grey Relational Analysis Types of fertilizer and soils, the physical properties of different soils (porosity and bulk density), precipitation data, soil nitrogen and phosphorus content, and soil COD were indexes in the analysis of leaching nitrogen and phosphorous. Gray correlation was used to analyze the effect of these indexes on the contaminant concentrations in the local water reserve. An index system based on well-correlated factors related to nitrogen and phosphorous loss from agricultural soil was established. The factors are

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listed below, in decreasing order: ammonia content, nitrate content, total nitrogen of groundwater, soil type, soil porosity (from shallow to deep), soil bulk density (from shallow to deep), precipitation, soil COD and soil total nitrogen content. Fertilizer plays the most significant role in affecting the quality of groundwater, followed by soil physical properties and soil chemical properties. The order of factors affecting total phosphorus content of groundwater was soil type, soil phosphorus content, soil COD, phosphate fertilizer of per hectare, soil porosity (from shallow to deep), soil bulk density (from shallow to deep) and precipitation, respectively. Phosphorus leaching from soil was the main source of phosphorus in groundwater. 3.4 Single Factor Assessment Index of Non-point Source Pollution 3.4.1 Fertilizer Grade of Nitrogen and Phosphorus Leaching Loss Compound fertilizer, with similar nitrogen and phosphorous contents commonly used fertilizers, was applied to survey region (total nitrogen content 15% according to N, total phosphorus content 15% according to P2O5). The result from formula (1, 2) gave a mean value of fertilizer in survey region of 150.7 kg/hm2. The standard deviation was 139.6. Mean value: n

x=

∑x

i

i =1

(1)

n

Standard Deviation: n

σ=

∑(x

i

− x) 2

(2)

i =1

n

Probability Density:

f ( x) =

1 2π ⋅ σ

e

−

( x−x)2 2σ 2

(3)

According to probability density calculations (figure 2), the second center should be considered as the average nitrogen concentration in fertilizer per hectare in every season. The standard deviation was divided into five intervals: 0~80, 80~220, 220~360, 360~500 and >500 kg·hm-2·season-1 (see table 4). An advantage of this division was that similar values can be drawn in same level, which reduces the risk that similar dispersed within the data. 3.4.2 The Precipitation Grade of Nitrogen and Phosphorus Leaching Loss When long-term soil leaching occurs, the optimum conditions were as follows: the effects of the sum of precipitation and irrigation are more than the effects of the sum of run-off, evaporation and good soil infiltration. Conditions of short-term soil leaching

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mirrored large amount of precipitation or irrigation [15]. In the study area, precipitation is heaviest from June to August, about 500mm, which accounts for more than 50% of the annual precipitation. In the summer irrigation was lessened, due to the increase precipitation. In this analysis, 200mm precipitation was used as the lower limit and was divided into 5 levels in 100 mm intervals. Short-term soil loss reached measurable level when single precipitation/irrigation events exceeded 50mm (see the table 5). Table 3. Sequence of gray correlation Table 3a

Ammonia Nitrogen

Groundwater Nitrate Nitrogen

Total Nitrogen

Nitrogen fertilizer

1

1

1

Soil type

2

2

2

shallow layer

3

3

3

medium layer

4

4

4

deep layer

5

5

5

Soil porosity

Soilbulk density shallow layer

6

6

6

medium layer

7

7

7

deep layer

8

8

8

Precipitation

9

9

9

COD of soil

10

10

10

Soil total nitrogen

11

11

11

Table 3b.

TP of groundwater soil type Soil total phosphorus COD of soil Nitrogen fertilizer Porosity (deep layer) Porosity (medium layer) Porosity (shallow layer) bulk density (shallow layer) bulk density (deep layer) bulk density (medium layer) Precipitation

1 2 3 4 5 6 7 8 9 10 11

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Fig. 2. Probability density function of fertilization Table 4. Fertilizer grade of nitrogen and phosphorus leaching Compound fertilizer -2 -1 (kg.hm .season )

Grade

Description

<80

F1

Fertilizer less than the average fertilizer level. The substance basis of leaching loss was not enough, and the leaching loss won’t be happened.

80~220

F2

Fertilizer was near the average amount. On the occasion of heavy rain or over-irrigation, light leaching loss will be happened.

220~360

F3

360~500

F4

>500

F5

The light leaching loss will be happened. The degree will increase according to rate of fertilizer and water. Middle leaching loss level. But heavy leaching loss can be happened. The rate of fertilizer is more than average rate. (Super) heavy leaching loss level.

Table 5. Driving factor grade of nitrogen and phosphorus fertilizer leaching Precipitation (mm. season-1)

grade

<200

W1

200~300

W2

300~400

W3

400~500

W4

>500

W5

description Shortage of precipitation. The general leaching loss won’t be happened. Precipitation met the requirements of leaching loss. Light leaching loss may be happened. Precipitation met the requirements of leaching loss. Light leaching loss may be happened. Precipitation was adequate, and moderate leaching loss may be happened. Precipitation was extremely adequate, and heavy leaching loss may be happened.

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3.4.3 The Soil Type Grade of Nitrogen and Phosphorus Leaching Loss Soil type played an important role in leaching loss. Under similar conditions, clay soil showed minimum nitrogen and phosphorus leaching loss, whereas, the largest nitrogen and phosphorus leaching loss was from sand soil. Medium loss was observed in loam soil [16],[17]. The soils of Dagu River, which can be used to cultivate vegetables, are brown earth, aquic brown earth, brown paddy soil, cinnamon soil, eluvial cinnamon soil, developed cinnamon soil, lime concretion black soil, and fluvo-Aquic soil. Nitrogen and phosphorus leaching loss was divided into three categories, according to an analysis of the physical property of the soils, including total soil porosity and soil bulk density (see table 6). Table 6. The soil type grade of nitrogen and phosphorus leaching loss Soil type

grade

description

brown earth

－

It widely distributes in the hill, valley and the front slope of mountain. The land is thick, and there is clayey layer generally. It played reduced role on nitrogen and phosphorus leaching loss. High degree of maturation, viscous soil. It played reduced role on nitrogen and phosphorus leaching loss Thin layer, rough texture, high impurity content. Lower capability of moisture and fertilizer conservation. An enhanced nitrogen and phosphorus leaching loss type. The process of sticky soil obviously, soil deep. Higher capability of moisture and fertilizer conservation. It played reduced role on nitrogen and phosphorus leaching loss. Distribution in the slope and valley. Thick layer. Higher capability of moisture and fertilizer conservation. It played reduced role on nitrogen and phosphorus leaching loss.

aquic brown earth brown paddy soil cinnamon soil Eluvial cinnamon soil developed cinnamon soil lime concretion black soil, fluvo-Aquic soil

－＋－－＋－ /

Thin layer. Gravel. An enhanced nitrogen and phosphorus leaching loss type. Thick layer. Sticky. It played reduced role on nitrogen and phosphorus leaching loss. Different physical and chemical properties and different degree of reposado. The soil type grade of nitrogen and phosphorus leaching loss was unclear.

Fig. 3. The Soil Body Configuration of nitrogen and phosphorus leaching loss

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3.4.4 The Soil Body Configuration Grade of Nitrogen and Phosphorus Leaching Loss According to results of table 5, when sandy soil is present in the upper layer, leaching will occur more readily. On the contrary, when clay soil is present, leaching will be respectively lower. Consequently, we classified the nitrogen and phosphorus leaching loss according to the soil composition. Areas of sand-layered and thin-layered soil were classified as enhanced nitrogen and phosphorus leaching loss types. Clay layered, intercalated clay layered, Mengyu type, and Mengyin type soils were as classified as reduced nitrogen and phosphorus leaching loss types.

4 Conclusions Fertilizer was as the main source of nitrogen and phosphorus NPS pollution. Precipitation was the most significant driving force of leaching loss of nitrogen and phosphorous in regional soils. The application of fertilizer is the most significant contributor to nitrogen content in groundwater, followed by the physical and chemical properties of regional soils. The results showed that the most influential factors determining the total nitrogen content of groundwater was the soil type, chemical properties of the soil, phosphorus content, the physical properties of the soil, and precipitation, respectively. The amount of nitrogen per hectare and the precipitation in a season were divided into five levels respectively. Nitrogen and phosphorus leaching loss per soil type was classified as follows, enhanced grade, reduced grade and uncertain grade. Soil body configurations of nitrogen and phosphorus leaching loss were classified as enhanced and reduced grades. Acknowledgments. Project supported by the Science and Technology Projects of Qingdao (08-2-1-36-nsh and 09-1-1-53-nsh).

References 1. Xing, G.X., Shi, S.L.: Situation of nitrogen pollution in water bodies in SuZhou region. Acta Pedologica Sinica 38(4), 540–545 (2001) 2. Xiong, Z.Q., Xing, G.X.: Non-point N pollution of lakes, rivers and wells in the Taihu Lake region. Rural Eco-Enivironment 18(2), 29–33 (2002) 3. Xu, Q.X., Meng, Z.F., Yu, C.H.: Approaches for reduction of nitrate contamina¬tion on vegetable by appropriately applying fertilizers. Agro-environmental Protection 19(2), 109–111 (2000) 4. Corwin, D.I., Wagenet, R.J.: Application of the Modeling of non-point Sources Pollutants in the vadose zone. Journal Environment Quality 25, 403–411 (1996) 5. Griffin, J.R.: Introducing NPS Water Pollution. EPA Journal, 6–9 (November /December 1991) 6. Kronvang, B.: Diffuse nutrient losses in Denmark. Water Science Technology 133, 81–88 (1996) 7. Boersp, C.M.: Nutrient emissions from agriculture in the Netherlands: causes and remedies. Water Science Technology 33(1), 183–190 (1996)

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8. Feng, H.X., Yang, X., Li, X.Y., Wang, M.Z.: The effects of continuous cropping of vegetables on the biochemical properties of soil. Journal of ChangJiang Vegetables 11, 40–43 (2004) 9. Zhang, J.H., Wang, J.Q., Wan, Y., Han, C.: Agricultural non-point source pollution investigation and assessment in Qingdao. Chinese Agricultural Science Bulletin 26(10), 276–280 (2010) 10. Zhang, J.H., Wang, J.Q., Liu, J.T., Yao, Z.X., Li, D.P.: Evaluation of surface water pollution in Qingdao vegetable area based on gray correlation method. Shandong Agricultural Sciences 5, 78–82 (2010) 11. Zhou, G.Z., Zhang, J.H., Wang, J.Q., Li, J.: Application of the fuzzy mathematics in evaluation Dagu River water quality. Journal of Agro-environment Science 29(suppl.), 191–195 (2010) 12. Zhang, G.L., Zhang, S.: Advances of cropland Nitrogen Leaching. Soil 6, 291–297 (1998) 13. Liu, J.L., Li, R.G., Liao, W.H.: The yield response of vegetable to phosphate fertilizer and soil Phosphorus accumulation in a Chinese Cabbage-capsicum Rotation. Scientia Agricultura Sinica 38(8), 1616–1620 (2005) 14. Shi, C.Y., Zhang, F.D., Zhang, J.Q.: Change of soil nutrients under greenhouses under long-term fertilization condition. Plant Nutrition and Fertilizer Science 9(4), 437–441 (2003) 15. Johnes, P.J.: Evaluation and management of the impact of land use change on the nitrogen and phosphorus load delivered to surface waters, the export coefficient modeling ap¬proach. Journal of Hydrology 183, 323–349 (1996) 16. Chen, S.G.: Dry calcareous soil characteristics and nitrogen volatilization loss of ammonia Channels. Agricultural Research in the Arid Areas 3, 28–37 (1988) 17. Chen, X., Jiang, S.Q., Zhang, K.Z., Bian, Z.P.: Law of phosphorus loss and its affecting factors in red soil slopeland. Journal of Soil Erosion and Soil and Water 5(3), 38–41, 63 (1999)

Study on Water Resources Optimal Allocation of Irrigation District and Irrigation Decision Support System Liang Zhang1,2, Daoxi Li2, and Xiaoyu An2 1

2

Zhengzhou University, Zhenzhou, P. R. China North China University of Water Resources and Electric Power, Zhenzhou, P. R. China [email protected]

Abstract. This paper develops the system of optimal allocation of water resources in irrigation district and irrigation decision-making support which integrates technologies of decision-making support, information management, information search and so on. It has the multi-function of water production function calculation, crop water requirement calculation, water resources optimal allocation and real-time amending the decision of irrigation. This system integrates the experience of experts with computer technology to guide farmers to irrigate in a proper way, for which the limited water resources can produce a marked effect on irrigation, so irrigation district management and efficiency are improved. Keywords: Optimal allocation of water resources, irrigation district, watersaving irrigation, decision support system.

1 Introduction As we all know, agriculture is the main consumer of water. At present, agriculture of China faces water shortage, for agricultural water has been diverted by industrial water and domestic water, meanwhile, serious waste of agricultural water, lack of corollary irrigation facilities, sever defacement of trenches and long-term flooding irrigation lower the effective availability of irrigation water, as a result, valuable water can’t work in due course, therefore, we must vigorously advocate water-saving irrigation to construct a water-saving agriculture[1]. The key of water saving in agricultural irrigation is management[2], so management is meaningful in this area. There have been many studies about water-saving irrigation in China, but most of them are about hardware of facilities of water-saving irrigation, few about software such as water resources and optimal allocation[3]. To apply computer technology to irrigation district management will make full use of the valuable experience of experts in water conservancy with the help of modern technology to make a significant contribution to the improvement of irrigation district management, for which study on optimal allocation of water resources in irrigation district and irrigation decision supporting system is significant1. 1

Endowed by national science and technology supporting project (2007BAD88B02) (2006BAD11B09-2).

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 716–725, 2011. © IFIP International Federation for Information Processing 2011

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2 A Brief Review of DSS Applied in Water Resource Management Decision support system (DSS) is a form of information system development, which is firstly pointed out by Scott Norton in 1971. It is an information system based on computer to support decision-making action, and a kind of interactive software aiming at assistant decision-making of application problems such as planning, management, scheduling, combat command and scheme optimization. With the developing of computer technology and the introducing of expert system and artificial intelligent technology, DSS technology is combined with other various technologies and has been developed into expert system and intelligent decision support system. DSS has been applied to water resource management since 1980s. Raboh.R.Reiter, J.and Gaschnig. J(1982) [4] presented HYDRO system to supply a similar parameter to watershed feature parameter which is selected by hydrological experts with great efforts and be applied to estimating the effect of different hydrological factors. Palmer. R. N and Tull, R. M(1987) [5], Palmer, P.N and Holmesk. J(1988) [6] have successively invented SID and WMS related to expert system of drought management plan, these two systems are similar in function and can be used for predicting and displaying the information related to drought management plan. Based on discrimination of the similar degrees of present drought and past drought, according to their experience, users can make decision of water quantity optimal distribution with linear programming model. Based on drought degree, Raman.H[7] has built up expert system with linear programming model to conduct crop optimization to guide drought scheduling decision that irrigation system will face in the future. CADSM[8] model is an expert system functioning as decision support, which can simulate crop yield and crop water-needing process to predict the effect of soil salinity and moisture on yield to supply users with water distribution plan of different canal systems. The software of water resource management decision support system in China is developed and applied relatively late. Wenbing Weng et al. (1992)[9] develope decision support system of water resource planning of JIng-Jin-Tang area (Beijing, Tianjin and Tanggu), this system has the function of expert knowledge and consultation. Jianxin Xu(1999)[3] developes regional water resource planning and irrigation water-saving irrigation development of Expert system through analyzing main factors of irrigation technology choice in irrigation area and introducing semi-structured and multi-objected optimum technique. Based on Penman’s formula, Zhouping Shangguan(2001)[10] combines with present agronomy knowledge of northwest arid area, model and experience to conduct system integration to build up intelligent decision support system by using artificial intelligent technology. Hujun Shang(2002)[11] points out developing model combined with data-based system , expert system and computer simulation through researching water-saving irrigation prediction and decision management database system. However, the studies mentioned above are mainly theoretical, exploratory or expert consulting; most results of them haven’t been perfect and practical yet. So far the study on water resource management and agricultural irrigation decision support system in China still exists in the exploring stage.

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3 Introduction of the System The developing and operating of water resources optimal allocation of irrigation district and irrigation decision support system in this study are based on Chinese Windows platform, main framework of this system software is compiled with Borland Delphi 7.0, using Delphi, some numerical computation modules are developed by data interface with the method of Matlab, the program of assistant decision database of water-saving irrigation is designed by Paradox 7.0 formula. This system is a system which has a database, a data management system and model calculation program library. It has a brief human-computer interface and a capability of reasoning and outputting the result of words and charts. Fig.1 indicates the system structure.

Fig. 1. System structure

The general function of the management system of database is to memorize, search, collate, collect, and survey all sorts of data. Meanwhile, this system can also supply necessary data for related results; this system can be divided into three parts in general, as follow: Database of fundamental data, which deposits all the basic data of irrigation districts including the proportion of them, crop planting, social economy condition of them, population information of them and so on. Text database, which deposits the data in form of text, mainly including calculation result memorized in form of text which calculated by working model. Chart database, which mainly deposits all sorts of charts of system, and can output the calculation result in form of charts according to the requests of the customers, by the way, the output information will be made more intuitionistic.

①

③

②

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①

The reasoning system of model calculation contains three parts: Crop water production function calculation; Water resources optimal allocation of irrigation districts; design of optimal irrigation system under the condition of insufficient irrigation. These subparts not only can work independently but also can work together as a whole.

②

③

4 Key Technology of Model 4.1 The Constitution of Water Production Function and Parameters Calculation 4.1.1 Analysis of Water Production Function In order to conduct water resources optimal allocation to achieve the highest efficiency, we must set up the function of irrigation amount and yield of all sorts of crops in different hydrological years and make use of indirect relationship between irrigation amount and yield to get the function of irrigation amount and net benefit. In real course of solving, the function formula of irrigation amount—crop yield in different hydrological years is constituted firstly as usual, and then irrigation amount—irrigation benefit function as follow is constituted by using the relationship between input and output. By derivation of it, the change rate of benefit of crop in every unit of irrigation amount can be achieved, that is marginal benefit. According to marginal benefit, efficient irrigation method of limited water resources for crop can be inferred. The formula of water production function usually has the linear relationship, quadratic parabola relationship, power exponential function and so on. Based on a great amount of research result, and analysis of the experiment data of past irrigation experiments, the author thinks that as for the plain areas of the Yellow River, the Huaihe River and the Haihe River in China, water production function had usually better appear to be a quadratic parabola. 4.1.2 The Solving Principle of Water Production Function Supposing the basic formula as follow:

<

D[ E[ F

(1)

In the formula above, Y stands for yield per unit area (kg/hm2); X stands for water consumption per unit area (m3/hm2); a, b and c are regression analysis coefficient which can be achieved with the least square method. The function relationship between irrigation amount and crop yield increase can be educed by deducting the yield without irrigation from the crop yield in all. According to the formula below[12], the increasing production benefit and irrigation benefit of all sorts of crops corresponding to different irrigation amount in different hydrological years can be educed.

ǺL

Ȁ
(2)

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In the formula, Bi represents increasing production benefit (Yuan/hm2); K represents the price of crops (Yuan/kg); Yi stands for crop yields of different irrigation amount in unit area ; Yo stands for crop yield without irrigation in unit area; C represents related water charge for different irrigation amount. Irrigation benefit can be educed by the formula as follow:

%H L

H%L

(3)

In the formula above, Bεi stands for irrigation benefit (Yuan/hm2); ε represents the sharing coefficient of irrigation factor. As usual, winter wheat takes ε=0.45, other crops take ε=0.4. 4.2 Calculation of Water Resources Optimal Allocation of Irrigation District This paper mainly studies how to distribute limited water resources in a proper way to achieve the maximum total benefit of an irrigation district without changing the agricultural planting structure. Dynamic programming method is adopted here to study water resources optimal allocation[13], mathematic model should be constituted firstly, and the model includes five parts as follow: Phase variant, as usual each crop is regarded as a water consumer, that’s a phase. As for an irrigation district with N sorts of crops, it has N phase variants, that’s to say phase variant n =1, 2, …, N. Condition variant and decision-making variant, condition variant is water quantity can be distributed in each phase, which is qn for short; decision-making variant is the water quantity supplied for every crop in each phase, which is xn for short. System equation, according to the relationship between condition variant and decision-making variant, system equation can be reckoned, as follow:

① ② ③

qn+1=qn-xn

(4)

④

Objective function: supposing F(w) is maximum net benefit achieved by contributing total water W to N sorts of crops, then:

F

（ w ）= max ⎧⎪⎨⎪ N∑

⑤ Constraint condition

⎫⎪ r ( x )⎬ n n ⎪ ⎩i = 1 ⎭

(5)

a. the sum of water quantities for every crop can’t exceed total water resources. b. the water quantity xn for crop of n can’t exceed the minimum among the largest passing water capacity of trench, available water of water source and total water resource, and it can’t be negative.

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c. qn (n=1,2,…,N) can’t exceed the water quantity which is possibly supplied for n sorts of crops at the same time by every phase. Initial condition : q1=W Making use of the constrain conditions, and initial condition above, we can constitute dynamic programme model of water resources optimal allocation, and solve the mathematic model above by the way of inverted sequence recurrence, the recursive equation as follow:

⑥

f

n

(q

f

n

N

) =

max

0 ≤ x 0 ≤ q

n n

≤ q ≤ W

( q ) = max

n

{r

n

{r N

( x

) f

n

(x

N

n + 1

)}

(q

n + 1

}

( n = 1, 2 ,..., N − 1)

(6)

n = N ,0 ≤ x N ≤ q N

(7)

)

4.3 The Design of Optimal Irrigation System (Optimal Allocation of Water for Every Growing Stage of Crops) Under the condition of limited water and water in short supplying, it’s impossible to completely meet the need of water for crops in each stage and the whole course of their growing, in this way, proper irrigation should be insufficient irrigation which is to realize water optimal allocation among different crops and different growing phases of them to make sure water supply of every crop in its water requirement critical period and reduce water supply in water requirement uncritical period, by this way, what we seek for is the highest total yield in the whole irrigation district under the condition of limited water resources. Under the condition of limited water supply, for water supply of each crop usually exists in the left hand of the point in which water supply can realize the maximum benefit, the optimal irrigation course design of a single crop this paper aims at the highest yield. With Jensen’s model, we can design irrigation course, the model as follow:

F = max(

n Y ETi λi ) = max ∏ ( ) Ym i =1 ETmi

(8)

There are several rules we should obey to conduct system design of optimal irrigation:

① Real quantity of water supply should be based on the quantity of optimal allocation water supply. ② Under the condition of insufficient irrigation, moisture content of planned soil ③

wetting zone of a single crop is controlled between field capacity and wilting coefficient. The design of irrigation quota must accord with the reality of agricultural production and deep percolation mustn’t occur.

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L. Zhang, D. Li, and X. An

④ The system design must make good use of rain water, and irrigation mustn’t result in extra-runoff of rain water. ⑤ The design of irrigation course must meet the water supply constraint and the constraint of water conveyance capacity[14].

In the course of designing optimal plan, we take the way of simulation model with the basic principle of water balance equation to fulfill the irrigation decision-making course by compiling computer decision support system software. Firstly, the necessary basic material such as effective rainfall, field capacity, initial soil water content is input, in this way, computer can automatically calculate the real water requirement in every growing stage of crops in the background. Secondly, irrigating water time, quantity of irrigating water, surplus water and the yield ratio of crops under the condition of sufficient irrigation and their real yield can be forecasted after inputting limited irrigating water quota of crops in their growing stages and the bound of the lowest moisture rate by human-computer interface, and then based on the primary irrigation project, the highest rate of real yield of crops and their yield under the condition of sufficient irrigation can be achieved by making sure the modified indexes from the changing course chart of soil moisture rate of planning moisture layer and modifying designing project repeatedly. If there are some changes in the course of real rainfall, rainfall course, limited irrigating water quota of crops in their growing stages and the lower bound of the lowest moisture rate can be carried out real-time modification to achieve the highest yield crops. This method is applicable to all the irrigating course design of crops under different rainfall frequency.

5 Example for Software Application We take an irrigation district of Daming County Hebei Province as an example, control area of this irrigation district is 73km2, irrigation area of it is 4667hm2, the main crops and their growing rates are respectively winter wheat 65.5%, spring cotton 11%, spring corn 23.5%, summer cotton 10%, and summer corn 55%, multiple cropping index 1.655. Total water supply of surface water in this irrigation district is 1650×104m3 in the year of P=75%. 5.1 Optimal Allocation of Irrigation Water Resources Firstly water production function of crops can be solved with local irrigation experiment material. Secondly the total water supply is dispersed as a calculation unit of 10×104m3, and then based on irrigation benefits solved of different crops, by dynamic planning mathematic model which has already been built, with the method of inverted sequence recursive, water optimal allocation calculation is carried out step by step. At last, optimal allocation water quantity of every crop and total benefit of irrigation of the typical district in the year of P=75%. Fig.2 shows the results of irrigation benefits of different crops and benefits of pre unit water.

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Fig. 2. Result of optimal distribution of irrigation water resources

5.2 Carrying Out Irrigation System Design of the Single Crop According to irrigation quota of each crop under the condition of total water optimal allocation, by using water balance equation at the time in which water content of

Fig. 3. Result of irrigation system

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planned moisture layer is less than 0.7 time of field water-holding ratio, under the condition of insufficient water supply for crops, the quantity of water consumption of the crops is modified. After three times trial calculation, irrigating water quota and irrigating time of irrigation district can be made out as fig.3. 5.3 Result Analysis From the optimal allocation of water quantity of each crop and the result of optimal irrigation system, we can find that among all the crops, the total water quantity distributed to winter wheat ( )and total irrigation quota of it are the largest for its benefit of per unit water is the highest, while those of summer cotton and summer corn are smaller and lower for the water-needed course of these crops happens to meet raining course, the yields of these crops are relatively high without irrigation. All the crops can be irrigated in their critical water-needed phase, so this optimal irrigation decision-making course with the system design is reasonable.

冬小麦（夏播棉）

（单方水效益）（夏玉米）

（灌溉定额）

6 Conclusion This paper applies DSS and technique to water resource optimization of irrigation district and irrigation decision and management to establish water resource optimization and irrigation decision support system that includes water production function calculation, water resources optimal allocation of irrigation districts and optimal irrigation decision model. In the course of programming, we take a method of mutually contacting with each other and relative independence from each other. Through our experimenting with example, this method of programming is proved to be right and this program is practical and effective. Meanwhile, it is proved that the technique of DSS has wide application prospect in water-saving agriculture, which can improve the scientific and technological content in the field of water-saving agriculture to make irrigation management take a new step.

References

中国农业需水与节水高效农业建设

1. Shi, Y., Lu, L., et al.: Chinese agricultural water demand and wate-saving and efficient agriculture. . China’s Sustainable Development Strategy of Water Resources Report, vol. 4. China WaterPower Press. Beijing (2001) 2. Xu., J., Shen, J.: Modern technology in the field of application of water-saving irrigation. . Science Technology and Dialectical, Xi’an (3) (1999) 3. Xu, J.: Expert System of Water Resources Evaluation and High-effective Irrigation in the Irrigation Area. . Ph.D thesis of Xi’an University of Technology, Xi’an (2000) 4. Reboh, R., Reiter, J., Caschnig, J.: Development of a knowledge Based Interface to Hydrological Simulation Program, Final Report SRI International, SRI project 3477, Menlo Park, Ca (1982) 5. Palmer, R.N., Tull, R.M.: An Expert System for Drought Management planning. Journal of Computing in Civil Engineering, ASCE 1(4), 284–297 (1987)

代科技在节水灌溉领域中的应用灌区水资源评价及节水高效灌溉专家系统

现

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6. Palmer, R.N., Holmesk, J.: Operational Guidance During Drought: Expert System Approach. Journal of Water Resources Planning and Management 114(4), 647–666 (1988) 7. Raman, H.: An Expert System for Drought Management Planning. Journal of Computing in Civil Engineering, ASCE 1(4), 284–297 8. Clarkson, C., Hartigan, J.: Expert System Approach to Water Management Decisions. In: Proc Asce 6 th Conf on Compute. in Civil Engineering, ASCE, pp. 100–107 (1989) 9. Weng, W., Cai, X.: A Study on the Decision Support System of the Water Resources Planning in the Jing-Jin-Tang Economic District. . Advances In Water Science (3) (1992) 10. Shangguan, Z., Shao, M., Xue, Z.: Decision Support System of Water Requirement Forecast for Dryland Crop. . Transactions of The Chinese Society of Agricultural Engineering 17(2), 42–46 (2001) 11. Shang, H., Wang, Z., Chai, P.: Research and Development of Water-saving Irrigation Database and Its Management System. . Research of Soil and Water Conservation 9(2), 97–101 (2002) 12. Zhang, L.: PengLou irrigation area Optimal Allocation of Water Resources and Irrigation Management Model. . Master’s thesis of North China Institute of Water Resources and Hydropower, ZhengZhou (2004) 13. Cui, Y., Li, Y.: Optimal Allocation of Irrigation Water Supply under Crop Water Stress Conditions. . Journal of Hydraulic Engineering (3), 37–42 (1997) 14. Wang, P.: Irrigation Area Optimization of Irrigation Mode and Decision Support System of Real-Time Irrigation. . Master’s thesis of North China Institute of Water Resources and Hydropower, ZhengZhou (2002)

京津塘水资源决策支持系统研究

旱地作物需水量预报决策辅助系统

节水灌溉管理数据库及其管理系统的研究与开发

彭楼灌区水资源优化分配及灌溉管理模式研究

作物缺水条件下灌溉供水量最优分配

灌区灌溉模式优选及实时灌溉决策支持系统

Study on Web-Based Cotton Fertilization Recommendation and Information Management Decision Support System Yv-mei Dang and Xin Lv* Key Laboratory of Oasis Ecology Agriculture of Xinjiang Production and construction Group Shihez, Xinjiang Province, P.R. China, 832003 [email protected], [email protected]

Abstract. Based on the regional difference in soil fertility, the nutrient uptake of crop, the nutrient supplying capacity of soil and the response of fertilizers, etc., this study took the 81th regiment of the 5th agricultural division in Xinjiang Bingtuan as the training and cooperative region, and established the information system of soil nutrients for cotton fields and recommendation model of fertilization. Constructed comprehensive, digital and intellectualized WEBbased cotton fertilization recommendation and information management decision support system by using SQL+JSP+Win2000.The system had been applied successfully in 2005 and 2006 there, and in 2006 the mean yield per hm2 increased by 7.3%than that of former 3 years, and the fertilizer cost per hm2 reduced 114 yuan(RMB). Keywords: Knowledge model, Fertilization recommendation, Decision support system, SQL+JSP+Win2000.

1 Introduction In this research ,it made collection and reorganization of field soil information from all regiments through network, provides the accurate and reliable soil material, take quantitative dynamic relationship as the master line that between the crops goal output, the soil nutrient conditions and fertilization measures, construct Dynamic Knowledge Model for cotton fertilization [1][2]; on this foundation, further unifies the soil fertility management system, constructs cotton information management system and Fertilization Decision Support System which are comprehensive, digitization and the intellectualization based on the platform of SQL+JSP+Win2000 with Web-based. the system has no high demand that only browser in clilnet, and provides a uniform interface, easy operation, easy Installation Maintenance[3].so as to provide the decision support for science fertilization more intuitively. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part II, IFIP AICT 345, pp. 726–733, 2011. © IFIP International Federation for Information Processing 2011

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2 Material and Methods The system is composed of the fertilizer database ,cotton fertilizer and management system (knoweledge model and fertilizer model), the method storehouse, cotton fertilizer knowledge base, Inference Engine and technical personnel and man-machine connection.[3] (Fig.1)

Fig. 1. Framework of system fuction

2.1 Establishment of Foundation Database Establish cotton foundation databases by the information of 81th Regiment from 1998-2005 years about fields soil fertility, soil fertilizing kinds and quantity previous years, the variety and the output of crop planting, disease and insect pests and irrigation information and so on. The field soil fertilizer information be given by technician

728

Y.-m. Dang and X. Lv

to take soil samples to make chemical test, the data be provided by Testing and Analysis Center of farm, to ensure the reliability and authenticity of information data. 2.2 Establishment of Model and Model Base Based on test data, observation data and the agronomic data, we established the model of fertilizer recommendation, production prediction and Soil Evaluation, formed a model database of basic knowledge. It established the standardized fertilizer model for different soil fertility and yield level through experiment, accordance with all kinds of factors which established, combined with soil science, fertilizer science, as well as the local soil texture and expert experiences [4].Seeks the best economical fertilizer rate, the biggest profit fertilizer rate, the maximum yield fertilizer rate, best proportion and time distribution of fertilizer. Simultaneously, summarize the experience gradually and modify the original model, causes it to meet actual need. datas and the parameters can be called by each model, the result be returned to dynamic database, and realize resource sharing of database and model base. 2.3 Establishment of System Knowledge Library There is main technical knowledge which are qualitative and cannot be expressed by model at present in cotton Cultivation Management knowledge system, involves prepares before sowing (seed treatment, ploughing, application method of base fertilizer, moisture management, forecasting and prevention of plants on pests and weed control), sowing date, density knowledge, the knowledge of crop nutrition and fertilizer, the knowledge of cotton moisture management , the knowledge of cotton growth and development, the knowledge of plant protection and so on. It including: variety selection knowledge base, density knowledge base, sowing date knowledge base, the relation between sowing date and accumulated temperature, suitable sowing date, the relation between cotton sowing date and yield formation, the knowledge base of fertilizer and crop nutrition, water management knowledge base, chemistry control knowledge base, pest and weed knowledge base and prevention knowledge base, plant protection. 2.4 Method Base It is the tool to save the module of method, composed of various algorithm program which are more generality and flexibility. 2.5 Inference Engine It is the logical core of the system make inference using the knowledge to data. It make inference to the data in database by controlling the knowledge base, and draws the new conclusion. In other words, the inference Engine is one kind of strategy procedure. Production inference strategy used in this system, the inference process is:

Study on Web-Based Cotton Fertilization Recommendation

729

< Known > rule: If A then B < Known > precondition: A< new > conclusion: B 2.6 Connection of Man-Machine IE6 is basic interface in this system, can make interaction with user by draw menus, toolbars, icons, graphics and tables, selective prompting be given, you can make selection by menus, the whole operation can be completed only by computer mouse and keyboard, then user can get the model and output the results. Mean while, the system provides the help files to make explanation for using the system. In addition, the contact surface of system has the good fault tolerance too, gives the error message and the processing prompt through examination common mistake by setting error trap, to ensure the correctness by user input.

3 Results and Discussion The system of cotton information management and cotton fertilization recommendation decision support system based on WEB realizes the main function such as :data management, system management, information query, fertilizer recommendation, soil evaluation, the expert knowledge and consultation, the result output and the system maintenance management and so on (Fig.2-5). 3.1 Data Management Module There are 5 attribute database management module in the module[5] (soil basic information database, fertilizer information database, fertilizer amount of previous years, fertilizer parameter database, user information database), each administration module has the operation functions such as: increase, deletion, saves, printing, search, sorting, screening and so on. and give the right of remote input and revise to user, advanced user may renew the attribute data whenever, so make the database can always reflect the newest tendency of farmland nutrients and other management information. Also user may carry on maintenance to database and module base(mainly to data edition, update and so on).At first, data be inputed in the database, so as to be called for inquiry and recommendation. 3.2 Information Inquiry Module Provides two ways to inquire for fertilizing scheme and field information. The user can copy the data from database according to the need, simultaneously the data in database can be batch introduced into through this contact surface. Information query and screening realizes information acquisiton function according to the query condition and screening condition the user combined to the data in database[5].The table for Inquiring or screening may be the table of expert system standard, also may be the result which the system recommendation decision-making leaves, the data message inquire may be printed directly and derive.(Fig.2)

730

Y.-m. Dang and X. Lv

Fig. 2. Information Query

Fig. 3. Management of Soil evaluation

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731

3.3 Module of Soil Appraisal Primary to compute membership degree of Organic Matter, available nitrogen, available P, available N, and obtains the index value of comprehensive evaluation. (Fig.3) 3.4 The Design of Soil Fertilizer Recommendation Module The module including conventional fertilization recommendation by soil test, drip irrigation fertilizer recommendation, effect function recommendation, organic fertilizer recommendation and microelements fertilizer recommendation. Under the suggestion of guide(Fig.4),the user can input the data of soil nutrient, fill in fertilizer amount of strip field, variety selection of fertilization and establish goal output according to local actual situation. The system judges whether the data user input is reasonable based on the ordinary years’ data of meteorology, soil, variety and so through operating the knowledge model, if reasonable, then make fertilizer recommended by calling the knowledge module of fertilizer module, according to the information user filled in, the result of fertilizer recommendation can be printed on the formula to apply fertilizer card or data export by Excel, the user can modify, edits and prints the recommendation result by Excel datas exported. If unreasonable, the system modify the plan which have be made, and sends it to the fertilizer model to make forecast, so circulates, until generating a set of fertilizer recommendation plan which meet requirement[6].

Fig. 4. Frame work of fertilize recommendation

732

Y.-m. Dang and X. Lv

3.5 Maintenance and Management of System The system gives different jurisdiction to the different grades user, the user may browse, inquire, modify, increase and delete the knowledge of the knowledge base and datas of database in own purview.( Fig.5)

Fig. 5. Management and maintenance of database

4 Conclusion Precise fertilization is important part of Precision Agriculture, It is the best fertilizer plan established above the scientific method to fertilizer.This system construct comprehensive digital and intelligent decision support system based on WEB using the SQL+JSP+Win2000, and collected and arrangemented soil information of all regiments by Internet, and provides the accurate and reliable soil material, then obtained the comprehensive index of soil fertility using fuzzy mathematics principle, make fertilizer recommendation, establish fertilization model of soil nutrient, the fertility district and the formula district of crop specific fertilizer; establish the balance fertilization system of nitrogen, the phosphorus, the potassium and the trace element according to the consideration about soil supplying nutrient capability

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and fertilizer needs of crops. It will be important theory value and the practice significance to implementing balance fertilization technique and generalization of other Scientific Research Achievements.

References [1] Zhu, Y., Cao, W.x.: A Knowledge Model-and Growth Model-Based Decision Support System for Wheat Management. Sceintia Agricultura Sinica 37(6), 814–820 (2004) [2] Hao, Y.-l., et al.: Programming Technology of J2EE. The Publishing House of Qinghua university and Beijing Jiaotong University, Beijing (2005) [3] Xiong, F.l., Qiao, K.z., Hu, H.-y.: Agricultural Expert System and Development Tools. The Publishing House of Qinghua University, Beijing (1999) [4] Yang, B.-r.: Knowledge Engineering and Knowledge Discovery, pp. 423–450. The Publishing House of Metallurgy Industry, Beijng (2000) [5] Yan, C., Lv, X.: Information Management and Fertilizing Decision System Based on Soil Nutrient of SuperMap IS Cotton Field. XinJiang Agricultural Sciences 41(6), 427–430 (2004) [6] Xie, K.-w., Cheng, Y.-q.: The Design and Implementation of the Expert System Based on Database. Journal of Hubei Institute For Nationalities (Natural science) (2), 193–196 (2005)

Author Index

Ai, Jumei III-721 An, Xiaoyu II-716 An, Zhengguang I-729, I-737 Bai, Jun-Hua II-90, IV-16 Bai, Wei III-92 Bai, Yichuan III-572 Bai, Zhongke III-173 Bao, Jie III-725, IV-610 Bao, Nisha III-173 Bao, Wenxing I-48, II-124, III-132, III-464, III-491 Bi, Chunguang I-312 Bi, Lan IV-450 Bonifazi, Giuseppe IV-751 Cai, Guoyin II-243 Cai, Hongzhen III-341 Cai, Kewen IV-390 Cai, Lecai I-539 Cai, Tijiu II-682 Cao, Hongxin II-441 Cao, Qinghua IV-237 Cao, Qing-Song IV-410, IV-450 Cao, Ran II-322 Cao, Shehua IV-192 Cao, Weixin III-186 Cao, Weixing I-446, II-479 Cao, Wenqin III-14 Cao, YongSheng II-110 Chang, Ruokui III-106 Chang, Zhongle I-590 Chao, Liu II-425 Che, Zhenhua I-569, II-198 Chen, Aixi IV-89 Chen, Baisong II-525 Chen, Baorui I-250, II-658, IV-134 Chen, Bin III-123 Chen, Bing IV-602 Chen, Di-yi II-205 Chen, Guifen I-312, II-408 Chen, Hong II-61 Chen, Hongjiang IV-215 Chen, Hui IV-268 Chen, Jianhua I-569, II-198

Chen, Jin II-607 Chen, Kelou II-139 Chen, Lairong III-532 Chen, Lidong I-359 Chen, Liming II-505 Chen, Ling I-178 Chen, Liping I-103 Chen, Tian’en I-103 Chen, Yaxiong I-390, III-357 Chen, Yongxing III-92 Chen, Zhaoxia IV-63 Cheng, Jianqun IV-361 Cheng, Jihong III-222 Cheng, Jilin II-283, III-554 Cheng, Youping I-149, I-353 Chu, Changbao IV-237 Ci, Xin IV-345 Cohen, Oded I-630 Cui, Hongguang I-267, I-428 Cui, Weiwei II-674 Cui, Yunpeng I-56, II-573, III-648 Dai, Lili I-267, I-428 Dai, Rong II-148 Dan, Nie III-732 Dang, Yv-mei II-726 Deng, Guang I-304 Deng, Hubin IV-96, IV-376 Deng, Jinfeng II-211 Deng, Lei IV-255 Deng, Li II-339, III-390 Deng, Shangmin I-674 Diao, Haiting III-57 Ding, Chao II-11 Ding, Jianjun III-36 Ding, Li I-437 Ding, Qingfeng IV-279 Ding, Qisheng IV-629, IV-642, IV-650, IV-659 Ding, Wenqin I-456 Ding, XiaoLing IV-345 Dong, Jing I-35, II-561 Dong, Jingui I-576 Dong, Lihong IV-231

736

Author Index

Dong, Qizheng IV-167 Dong, Shiyun IV-231 Dong, Sufen II-365 Dong, Yiwei III-92 Dou, Yantao IV-78 Du, Bin IV-355 Du, Huibin I-487 Du, Jing IV-720 Du, Jun I-155 Du, Mingyi I-681, II-243 Du, Shuyuan I-16 Duan, Qingling IV-691 Duan, Qingwei IV-134 E, Yue

II-110

Fan, Honggang I-35 Fan, Shijuan IV-116 Fan, Xinzhong I-576, I-590 Fang, Hui IV-124 Fang, Junlong IV-616 Feng, Shaoyuan II-473 Feng, Yaoze IV-184 Fu, Bing III-186 Fu, Qiang III-419 Fu, Xueliang I-487, I-526 Fu, Yu II-131 Fu, Zetian IV-672, IV-680 Fu, Zhuo II-525 Gan, Weihua II-400, II-579 Gao, Bingbo II-415 Gao, Gaili III-604 Gao, Haisheng IV-355 Gao, Hongyan II-53 Gao, Lingwang I-594 Gao, Miao I-282 Gao, Rui I-138 Gao, Shi-Ju IV-16 Gao, Xiaoliang III-732 Gao, Yang III-539 Gao, Ying IV-260 Gao, Yun I-600, II-61 Gao, Zhi-Fan IV-410 Ge, Daokuo II-441 Ge, Ningning I-594 Geng, Duanyang II-158 Geng, Duayang II-531 Geng, Xia III-1

Geng, Zhi III-710 Gitelson, Anatoly IV-47 Gong, Bikai IV-602 Gong, Shuipeng IV-616 Gong, Yi III-554 Gu, Jingqiu III-661 Gu, Wenjuan IV-543 Gu, Xiaohe I-296 Gui, Dongwei I-321 Guo, Huiling II-18, II-491 Guo, Mingming I-409, III-327 Guo, Qian I-374, III-222 Guo, Rui II-551, III-357 Guo, Wei II-322 Guo, Yuming I-691 Han, Jinyu III-539 Han, Ping I-282, I-290 Han, Qiang I-48 Han, Xiangbo I-16, I-472, I-717 Hannaway, David B. II-441 He, Bailin IV-63 He, Bei I-138 He, Bin II-18 He, Dongxian III-725, IV-504, IV-610 He, Fen II-102 He, Feng IV-8 He, Jianbin I-35, II-561 He, Junliang I-519 He, Lian II-283 He, Pengju II-573, III-648 He, Qingbo IV-206 He, Renwang II-517 He, Tian III-316, III-375 He, Wenying II-551, III-357 He, Yong IV-124 Hu, Chunxia I-238, II-41 Hu, Haiyan I-41, III-158 Hu, Jianping I-401, I-456, I-555, II-496, III-249 Hu, JinYou I-623, III-656 Hu, Juanxiu IV-504 Hu, Kaiqun III-304, III-483 Hu, Lin III-138 Hu, Ping II-30 Hu, Runwen II-667 Hu, Siquan I-131, IV-71 Hua, Yu II-650 Huang, Caojun II-309 Huang, Chong I-582

Author Index Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang, Huang,

Guanhua I-643, II-185 Han III-572 Kelin IV-89 Lan III-289 Qing I-250, II-658 Sheng II-351 Wen III-598 Wenjiang I-296, III-280 Xiaomao I-25 Yan II-682 Yanguo IV-321 Ying I-210 Yingsa I-401, I-456, II-496 Yinsa III-249 Yuxiang II-351 Zhigang IV-306

Ji, Baoping III-84 Ji, Ronghua III-304, III-483, III-532 Ji, Ying I-138 Jia, Chaojie I-390, III-357 Jia, Guifeng IV-198 Jia, Shaorong III-198 Jia, Song II-700, III-41 Jiang, Haiyan II-479, III-186 Jiang, Huanyu I-729, I-737 Jiang, Lihua I-149, I-353 Jiang, Na II-473 Jiang, Qiuxiang III-419 Jiang, Wencong II-381 Jiang, Xi II-706 Jiang, Xiangang IV-30 Jie, Dengfei II-118 Jin, Dan III-347, III-445 Jin, Tingxiang I-238, II-41 Jinbin, Li I-335 Jinfu, Lu IV-563 Jinlong, Lin I-608 Juanxiu, Hu III-725 Kai, Wang II-425 Kan, Daohong IV-701 Kong, Fanrang IV-206 Kong, Wenwen IV-124 Kuang, Tangqing IV-543 Lai, Zhigang I-508, IV-361 Lei, Jiaqiang I-321 Lei, Wen I-539 Lei, Xiaojun II-479

Li, Li, Li, Li, Li, Li, Li, Li,

737

Baojun II-30 Biao I-590 Changyou I-487, I-526 Chen III-549 Chengyun I-227, I-335 Chunzhi IV-147 Cunjun I-296, III-280 Daoliang III-725, IV-610, IV-629, IV-642, IV-650, IV-659, IV-672, IV-680, IV-701, IV-710, IV-720, IV-727, IV-735, IV-742 Li, Daoxi II-716 Li, Dapeng II-706 Li, Deying IV-474, IV-514 Li, Fanghua II-682 Li, Fengmin II-551 Li, Gailian I-238, II-41 Li, Gang I-250, II-658, IV-134 Li, Guo IV-691 Li, Guoqing III-241 Li, Haifeng I-321 Li, Hengbin III-379 Li, Honghui I-526 Li, Hongjian IV-89 Li, Hongwen IV-720 Li, Hongyi III-212 Li, Hui I-594, II-317, III-304, III-483 Li, Jia wei IV-39 Li, Jianyun IV-474 Li, Jin III-580 Li, Jing II-90, IV-467, IV-528 Li, Jun IV-382, IV-521 Li, Lin I-35, II-309, II-561 Li, Ling I-698 Li, Linyi II-587 Li, Lixin I-508 Li, Manman III-629 Li, Maogang III-20, III-29 Li, Meian I-487 Li, Minghui IV-514 Li, Mingyong I-576 Li, Na IV-480, IV-537 Li, Peiwu I-600, IV-246 Li, Qiaozhen III-92 Li, Qingji III-413, III-440 Li, Qingqing I-16 Li, Shao-Kun IV-16 Li, Shaokun II-90, II-691 Li, Shijuan I-219, I-261, I-476 Li, Shuqin II-351

738

Author Index

Li, Wei II-102 Li, Wenxin III-572 Li, Wenyue III-598 Li, Xianyue I-155 Li, Xiaoqin IV-294 Li, Xiaoyu I-600, II-61, IV-184, IV-246 Li, Xinlei I-409, III-327 Li, Xuemei II-18 Li, Yan II-185 Li, Yanling II-381, II-392 Li, Yaoming II-607 Li, Yi I-594 Li, Ying III-57 Li, Yong IV-221 Li, Yuan III-500, III-539 Li, Yuanzhang I-68 Li, Yuhong I-275, I-711, IV-575 Li, Yunkai I-155 Li, Yuzhong III-92 Li, Zengyuan I-304 Li, Zhigang III-500, III-539 Li, Zhihong II-465, III-563, III-572 Li, Zhimei III-563, III-572 Li, Zhizhong III-704 Li, Zhongqi IV-177 Liang, Jing II-633 Liang, Qing IV-30 Liang, Yong I-547, II-381, II-392, II-700, III-1, III-41, III-390, III-403, III-452 Liang, Yusheng III-57 Liao, Weichuan II-259 Liao, Xinglong I-1, I-532 Liming, Lu IV-563 Lin, Fengtao IV-568 Linker, Raphael I-630 Liu, Baifen IV-260 Liu, Changju IV-184 Liu, Chengliang II-23 Liu, Cuie III-8 Liu, Cuiling II-317 Liu, Ergen IV-1, IV-390 Liu, Fa I-401, I-456, II-496 Liu, Fei IV-124 Liu, Gang I-138, I-409, III-327, III-580 Liu, Guiyuan IV-96 Liu, Haijun II-185 Liu, Hailong II-290 Liu, Hua III-106

Liu, Jianshu IV-494 Liu, Jianting II-706 Liu, Jie I-600, IV-246 Liu, Jingbo II-415 Liu, Jingyu I-68 Liu, Jingyuan II-465 Liu, Jiping II-674 Liu, Juanjuan IV-108 Liu, Jun III-604 Liu, Junming III-629 Liu, Leping IV-167, IV-333 Liu, Li-Bo I-62 Liu, Lin I-227, I-335 Liu, Liyong IV-727, IV-735, IV-742 Liu, Lu I-623 Liu, Min IV-345 Liu, Mingzeng II-30 Liu, Muhua II-434, IV-467, IV-528 Liu, Ping’ an I-508, IV-361 Liu, Pingan IV-306 Liu, Shengping I-476 Liu, Shihong I-56, II-573, III-158, III-179, III-648 Liu, Shuangxi III-379, III-620, IV-710 Liu, Tao IV-427 Liu, Wei I-178, II-18 Liu, Xiaodong IV-376 Liu, Xiaojun I-446 Liu, Xiuping II-30 Liu, Xu III-289 Liu, Xue IV-672 Liu, Xuming III-704 Liu, Yajuan IV-103 Liu, Yan II-441 Liu, Yande II-1, III-613, IV-427 Liu, Yang I-681, II-243 Liu, Yanqi II-30 Liu, Yin III-327 Liu, Ying II-706 Liu, Yongbin IV-206 Liu, Yongxia II-441 Liu, Yu-xiao II-205 Liu, Zhanli I-472, I-717 Liu, Zheng II-177 Liu, Zhengfang IV-89 Liu, Zhengping IV-108 Liu, Zhifang I-87 Liu, Zhimin IV-89 Liu, Zhipeng I-721

Author Index Liu, Zhongqiang I-76, III-46, III-682, III-696 Long, Changjiang I-25, I-195 Long, Yan II-205 Lu, Anxiang I-282, I-563, II-83 Lu, Daoli III-123 Lu, Gang II-11, III-8 Lu, Huishan I-729, I-737 Lu, Jiahua I-569, II-198 Lu, Peng II-11, II-329, III-8 Lu, Quanguo IV-237 Lu, Shaokun III-725, IV-610 Lu, Weiping I-275, I-711, IV-575 Lu, Yan-Li IV-16 Lu, Zhixiong IV-294 Lu, Zhongmin II-357 Luan, RuPeng II-615 Luan, Xin I-590 Luan, Yunxia II-83, II-457 Luo, Chagen IV-368 Luo, Changshou III-638, III-672 Luo, Chunsheng IV-467, IV-528 Luo, Laipeng IV-1 Luo, Qingyao III-710 Luo, Shimin IV-286 Luo, Xiaoling IV-255 Lv, Jiake III-512 Lv, Xin II-290, II-726 Lv, Yongliang II-706 Ma, Daokun IV-629, IV-650, IV-659 Ma, Hailei I-526 Ma, Juncheng IV-680 Ma, Li II-408 Ma, Liang II-538 Ma, Lili IV-616 Ma, Liuyi II-597 Ma, Lizhen III-106 Ma, Xiaoguang II-465 Ma, Xiao-yi II-205 Ma, Xinming I-437, I-614, II-357, III-269 Ma, Xu I-1, I-532 Ma, Yuan IV-333 Ma, Zhihong I-282, II-83, II-234, III-592 Mao, Enrong III-257 Mao, Hanping II-53 Mao, Shuhua III-721 Mei, Weng II-650

739

Men, Weili I-674 Meng, Haili III-598 Meng, Hong III-158 Meng, Qingyi II-473 Meng, Xianxue III-179 Miao, Pengbo IV-528 Min, Shungeng III-592 Ming, Bo II-691 Mingyin, Yao I-608 Muhua, Liu I-608 Naor, Amos I-630 Ning, Dongzhou IV-376 Ouyang, Aiguo

IV-368

Pan, Fangting III-231 Pan, Guiying II-71 Pan, Jiayi III-710 Pan, Juan I-367 Pan, Ligang I-282, I-290, I-563, II-83, II-234, II-457 Pan, Qilong IV-735 Pan, Yuchun II-525 Pang, Siqin IV-78 Pei, Chunmei II-18, II-491 Peng, Bo I-119 Peng, Cheng II-641, III-661 Peng, Lin I-417 Ping, Hua I-290, II-234, II-457 Ping, Jia III-428 Ping, Xuecheng IV-306 Qi, Kun II-61 Qi, Lijun III-304, III-483 Qi, Limeng II-339 Qi, Long I-1 Qiao, Hongbo II-650 Qiao, Xiaojun III-66, III-75 Qiao, Xibo I-576, I-590 Qiao, Zhong III-473 Qin, JiangLin IV-47 Qin, Xiangyang I-563, III-580 Qing, Chang I-335 Qing, Zhaoshen III-84 Qiu, Wanying II-521 Qiu, Xiaobing III-473 Qiu, Ying IV-420 Qiu, Yun II-300, III-113, III-138 Qiulian, Li I-608

740

Author Index

Rao, Guisheng II-339 Rao, Honghui III-613 Ren, Jiwen IV-494 Ren, Rong III-132 Ren, Shumei I-155 Ren, Souhua II-309 Ren, Wentao I-267, I-428 Ren, Yanna II-357, III-269 Rundquist, Donald IV-47 Serranti, Silvia IV-751 Shang, Huaping II-227 Shao, Xiuping I-401, I-456, II-496 Shao-Wen, Li II-425 She, Chundong I-131, IV-71 Shen, Changjun IV-435 Shen, Lifeng IV-720 Shen, Tao IV-30, IV-592 Shen, Zuorui I-594 Shi, Guoqing III-231 Shi, Liang II-124 Shi, Xiaoxia II-264, II-641 Shi, Yan III-20, III-29 Shi, Yinxue III-367 Shi, Yuanyuan I-614 Shi, Yuling III-289 Shi, Zhou II-71 Shu, Xiaoping IV-521 Si, Yongsheng I-138 Song, Mingyu III-106 Song, Qin I-125 Song, Xiaoqiang III-322 Song, Xiaoyu I-296 Song, Yunliang III-123 Song, Zhenghe III-257 Steele, Mark IV-47 Su, Xiaolu I-41 Su, Yuan I-227, I-335 Sui, Xueyan II-691 Sun, Chao III-36 Sun, Chengli IV-8 Sun, Fa-Xiong IV-450 Sun, Guojun I-390, II-551, III-357 Sun, Jiang I-563 Sun, Jinping III-403, III-452 Sun, Jinying II-441 Sun, Kaimeng II-218 Sun, Li III-198 Sun, Ming IV-39 Sun, Nan II-465

Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun, Sun,

Ruizhi III-367 Sufen I-56, III-638, III-672 Suping I-576 Wenbin III-57 Wensheng III-165 Xia I-16 Xiang III-661, IV-583 Xiaoqing II-691 Xudong II-1 Yonghua I-96, I-464 Yongxiang I-547, III-1 Zhiguo I-9 Zhongwei II-329, II-374

Tai, Haijiang IV-642, IV-650 Tan, Feng II-479 Tan, Jinghe I-590 Tan, Jingying III-347, III-445 Tan, Yu-an I-68 Tan, Zongkun IV-47 Tang, Bin IV-89 Tang, Chengwen IV-198 Tang, Liang II-479 Teng, Guanghui III-704 Teng, Guifa II-365 Teng, Yun II-682 Wan, ChangZhao III-222 Wan, Peng I-25, I-195 Wan, Shubo III-146 Wan, ShuJing III-403 Wang, Bai II-682 Wang, Baoqing II-441 Wang, Buyu I-487, I-526 Wang, Changsheng II-30 Wang, Cheng III-66, III-84 Wang, Chun II-345, II-567 Wang, Dong III-592 Wang, Dongqing I-487 Wang, Fang IV-691 Wang, Fang-Yong IV-16 Wang, Fangzhou III-165 Wang, Fei III-198 Wang, Fengxin II-185 Wang, Fuxiang III-563 Wang, Haiguang I-582 Wang, Haiou I-131, III-231, IV-71 Wang, Hongbin I-35, II-561 Wang, Jian I-62, II-415, III-113 Wang, Jianqin I-119

Author Index Wang, Jihua I-282, I-290, I-563, II-83, II-525 Wang, Jing III-249 Wang, Jinhua IV-376 Wang, Jinxing III-379, III-620, IV-710 Wang, Junfeng I-131, IV-71 Wang, Junqiang II-706, IV-592 Wang, Kaili I-643 Wang, Kaiyi I-76, III-46, III-682, III-696 Wang, Ke-Ru IV-16 Wang, Lianzhi IV-629 Wang, Li-jun III-99 Wang, Ling II-290 Wang, Lingyan I-155 Wang, Pu III-491 Wang, Qiang III-269 Wang, Qing III-347, III-445 Wang, Qingchun III-532 Wang, Qiong IV-16 Wang, Ruijuan I-374 Wang, Sangen II-166 Wang, Shengfeng III-428 Wang, Shicong III-473 Wang, Shufeng I-76, III-696 Wang, Shushan III-123 Wang, Shuwen IV-616 Wang, Shuyan I-110 Wang, Susheng I-359 Wang, Wei I-96, I-464, I-600, IV-78, IV-184, IV-246 Wang, Wensheng I-9, I-149, I-203, I-353 Wang, Xi II-567 Wang, Xiangyou I-16, I-472, II-158, II-531 Wang, Xiao IV-467, IV-528 Wang, Xiaojun II-392 Wang, Xiaoli II-623 Wang, Xihua III-36 Wang, Xin I-381 Wang, Xingxing III-20 Wang, Xinzhong II-567 Wang, Xu IV-134 Wang, Xuan III-512 Wang, Yanan III-92 Wang, Yang II-329, II-374 Wang, Yangqiu II-491 Wang, Yanlin IV-514 Wang, Yuanhong III-106 Wang, Yunsheng I-374, II-434, III-222

Wang, Zhaopeng I-576 Wang, Zhenzhi III-46, III-682 Wang, Zhiwei IV-116 Wang, Zhongyi III-289 Wang, Zilong III-419 Wei, Chaofu III-512 Wei, Enzhu III-249 Wei, Lin III-563 Wei, Qingfeng III-638, III-672 Wei, Xinhua II-607 Wei, Xiufang II-441 Wei, Yaoguang IV-642, IV-650 Wei, Yong III-106 Wen, Nannan IV-659 Wu, Chaohui I-275, I-711, IV-575 Wu, Dake II-166 Wu, Dan IV-390 Wu, Ding-Feng I-62, III-113 Wu, Dongsheng IV-382, IV-521 Wu, Hongchao I-576, I-590 Wu, Honggan I-304 Wu, Huarui III-661, IV-583 Wu, Jiajiao III-563 Wu, Jingzhu II-317 Wu, Qingping IV-89 Wu, Qiulan I-547, III-1 Wu, Quan III-198 Wu, Wenbiao III-75 Wu, Xiaoying II-465 Wu, Yali I-691 Wu, Yongchang II-264 Wu, Zhigang III-572 Xi, Junmei IV-237 Xi, Lei I-437, III-269 Xia, Hui I-9 Xia, Junfang II-667 Xia, Lianming II-158, II-531 Xia, Xiaobin IV-8 Xiang, Ling I-290, II-234 Xiang, Quanli I-267, I-428 Xiang, Xinjian I-495 Xiao, Chun-Hua IV-16 Xie, Dandan II-517 Xie, Deti III-512 Xie, Fengyun IV-443 Xie, Nengfu I-149, I-203, I-353 Xie, Rui-Zhi IV-16 Xie, Sanmao IV-314 Xie, Zuqing I-555

741

742

Author Index

Xin, Xiaoping I-250, II-658, IV-134 Xing, Qirong IV-177 Xing, Yajuan I-487 Xing, Zhen IV-435 Xiong, Bangshu IV-8 Xiong, Benhai III-710 Xiong, Guangyao IV-514 Xiong, Guo-Liang IV-410 Xiong, Jinhui I-110 Xiong, Shuping I-614, III-269 Xu, Beili IV-521 Xu, Binshi IV-231 Xu, Chunying III-92 Xu, Fuhou II-597 Xu, Hongmei I-656, I-698 Xu, Jian I-367 Xu, Jianxin III-428 Xu, Li III-473 Xu, Liming II-505 Xu, Ling I-227 Xu, Lunhui IV-321 Xu, Shenghang IV-108 Xu, Shipu II-434, III-222 Xu, Xianfeng IV-63 Xu, XiangBin IV-460, IV-486 Xu, Xiaoli IV-78 Xu, Xin I-437, II-357 Xu, Xingang I-296, III-280 Xu, Yizong I-210 Xue, Fengchang IV-623 Xue, Heru I-502, II-252 Xue, Long IV-403, IV-467, IV-528 Xue, Yan I-219 Xue, Yandong I-155 Yan, Congcong I-472 Yan, Hua III-75 Yan, Jianwu IV-237 Yan, Junyong IV-147 Yan, Lin II-633 Yan, Manfu I-87, I-343 Yan, Qin III-452 Yan, Xiaomei III-620 Yan, YinFa IV-345 Yan, Yuchun IV-134 Yane, Duan II-274 Yang, Chao IV-116 Yang, Deyong I-555, II-496 Yang, Fei III-327 Yang, Feng I-76, III-46, III-682, III-696

Yang, Fengping IV-177, IV-279 Yang, Guixia I-250, II-658, IV-134 Yang, Hao III-280 Yang, Huiying II-185 Yang, Jianhua II-131 Yang, Jing I-227, I-335 Yang, Juan I-374, III-222, III-269 Yang, Le I-569, II-198 Yang, Liang III-710 Yang, Linnan I-417 Yang, Min III-123 Yang, Minghao III-367 Yang, Peiling I-155 Yang, Ping III-413 Yang, Po IV-504 Yang, Sen II-551, III-357 Yang, Shuqin III-428 Yang, Tao III-298, III-322, III-413, III-440 Yang, Wenzhu IV-701, IV-710 Yang, Xiaodong III-280 Yang, Xiaohui II-351 Yang, Xiaorong I-149, I-203, I-353 Yang, Xiaoxia II-700, III-1, III-41 Yang, Xin I-275, I-711, IV-575 Yang, Xiuqing II-18, II-491 Yang, Yafei II-650 Yang, Yang III-316, III-375, IV-398 Yang, Yi I-267, I-428 Yang, Yibo II-517 Yang, Ying I-119 Yang, Yong I-35, I-110, II-561, III-598 Yang, Yongsheng III-464 Yang, Yujian III-146 Yang, Yushu II-322 Yao, Jie II-365 Yao, Shan I-367 Yao, Zhenxuan II-706 Ye, Baoying III-173 Ye, Fan IV-321 Ye, Hairong II-491 Ye, Shengfeng III-592 Yin, Jinju IV-494 Yin, Zhongdong II-538 Ying, Yibin I-729, I-737 Yong, Wang II-650 You-Hua, Zhang II-425 Yu, Feng II-615 Yu, Ligen III-704

Author Index Yuan, Haibo III-20, III-29 Yuan, Jun IV-294 Yuan, Junjing III-341 Yuan, Shengfa I-656, IV-198 Yuan, Tao II-434, II-587 Yuan, Xiaoqing IV-727, IV-742 Yuan, Xue III-304, III-483 Yuchuan, Yang IV-563 Yue, E. I-476 Zang, Yu III-257 Zang, Zhiyuan I-594 Zejian, Lei I-608 Zeng, Fanjiang I-321 Zeng, Qingtian I-203 Zeng, Yanwei II-381, II-392 Zeng, Yi III-241 Zeng, Zhixuan II-525 Zha, Xiaojing IV-382, IV-521 Zhang, Baihua III-525 Zhang, Baohui IV-134 Zhang, Baojun II-441 Zhang, Benhua I-428 Zhang, Changli IV-616 Zhang, Chengming III-390, III-403, III-452 Zhang, Chi I-103 Zhang, Chunlei II-441 Zhang, Chunmei III-29 Zhang, Chunqing III-620 Zhang, Dalei III-403 Zhang, Dongxing III-604 Zhang, Fan II-365 Zhang, Feng III-413 Zhang, Guoliang I-594 Zhang, Haihong I-721, II-118 Zhang, Hailiang II-1 Zhang, Hao I-437, II-357 Zhang, Haokun I-502 Zhang, Hong I-96, I-464, I-539 Zhang, Hongbin I-250, II-658, IV-134 Zhang, Jian I-623, III-341, III-656, IV-474, IV-537 Zhang, Jianhang I-343 Zhang, Jianhua III-304, III-483 Zhang, Jing I-381, I-698 Zhang, Jingjing I-623, III-656 Zhang, Jinheng II-706, IV-592 Zhang, Jishuai II-650 Zhang, Juan II-357

743

Zhang, Jun IV-246 Zhang, Junfeng I-56, II-615, III-638, III-672 Zhang, Junxiong II-102 Zhang, Kai II-23 Zhang, Lei IV-96 Zhang, Li II-357 Zhang, Liang II-716 Zhang, Lihua III-554 Zhang, Limin I-417 Zhang, Lingxian IV-672, IV-680 Zhang, Lingzi IV-691 Zhang, Longlong III-269 Zhang, Mei III-186 Zhang, Min I-569, II-198 Zhang, Mingfei IV-629 Zhang, Na I-367 Zhang, Ping IV-206 Zhang, Qing I-343 Zhang, Qingfeng II-158, II-531 Zhang, Rentian III-554 Zhang, Runqing II-339 Zhang, Shujuan I-721, II-118 Zhang, Shuyuan I-519 Zhang, Tingting II-400, II-579 Zhang, Tongda II-597 Zhang, Wei II-615 Zhang, Xiandi I-76, III-46, III-682, III-696 Zhang, Xiaodong II-53, II-691 Zhang, Xiaojing II-491 Zhang, Xiaolan IV-480, IV-537 Zhang, Xiaoyan III-146 Zhang, Xin III-66, III-75, IV-435, IV-701, IV-710 Zhang, Xu I-304 Zhang, Xuelan I-68 Zhang, Yang III-316, III-375 Zhang, Yanrong II-139 Zhang, Yaoli II-641 Zhang, Yu IV-221 Zhang, Yue I-227 Zhang, Yunhe III-66 Zhang, Yuou I-446 Zhang, Yuxiang II-597 Zhang, Zhen I-359 Zhao, Chunjiang I-76, III-580 Zhao, Dongjie III-289 Zhao, Dongmei III-473 Zhao, Fukuan I-125

744

Author Index

Zhao, Hu II-166 Zhao, Huamin I-721 Zhao, Huizhong I-569, II-198 Zhao, Jianshe III-598 Zhao, JiChun II-615 Zhao, Jingyin I-374, II-434, II-587 Zhao, Jingying III-222 Zhao, Lanying IV-294 Zhao, Liu I-290, II-83, II-234 Zhao, Longzhi IV-474, IV-480, IV-514, IV-537 Zhao, Mingjuan IV-480, IV-537 Zhao, Peng I-614 Zhao, Suolao II-441 Zhao, Ting II-252 Zhao, Wei I-155 Zhao, Wen IV-333 Zhao, Wenlong I-390 Zhao, Wenping I-16 Zhao, Xiaoming I-669 Zhao, Yanqing IV-398 Zhao, Yanru I-721 Zhao, Yujun II-561 Zhao, Yuling II-517 Zhao, Zhiyong II-177 Zheng, Guang I-437 Zheng, Huaiguo I-56 Zheng, Huoguo III-158, III-179, III-648 Zheng, Lihua III-473 Zheng, Meizhu IV-514 Zheng, Wengang III-66, III-75, IV-435 Zheng, Wenxiu III-379 Zheng, Yanxia I-519, II-177, II-473 Zheng, Youfei I-381 Zheng, Yuelan II-607 Zheng, Yujun I-125 Zheng, Zhihong II-23 Zhong, Guangrong III-231 Zhong, Mingdong IV-167 Zhong, Shiquan I-275, I-711, IV-575 Zhong, Zhiyou I-569, II-198 Zhou, Chao III-613, IV-368 Zhou, Dongsheng III-298 Zhou, Ermin II-139 Zhou, Fengqi IV-89

Zhou, Guo-Min I-62, II-300, II-623, III-113, III-138 Zhou, Huamao III-613 Zhou, Huilan IV-555 Zhou, Ji-Hui IV-443, IV-450 Zhou, Lianqing II-71 Zhou, Liying III-672 Zhou, Mingyao I-359 Zhou, Nan III-473 Zhou, Wei IV-246 Zhou, Weihong III-357 Zhou, XinJian IV-486 Zhou, Zexiang I-68 Zhou, Zhisheng II-309 Zhou, Zhu I-600, IV-246 Zhu, Chuanbao II-441 Zhu, Dawei II-441 Zhu, Dazhou I-563, III-84, III-92 Zhu, Dongnan I-526 Zhu, Fengmei IV-355 Zhu, Haiyan III-14 Zhu, Huaji III-661, IV-583 Zhu, Jianhua III-146 Zhu, Jie IV-410 Zhu, Juanuan II-441 Zhu, Qixin IV-279 Zhu, Shiping II-633 Zhu, Wei IV-486 Zhu, Wenquan I-681 Zhu, Yan I-446, II-479, III-186 Zhu, YePing II-110 Zhu, Yeping I-219, I-261, I-476 Zhu, Youyong I-227, I-335 Zhu, Yuwei II-400, II-579 Zhu, Zhenlin II-691 Zhu, Zhongkui IV-206 Zhu, Zhongxiang III-257 Zhuang, Weidong II-345, II-567 Zong, Li I-656, I-698 Zong, Wangyuan I-96, I-464 Zou, Qiang IV-124 Zude, Manuela III-84 Zuo, Changqing II-538 Zuo, Tingting II-131 Zuo, Yanjun I-1, I-532