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

IFIP Advances in Information and Communication Technology

347

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 IV

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 ISBN 978-3-642-18368-3 e-ISBN 978-3-642-18369-0 DOI 10.1007/978-3-642-18369-0 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 IV

A Compression Method of Decision Table Based on Matrix Computation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Laipeng Luo and Ergen Liu

1

A Laplacian of Gaussian-Based Approach for Spot Detection in Two-Dimensional Gel Electrophoresis Images . . . . . . . . . . . . . . . . . . . . . . . . Feng He, Bangshu Xiong, Chengli Sun, and Xiaobin Xia

8

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chun-Hua Xiao, Shao-Kun Li, Ke-Ru Wang, Yan-Li Lu, Jun-Hua Bai, Rui-Zhi Xie, Shi-Ju Gao, Qiong Wang, and Fang-Yong Wang

16

A New Color Information Entropy Retrieval Method for Pathological Cell Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiangang Jiang, Qing Liang, and Tao Shen

30

A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . Jia wei Li and Ming Sun

39

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Visible/Red-Infrared Hyperspectral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . JiangLin Qin, Donald Rundquist, Anatoly Gitelson, Zongkun Tan, and Mark Steele

47

Application of Improved BP Neural Network in Controlling the Constant-Force Grinding Feed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhaoxia Chen, Bailin He, and Xianfeng Xu

63

A Semantic Middleware of Grain Storage Internet . . . . . . . . . . . . . . . . . . . . Siquan Hu, Haiou Wang, Chundong She, and Junfeng Wang

71

AE Feature Analysis on Welding Crack Defects of HG70 Steel Used by Truck Crane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yantao Dou, Xiaoli Xu, Wei Wang, and Siqin Pang

78

An Equilateral Triangle Waveguide Beam Splitter . . . . . . . . . . . . . . . . . . . . Zhimin Liu, Fengqi Zhou, Hongjian Li, Bin Tang, Zhengfang Liu, Qingping Wu, Aixi Chen, and Kelin Huang

89

Analysis and Implementation of Embedded SNMP Agent . . . . . . . . . . . . . Hubin Deng, Guiyuan Liu, and Lei Zhang

96

VIII

Table of Contents – Part IV

Application of Computer Technology in Advanced Material Science and Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yajuan Liu

103

Application of Interferometry in Ultrasonic System for Vibration . . . . . . . Zhengping Liu, Shenghang Xu, and Juanjuan Liu

108

Automatic Control System for Highway Tunnel Lighting . . . . . . . . . . . . . . Shijuan Fan, Chao Yang, and Zhiwei Wang

116

Comparative Study of Distance Discriminant Analysis and Bp Neural Network for Identification of Rapeseed Cultivars Using Visible/Near Infrared Spectra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qiang Zou, Hui Fang, Fei Liu, Wenwen Kong, and Yong He Current Situation and Prospect of Grassland Management Decision Support Systems in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qingwei Duan, Xiaoping Xin, Guixia Yang, Baorui Chen, Hongbin Zhang, Yuchun Yan, Xu Wang, Baohui Zhang, and Gang Li

124

134

Design Method and Implementation of Ternary Logic Optical Calculator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunzhi Li and Junyong Yan

147

Design of Automatic Cutting and Welding Machine for Brake Beam-Axle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leping Liu, Mingdong Zhong, and Qizheng Dong

167

Design of Multifunction Vehicle Bus Controller . . . . . . . . . . . . . . . . . . . . . . Zhongqi Li, Fengping Yang, and Qirong Xing

177

Detection of Soil Total Nitrogen by Vis-SWNIR Spectroscopy . . . . . . . . . Yaoze Feng, Xiaoyu Li, Wei Wang, and Changju Liu

184

Development and Application of Tennis Match Video Retrieval Technology in Multimedia Education . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shehua Cao

192

Fault Diagnosis of Roller Bearing Based on PCA and Multi-class Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Guifeng Jia, Shengfa Yuan, and Chengwen Tang

198

Health Status Identification of Connecting Rod Bearing Based on Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yongbin Liu, Qingbo He, Ping Zhang, Zhongkui Zhu, and Fanrang Kong Investigation of the Methods for Tool Wear On-Line Monitoring during the Cutting Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hongjiang Chen

206

215

Table of Contents – Part IV

IX

Magnetic-Field-Based 3D ETREE Modelling for Multi-Frequency Eddy Current Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yu Zhang and Yong Li

221

Measurement of Self-emitting Magnetic Signals from a Precut Notch of Q235 Steel during Tensile Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lihong Dong, Binshi Xu, and Shiyun Dong

231

Modeling and Performance Analysis of Giant Magnetostrictive Microgripper with Flexure Hinge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qinghua Cao, Quanguo Lu, Junmei Xi, Jianwu Yan, and Changbao Chu Non-destructive Measurement of Sugar Content in Chestnuts Using Near-Infrared Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jie Liu, Xiaoyu Li, Peiwu Li, Wei Wang, Jun Zhang, Wei Zhou, and Zhu Zhou

237

246

Nondestructive Testing Technology and Optimization of On-Service Urea Reactor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoling Luo and Lei Deng

255

Parameters Turning of the Active-Disturbance Rejection Controller Based on RBF Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Baifen Liu and Ying Gao

260

Research of Intelligent Gas Detecting System for Coal Mine . . . . . . . . . . . Hui Chen

268

Research of Subway’s Train Control System Based on TCN . . . . . . . . . . . Qingfeng Ding, Fengping Yang, and Qixin Zhu

279

Shape Detection for Impeller Blades by Non-contact Coordinate Measuring Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shimin Luo

286

Simulation of Road Surface Roughness Based on the Piecewise Fractal Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhixiong Lu, Lanying Zhao, Xiaoqin Li, and Jun Yuan

294

Stress Analysis near the Welding Interface Edges of a QFP Structure under Thermal Loading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhigang Huang, Xuecheng Ping, and Pingan Liu

306

Study of Intelligent Diagnosis System for Mechanism Wear Fault Based on Fuzzy-Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sanmao Xie

314

X

Table of Contents – Part IV

Study on Autonomous Path Planning by Mobile Robot for Road Nondestructive Testing Based on GPS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lunhui Xu, Fan Ye, and Yanguo Huang

321

Study on Imitating Grinding of Two-Dimensional Ultrasonic Vibration Turning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Leping Liu, Wen Zhao, and Yuan Ma

333

Study on Optimal Path Changing Tools in CNC Turret Typing Machine Based on Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Min Liu, XiaoLing Ding, YinFa Yan, and Xin Ci

345

Study on the Problem and Countermeasure of Fruit Production Quality and Safety in Yanshan Mountain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Haisheng Gao, Bin Du, and Fengmei Zhu

355

Calculation and Analysis of Double-Axis Elliptical-Parabolic Compond Flexure Hinge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ping’ an Liu, Jianqun Cheng, and Zhigang Lai

361

Surface Distresses Detection of Pavement Based on Digital Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Aiguo Ouyang, Chagen Luo, and Chao Zhou

368

The Application Research of Neural Network in Embedded Intelligent Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaodong Liu, Dongzhou Ning, Hubin Deng, and Jinhua Wang

376

The Theoretical Analysis of Test Result’s Errors for the Roller Type Automobile Brake Tester . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun Li, Xiaojing Zha, and Dongsheng Wu

382

A Type of Arithmetic Labels about Circulating Ring . . . . . . . . . . . . . . . . . Ergen Liu, Dan Wu, and Kewen Cai

390

Application of CPLD in Pulse Power for EDM . . . . . . . . . . . . . . . . . . . . . . . Yang Yang and Yanqing Zhao

398

Application of IDL and ENVI Redevelopment in Hyperspectral Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Long Xue

403

Design of Integrated Error Compensating System for the Portable Flexible CMMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Qing-Song Cao, Jie Zhu, Zhi-Fan Gao, and Guo-Liang Xiong

410

Detecting and Analyzing System for the Vibration Comfort of Car Seats Based on LabVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Qiu

420

Table of Contents – Part IV

XI

Determination of Pesticide Residues on the Surface of Fruits Using Micro-Raman Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yande Liu and Tao Liu

427

Development of the Meter for Measuring Pork Quality Based on the Electrical Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Zhen Xing, Wengang Zheng, Changjun Shen, and Xin Zhang

435

Experimental Investigation of Influence on Non-destructive Testing by Form of Eddy Current Sensor Probe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengyun Xie and Jihui Zhou

443

Feasibility of Coordinate Measuring System Based on Wire Driven Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ji-Hui Zhou, Qing-Song Cao, Fa-Xiong Sun, and Lan Bi

450

HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XiangBin Xu

460

Nondestructive Measurement of Sugar Content in Navel Orange Based on Vis-NIR Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chunsheng Luo, Long Xue, Muhua Liu, Jing Li, and Xiao Wang

467

Numerical Simulation of Temperature Field in Selective Laser Sintering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jian Zhang, Deying Li, Jianyun Li, and Longzhi Zhao

474

Numerical Simulations of Compression Properties of SiC/Al Co-continuous Composites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mingjuan Zhao, Na Li, Longzhi Zhao, and Xiaolan Zhang

480

Simulation and Optimization in Production Logistics Based on eM-Plant Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XinJian Zhou, XiangBin Xu, and Wei Zhu

486

Simulation of Transient Temperature Field in the Selective Laser Sintering Process of W/Ni Powder Mixture . . . . . . . . . . . . . . . . . . . . . . . . . . Jiwen Ren, Jianshu Liu, and Jinju Yin

494

Study on Plant Nutrition Indicator Using Leaf Spectral Transmittance for Nitrogen Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juanxiu Hu, Dongxian He, and Po Yang

504

Study on the Influence of Non-electrical Parameters on Processing Quality of WEDM-HS and Improvement Measures . . . . . . . . . . . . . . . . . . . Guangyao Xiong, Meizhu Zheng, Deying Li, Longzhi Zhao, Yanlin Wang, and Minghui Li

514

XII

Table of Contents – Part IV

Test Analysis and Theoretical Calculation on Braking Distance of Automobile with ABS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongsheng Wu, Jun Li, Xiaoping Shu, Xiaojing Zha, and Beili Xu The Detection of Early-Maturing Pear’s Effective Acidity Based on Hyperspectral Imaging Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pengbo Miao, Long Xue, Muhua Liu, Jing Li, Xiao Wang, and Chunsheng Luo The Effects of Internal and External Factors on the Mechanical Behavior of the Foam Copper . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Longzhi Zhao, Xiaolan Zhang, Na Li, Mingjuan Zhao, and Jian Zhang

521

528

537

Optimum Design of Runner System for Router Cover Based on Mold Flow Analysis Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tangqing Kuang and Wenjuan Gu

543

Design of Tread Flange Injection Mold Based on Pro/E . . . . . . . . . . . . . . . Huilan Zhou

555

Study on the Online Control System to Prevent Drunk Driving Based on Photoelectric Detection Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lu Liming, Yang Yuchuan, and Lu Jinfu

563

The Design and Simulation of Electro-Hydraulic Velocity Control System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengtao Lin

568

Application of Background Information Database in Trend Change of Agricultural Land Area of Guangxi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Yang, Shiquan Zhong, Yuhong Li, Weiping Lu, and Chaohui Wu

575

Reasons of the Incremental Information in the Updating Spatial Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Huaji Zhu, Huarui Wu, and Xiang Sun

583

Research on Non-point Source Pollution Based on Spatial Information Technology: A Case Study in Qingdao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tao Shen, Jinheng Zhang, and Junqiang Wang

592

The Regulation Analysis of Low-Carbon Orientation for China Land Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bikai Gong and Bing Chen

602

A CDMA-Based Soil-Quality Monitoring System for Mineland Reclamation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dongxian He, Daoliang Li, Jie Bao, and Shaokun Lu

610

Table of Contents – Part IV

Design and Implementation of a Low-Power ZigBee Wireless Temperature Humidity Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shuipeng Gong, Changli Zhang, Lili Ma, Junlong Fang, and Shuwen Wang Land Evaluation Supported by MDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fengchang Xue Design and Development of Water Quality Monitoring System Based on Wireless Sensor Network in Aquaculture . . . . . . . . . . . . . . . . . . . . . . . . . Mingfei Zhang, Daoliang Li, Lianzhi Wang, Daokun Ma, and Qisheng Ding

XIII

616

623

629

Design of an Intelligent PH Sensor for Aquaculture Industry . . . . . . . . . . . Haijiang Tai, Qisheng Ding, Daoliang Li, and Yaoguang Wei

642

A Simple Temperature Compensation Method for Turbidity Sensor . . . . . Haijiang Tai, Daoliang Li, Yaoguang Wei, Daokun Ma, and Qisheng Ding

650

A Wireless Intelligent Valve Controller for Agriculture Integrated Irrigation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Nannan Wen, Daoliang Li, Daokun Ma, and Qisheng Ding

659

Evaluation of the Rural Informatization Level in Central China Based on Catastrophe Progression Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingxian Zhang, Xue Liu, Zetian Fu, and Daoliang Li

672

GIS-Based Evaluation on the Eco-Demonstration Construction in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lingxian Zhang, Juncheng Ma, Daoliang Li, and Zetian Fu

680

Modeling and Analysis of Pollution-Free Agricultural Regulatory Based on Petri-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fang Wang, Qingling Duan, Lingzi Zhang, and Guo Li

691

An Online Image Segmentation Method for Foreign Fiber Detection in Lint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Daohong Kan, Daoliang Li, Wenzhu Yang, and Xin Zhang

701

An Efficient Iterative Thresholding Algorithms for Color Images of Cotton Foreign Fibers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin Zhang, Daoliang Li, Wenzhu Yang, Jinxing Wang, and Shuangxi Liu Application of Grey Prediction Model in Rural Informatization Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jing Du, Daoliang Li, Hongwen Li, and Lifeng Shen

710

720

XIV

Table of Contents – Part IV

Study on Evaluation Method for Chinese Agricultural Informatization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xiaoqing Yuan, Liyong Liu, and Daoliang Li

727

Research on Calculation Method for Agricultural Informatization Contribution Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liyong Liu, Qilong Pan, and Daoliang Li

735

An Empirical Research on the Evaluation Index Regarding the Service Quality of Agricultural Information Websites in China . . . . . . . . . . . . . . . . Liyong Liu, Xiaoqing Yuan, and Daoliang Li

742

Hyperspectral Sensing Techniques Applied to Bio-masses Characterization: The Olive Husk Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Giuseppe Bonifazi and Silvia Serranti

751

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

765

A Compression Method of Decision Table Based on Matrix Computation Laipeng Luo and Ergen Liu School of Basic Sciences, East China Jiaotong University, Nanchang, P.R. China [email protected]

Abstract. A new algorithm of attribute reduction based on boolean matrix computation is proposed in this paper. The method compresses the valid information stored in table into a binary tree, at the same time deleting the invalid information and sharing a branch about the same prefix information. Some relative concepts such as local core attributes, local attribute reduction and global core attributes, global attribute reduction are introduced. The conclusions that the global core set is the union of all local core sets and the global attribute reduction sets are the union of respective local attribute reduction sets are proved. The attribute reduction steps of the algorithm are presented. At last, The correctness and effectiveness of the new algorithm are also shown in experiment and in an example. Keywords: Rough Set, equivalence matrix, attribute reduction, information compression.

1 Introduction Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s[1,2], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, deci-sion analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition[3,4,5]. Attribute reduction is one of the main applications of Rough set. The general problem of finding all reductions are NP-hard, thus it is important for attribute reduction of Rough set to design algorithms with lower price and investigate new computation method. A matrix computation method of Rough set was proposed by the author in[6,7]for information system. A matrix was seen as an internal representation of equivalence relations. By defining the operation of the equivalence matrix, matrices are applied to define dependencies between two subsets of attributes, significance of an attribute ect. The approach presents a series of algorithms and their time complexity of attribute reduction. However, there are still several problems to be solved for the method: (1)The number of objects has great influence on time complexity of these algorithms; (2)These algorithms need too many computations of matrices; (3)The method only discusses matrix computation for information system; (4) How to apply the method to variable precision Rough set model. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 1–7, 2011. © IFIP International Federation for Information Processing 2011

2

L. Luo and E. Liu

With the above-mentioned motivation, in another paper[8],aiming at the problem that how to apply the matrix computation to variable precision Rough set model,we have proposed a measure and computation approach based on matrix about studying Rough set theory. In this paper, we will provide a new insight into attributes reduction in decision table. A compression method of decision table based on matrix computation is proposed.We shall prove the feasibility of the compression method in theory and show its effectiveness in experiment and in an agriculture example.

2 Equivalence Matrix of Decision Table Definition 2.1. Let U be a non-empty finite set of objects and R be equivalence relation on U, We denote the partition gived by the R as follows: U/R= {X1, X 2 ,...X n} . Definition 2.2. Let R be equivalence relation on U, then R is expressed in terms of a binary n×n requivalence matrix MR=[mij]n×n where n= U , mij

⎧1 xi Rx j =⎨ . or ⎩0

Definition 2.3. Let P=(pij)n×n,Q=(qij)n×n be two binary n×n matrices. The intersection P∩Q of matrices P and Q is defined as follows:P∩Q=[tij]n×n,where tij=min{pij,qij}. m

Definition 2.4. Let R={r1,r2,…,rm},then M R = I M ri . i =1

Definition 2.5. LetC,D be equivalence relation and MC =(cij)n×n, MD =(dij)n×n be their matrices respectively. If for arbitrary positive integer i,j, cij≤dij,then M C ≤ M D .

∪

Definition 2.6. Let S=(U,R=C D,V,f) be a decision table, where C is condition attributes and D is decision attributes, Then S is consistent if and only if M C ≤ M D . Definition 2.7. Let S=(U,R=C

∈

∪ D,V,f, M ≤ M ) be a decision table, Given an C

D

attribute a C, then attribute a is nonsignificant in C if M{C−a} ≤ MD .

∪

Definition 2.8. Let S=(U,R=C D,V,f, MC ≤ MD ) be a decision table. The set of all attributes

C ′′ ⊆ C which are significant in S is called the core set of C.

Definition 2.9. Let S=(U,R=C

∪D,V,f, M ≤ M ) be a decision table. A subset T ′ of C C

D

is said to be a attribute reduction of C if and only if

T ′′ ⊂ T ′, then : MT′ > MD .

T ′ satisfies: (1) MT′ ≤ MD ; (2)if

3 Theory Analysis of Matrix Computation Put [R]ij express the element in row i and in column j of MR, where R={r1,r2,…,rm}. Obviously, [R]ij has the following properties:

A Compression Method of Decision Table Based on Matrix Computation

∧ ∧ ∧

3

(1) [R]ij =[r1]ij [r2]ij … [rm]ij(i=1,2…n,j=1,2…n);(2)if [R]ij =1,then for arbitrary rk R, [rk]ij =1;(3) if [R]ij =0,then there at least exist an attribute rk R,such that [rk]ij =0.Referring to properties,we immediately derive the following facts:

∈

Theorem 3.1. Let S=(U,R=C

∈

∪D,V,f, M ≤ M ) be a decision table. A subset T ′ of C C

D

is said to be a attribute reduction if and only if for any [D]ij=0 in MD, [ T ′ ]ij satisfies:(1) [ T ′ ]ij=0;(2)There no exist T ′′ ⊆ T ′, such that [ T ′′ ]ij=0.

∪ D,V,f,

MC ≤ MD ) be a decision table where C={c1,c2,…,cm}.c C is core attribute if and only if there at least exist positive integer i,j,( i=1,2…n,j=1,2…n) such that [D]ij=0, [c]ij=0,but for any b C-c, [b]ij=1. Theorem 3.2. Let S=(U,R=C

∈

∈

∈

∈

Proof. Let c C be core attribute.If there exist some attribute b C(c≠b),such that [b]ij=0,for any[D]ij=0 ,[C]ij=min {[c1]ij,[c2]ij,…[cm]ij}=0, then after deleting attribute b in C,we have M{C−c} ≤ MD .Thus there exist attribute set C ′ ⊆ {C-c},such that C ′ is attribute reduction of S which contradict that c is core attribute in S. Conversely, if there exist positive integer i,j,(i=1,2…n,j=1,2…n), such that [D]ij=0, [c]ij=0,and [b]ij=1 for every b C-c,then after deleting attribute b in C,we have [C]ij=0≠[C-c]ij=1.Thus c is core attribute in S by theorem 3.1.

∈

Definition 3.1. Let S=(U,R=C

∪D,V,f, M ≤ M ) be a decision table. If there exist C

D

positive integer i,j, such that attribute c satisfies [c]ij=0, [C-c]ij=1 when [D]ij=0, [C]ij=0,then attribute c is called local core attribute of decision table S.

∪

Theorem 3.3. If c1,c2…,ck be all local core attribute of S=(U,R=C D,V,f, M C ≤ M D ), then core attribute set C ′ of decision table S is

k

Uc

i =1

i

.That is, C ′ =

k

Uc . i =1

i

Definition 3.2. Core attribute set C ′ of decision table S is called global core attributes.

∪D,V,f, M ≤ M ) be a decision table and C′ be core

Definition 3.3. Let S=(U,R=C

∈

C

D

attribute set.If ak C- C ′ satisfies that there exist positive integer i,j,such that [ C ′ ]ij=1, [C]ij=0 and [ak]ij=0,then attribute set C ′ {ak} is called a local attribute reduction of decision table S. Obviously, local attribute reduction derived by [C]ij=0 can has not only one. All local attribute reduction derived by [C]ij=0 is called a local attribute reduction set.

∪

Definition 3.4. Attribute reduction set T ⊂C of decision table is called global attribute reduction. Theorem 3.4. Let B1,B2…,Bk be all local attribute reduction sets of decision table S=(U,R=C D,V,f, MC ≤MD ),where Bi ={Ai1, Ai2,L, Aiki }(1≤ i ≤ k) and Aij (1 ≤ j ≤ ik ) is

∪

a local reduction.If Tt is attribute reduction,then Tt satisfies:(1) If for arbitrary positive

4

L. Luo and E. Liu k

integer i,j, Bi∩Bj=φ,then Bi∩Bjφ,then

Tt = UAip ,1 ≤ p ≤ ik ; (2) If there exist positive integer i,j, i=1

Tt = UAip ,1 ≤ p ≤ ik where for any Aim,Ajn

∈T , A t

im,Ajn

must satisfy

Aim ∉ B j ٛor Ajn ∉ Bi . k

Proof. (1) Put

Tt = UAip and let C ′ be core attribute set.There at least exist an i =1

b ∈ Tt for any [D]ij=0,[C]ij=0,such that [b]ij=0.That is [ Tt ]ij=0. On the other hand,for arbitrary B⊂Tt , Suppose B ∪ ck = Tt and C′ ∪ ck ∈ Bm By definition3.2 there exist positive integer p,q,such that [C′]pq =1,[ck ]pq =0 While for arbitrary i,j, if attribute

.

。

Bi∩Bj=φ, then [B]pq =[Tt −ck ] ≠0 By theorem 3.1,attribute set B is not attribute reduction of system S. k

(2) If there exist positive integer i,j,Bi∩Bj≠φ, then

Tt = UAip ,1 ≤ p ≤ ik is not the i=1

optimal attribute reduction of system S.It is fact that if Bi∩Bj = A′ and

′ A′ ≠ Ajm ∈ B j ,then A′ ∪Ajm ⊆Tt is not the optimal attribute reduction. Hence, by above (1), if for any

Aim ∈ Bi , A jn ∈ Bj and Aim, Ajn ∈Tt , Aim,Ajn satify

Aim ∉ Bj ٛor A jn ∉ B i then Tt = UAip,1≤ p ≤ ik is the optimal attribute reduction.

4 Compression of Decision Table Because equivalence matrices of attribute set are symmetric, in practical application, we pay attentin to the upper-triangle above the diagonal or the lower- triangle below the diagonal. Let C={c1, c2,…, cr} be condition attribute set and D be decision attribute set. For arbitrary positive integer i,j, we obtain: [C]ij =[c1]ij [c2]ij … [cr]ij.By theorem 3.1,in practical application, we only devote our attention to [D]ij = 0 and [C]ij = 0 in the upper-triangle above the diagonal or the lower- triangle below the diagonal. In this paper,we compress information of [D]ij = 0 and [C]ij = 0 in the equivalence matrix into a binary tree where [c1]ij,[c2]ij,… [cr]ij are orderly arranged and nodes of the tree. Please refer to below for details. First, create the root of the tree, labeled with “null”. Scan the elements of MD a time and find all positive integer i,j which satisfy [D]ij = 0.The corresponding elements of each equivalence relation matrix of condition attributes orderly sorted lead to the construction of the first branch of the tree with r nodes where [c1]ij is linked as a child of the root, [c2]ij is linked to [c1]ij. The rest may be deduced by analogy.

∧ ∧ ∧

∧

A Compression Method of Decision Table Based on Matrix Computation

5

∧

The second [c1]mn,[c2]mn,… [cr]mnwould result in a branch where [c1]mn is linked as a child of the root, [c2]mn is linked to [c1]mn.the rest may be deduced by analogy. Howeve, this branch would share an existing path with other branchs if along the root node, some branch has the common prefix. For example, if [c1]ij=[c1]mn, [c2]ij=[c2]mn, [c3]ij≠[c3]mn, then the first two nodes of the branch which contains [c1]ij, [c2]ij … [cr]ij is the same as the branch which contains[c1]mn, [c2]mn … [cr]mn.The rest branches may be constructed by analogy. By theorem 3.2, definition 3.2, all local core attributes and all local attribute reducion are derived from these branches. By theorem 3.3,3.4, we get global core attributes and global attribute reduction.

∧ ∧

∧ ∧

5 Description of Attribute Reduction Algorithm Let S=(U,R=C

∪D,V,f, M

C

≤ M D ) be a decision table.

Step1: Compute the equivalence matrices of decision attribute set and each of condition attributes ,and arrage the equivalence matrices of each of condition attributes in ordor; Step2: According to [D]ij = 0,compress [c1]ij,[c2]ij,… [cr]ij into a binary tree where [ck]ij,(k=1,2,…r)is node of the tree. Step3: Scan the tree and find the only zero value node in every branch.We get local core of every branch. Core set of system S is union of all local core. Step4: Prune the branch that includes local core and at the same time, retain shareable prefix part. Step5: Travel every branch of binary tree pruned and find local attribute reduction sets of all branchs

∧

Step6: Compute attribute reduction of system S according to {Tt Tt =

UA

ip

,1 ≤ p ≤ ik }

6 Algorithm Analysis The time complexity of algorithm in [7] for finding all core attributes is 2

2

C

2

o( C U ) ,and for finding all attribute reduction is o(2 C U ) . The time complexity of algorithm in this paper for finding all core attributes is at most C +1

C +1

o(2 ) ,and for finding all attribute reduction is at most o(2×2 ) . In general, U >> C ,hence,the method presented in this paper has an advantage over the method in [7]. Next, to compare the two methods, We made the relevant experiments on monks datasets in UCI database. The datasets have one decision attribute, six conditon attributes and 423 records. We did four experiments with the first 100,150,300,423 recordes of standard datasets monks datas. The experiment environment is Petium4 2.1GMHZ,RAM512M, windows XP. The results are as follows:

6

L. Luo and E. Liu Table 1. Run time of two algorithms

5XQ WLPH



after com pression

 

beforer com pression

  







'DWD VHW

The chart shows that the method presented in this paper is efficient and scalable for finding core attributes set and attribute reduction sets, and is faser than the method in [7].

7 An Application Example in Agriculture The following is knowledge representation system of cotton diseases. The condition attributes are a-“diseased spot color”, b-“disease site”,c-“disease shape”,d-“feature” and the decision attribute is e-“the type of disease”. {1,2,3} represent the different value of each attribute. Table 2. Knowledge representation system

According to the method proposed in this paper,we can obtain that the core attribute is {a} and the attribute reduction set are {a,b} and {a,c,d}.The conclusion is the same as the other method.That is to say that diseased spot is chief factor to judge the type of disease and diseased spot color, disease site or diseased spot color, disease shape, feature may judge exactly the type of disease.

8 Conclusion In this paper, We further discuss the approach of matrix computation about Rough set and applied it to dicision table. In theory, we have proved the relation between the

A Compression Method of Decision Table Based on Matrix Computation

7

elements of equivalence matrice and core attributes,attribute reduction. At the same time, we suggest an attribute reduction algorithm based on a storage structure of binary tree which can compress the invalid and the same prefix information. The algorithm designed is lower price. We also find that by changing the order of condition attributes sorted, the algorithm is more efficient.

References [1] Pawlak, Z.: Rough Sets. International Jounal of Information and Computer Science 11(5), 341–356 (1982) [2] Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about data. Kluwer Academic Publishers, Dordrecht (1991) [3] Wang, G.Y., Hu, F., Huang, H., Wu, Y.: A granular computing model based on tolerance relation. The Journal of China Universities of Post s and Telecommunications 12(3), 86–90 (2005) [4] Greco, S., Wojna, A.G., Slowinski, R.: Fuzzy rough sets and multiple-premise gradualdecision rules. International Journal of Approximate Reasoning 41(2), 179–211 (2006) [5] Lin, T.Y., Yin, P.: Heuristically fast finding of the shortest reducts. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 465–470. Springer, Heidelberg (2004) [6] Guan, J.W., Bell, D.A.: Matrix computational method for information systems. Artificial Intelligence 105, 77–103 (1998) [7] Guan, J.W., Bell, D.A., Guan, Z.: Matrix computation for information systems. Information Sciences 131, 129–156 (2001) [8] Luo, L., Liu, E., Yi, C.: Matrix approach to the study of rough set theory. Systems Engineering and Electrionics 31(4), 859–862 (2009) (in chinese)

A Laplacian of Gaussian-Based Approach for Spot Detection in Two-Dimensional Gel Electrophoresis Images Feng He, Bangshu Xiong, Chengli Sun, and Xiaobin Xia Key Laboratory of Nondestructive Test of Ministry of Education, Nanchang Hangkong University, 330063 Nanchang, P.R. China [email protected], [email protected]

Abstract. Two-dimension gel electrophoresis (2-DE) is a proteomic technique that allows the analysis of protein profiles expressed in a given cell, tissue or biological system at a given time. The 2-DE images depict protein as spots of various intensities and sizes. Due to the presence of noise, the inhomogeneous background, and the overlap between the spots in 2-DE image, the protein spot detection is not a straightforward process. In this paper, we present an improved protein spot detection approach, which is based on Laplacian of Gaussian algorithm, and we extract the regional maxima by morphological grayscale reconstruction algorithm, which can reduce the impact of noisy and background in spot detection. Experiments on real 2-DE images show that the proposed approach is more reliable, precise and less sensitive to noise than the traditional Laplacian of Gaussian algorithm and it offers a good performance in our gel image analysis software. Keywords: Two-dimensional gel electrophoresis, Spot detection, Laplacian of Gaussian, Morphological grayscale reconstruction.

1 Introduction Proteomic research deals with the systematic analysis of protein profiles expressed in a given cell, tissue or biological system at a given time. In this field, two-dimensional gel electrophoresis (2-DE) is a well-established and widely used technique to separate proteins extracted from sample for identification and analysis of differential expression, according to their isoelectric points and molecular weight [1]. The result of that process is many dark spots on the gel, and each spot represents a protein or a group of proteins. The two-dimensional gel electrophoresis (2-DE) images show the expression levels of several hundreds of proteins where each protein is represented as a spot of grey level values [2]. In order to extract protein spots, image processing techniques can help us to analyze proteins further. Each spot can be characterized by its location and other information, such as area, volume, intensity, etc. Due to the presence of noise, the inhomogeneous background, and the overlap between the spots in 2-DE image, the protein spot detection is not a straightforward process. D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 8–15, 2011. © IFIP International Federation for Information Processing 2011

A Laplacian of Gaussian-Based Approach for Spot Detection in 2-DE Images

9

A variety of software packages have been developed for protein spot detection [3]. Many of these packages implement image segmentation methods based on edge detection algorithms such as Laplacian filtering, in conjunction with smoothing operators. However, if a 2-DE image contains artifacts and noise, such as cracks in the gel surface, fingerprints, dust and other pollutions, they will lead to spurious spot detection [4]. The watershed transformation algorithm has also been a popular choice for 2-DE image segmentation [3] [5]. State of the art approaches to spot detection by image segmentation include geometric algorithms [6], parametric spot models [3] and the pixel value collection method [2] [7]. In this article, we introduce an improved protein spot detection approach that is based on Laplacian of Gaussian algorithm, in conjunction with morphological grayscale reconstruction to extract the regional maxima in 2-DE images. The proposed approach extracts the regional maxima of the Gaussian-smoothed gel image by morphological grayscale reconstruction algorithm [8], and uses the second derivative (laplacian) and direction of the gaussian-smoothed gel image as well as neighborhood connectivity properties in determining spot extents. Relative to the traditional Laplacian of Gaussian algorithm, our approach can reduce the impact of noise and avoid the spurious spot detection by using morphological grayscale reconstruction algorithm to restrict regional maxima, and the results are more reliable and precise. The rest of this paper is organized as follows: Section 2 describes the spot detection approach which has been applying in our soft, Section 3 presents the experimental results, and Section 4 presents conclusions and the future work.

(a)

(b)

Fig. 1. (a) Original Image, (b) Inverted Image

2 Algorithm In the following description it is assumed that the original image shows in Fig. 1a is inverted, that is, the image background is dark, and the spots appear as light peaks rising from the background in Fig. 1b. The proposed approach to protein spot detection consists of the following steps: a) Smooth the original gel image; b) Extract the regional maxima of the smoothed image by grayscale reconstruction algorithm;

10

F. He et al.

c) Compute the Laplacian of the smoothed image and define central core regions where the Laplacian pixels are negative in both x and y directions; d) Extract all of spot regions; e) Estimate background density and normalize the valid spot quantifications. 2.1 Image Smoothing Operations During the detection process, the gel images are normally very noisy, so we must implement smoothing filter to reduce high frequency noise in 2-DE image at first. If the image is not sufficiently smoothed, it will find the edges of the spots incorrectly and generate spurious regional maxima. In the proposed approach, we use a Gaussian low-pass filter with a window (e.g. 3x3, 5x5, 7x7, etc) to smooth the inverted image. 2.2 Regional Maxima Extraction Grayscale reconstruction is a very useful operator provided by mathematical morphology. The regional maxima and minima are important features of images; they often represent the corresponding image goal: the regional maxima correspond to the light goal, and the region minima correspond to the dark goal. Grayscale reconstruction turns out to provide a very efficient method to extract regional maxima and minima from grayscale images. Furthermore, the technique extends to the determination of “maximal structures” called h-domes [8]. The h-dome transformation is illustrated on Fig. 2. The h-dome transformation extracts light structures without involving any size or shape criterion. The only parameter (h) is related to the height of these structures. This characteristic is of interest for complex segmentation problems. The h-dome image Dh(I) of the h-domes of a grayscale image I is given by Dh(I)=I-RIδ(I-H) . where

RIδ(I-H)

(1)

is the dilated reconstruction of I from I-h.

Fig. 2. Determination of the h-domes of grayscale image I

In the proposed approach, we can execute h-dome transformation to extract light maximal structures of the smoothed image. It can restrain all maxima, the depth of which are less than or equal to the parameter (h), and reduce the impact of noisy in spot detection.

A Laplacian of Gaussian-Based Approach for Spot Detection in 2-DE Images

11

2.3 Image Laplacian Operations 2-DE images show the expression levels of several hundreds of proteins where each protein is represented as a spot of grey level values. Under optimal density within the area of the spot appears as a monotonically increasing function as illustrated in Fig. 3a. The Laplacian or second derivative of this function is shown in Fig. 3b. We define the central core region of negative values for digital approximations to both partial second derivatives, with respect to x and y directions, as the central core region. The region on the outside of the central core is propagated until it reaches the extent of the positive peaks of the side lobes. This propagated central core region, computed in two dimensions, is then effectively used as a mask for quantitating that spot [5]. This is the reason we want to use the Laplacian for helping to analyze the image.

(a)

(b)

Fig. 3. (a) Cross section of the ideal spot, (b) Laplacian of this ideal spot cross section

After acquired the smoothed image, it is used when computing the digital approximation of the Laplacian of this image. We store the Laplacian direction and magnitude values in two additional images. All laplacian direction image pixels are set to 1, if the Laplacian values are negative in both x and y, otherwise they are set to 0. This directional image defines the initial central core regions of a gel. The central core regions are propagated to adjacent pixels until they reach the maximum value of the Laplacian magnitude image. These final regions are called the propagated central core region and, after some corrections, define the extent of the spot to be quantitated. The next step describes the extraction of each spot region in the proposed approach. 2.4 Spot Region Extraction After the image laplacian operations, the next step is to extract all of the spot regions in sequential raster search of the central core image. The central core regions are propagated to adjacent pixels until they reach the maximum value of the Laplacian magnitude image. These final regions are called the propagated central core region and, after some minor corrections, define the extent of the spot to be quantitated. The steps in finding the final propagated central core for the current spot are enumerated as follows. This algorithm is iterated for each spot as it traverses down the image in a raster pattern. Step1. Find the central core for the current spot in the Laplacian direction image, where the central core regions are set to 1 and the other set to 0. Given a new spot pixel to find all (x, y) pairs that are 4-neighbor connected to this spot with central core pixel, and save this pairs in a list.

12

F. He et al.

Step2. If the central core size is less than the threshold which is the minimum of central core size, then delete the spot, and set that region to 0 in the central core image. Step3. There are situations where some spots are merged into single spots. If the central core size is greater than the threshold which is the minimum to split, then try to split the spot into several spots. Step4. Try to find saturated spots, if the (max Density of the spot)/ (max Density in the image) is greater than percent threshold of the darkest pixel, then try to fill holes in the saturated spot with central core pixels. Step5. Remove concavities which with 0 values between central core pixels with central core value in the current central core spot. Step6. Remove interior central core pixels to speed up the subsequent steps and obtain the edge of the central core. This is useful for segmenting very large spots when using very high pixel resolution. Step7. Propagate the central core to a propagated central core by looking for the maxima away from the center of the spot in all directions. This propagation is terminated by various conditions including running into another spot, noise, etc. If the image is not adequately smoothed, this step will not work very well. Step8. Optimize the propagated central core region. We can fill holes in the propagated central core such that central pixels with 0 values are filled with propagated central core value. Step9. Delete the spurious propagated central core. If the propagated central core region does not contain a regional maximum, so it is a spurious propagated central core, and we could delete it. Step10. Finally, compute the spot features using data from the original image and save the features for this spot in a list of all spots. The spot features include density weighted centroid, standard deviation and covariance spot size, density, area and volume. 2.5 Background Density Estimation and Spot Quantification After all spots are initially segmented, it then performs background correction and normalization on the quantifications. If the background appears relatively uniform, we have hound subtracting global minimum intensity for the gel works sufficiently well. However, the background appears to be spatially varying, we use a smoothing low pass filter to estimate the background [9]. First a rest of image is computed as the original image less the segmented spots with the spots having density value 0. Then the filter is computed over the entire image by moving an averaging window (e.g. 32x32) over the image in a raster, 1 pixel at a time where the mean density is computing in the averaging window at each point not including the 0 values. This background image is used to estimate the background for each spot and correct the spot density. To normalize, we divide each spot intensity on a given gel by the mean spot intensity for that gel, and save the spot features in a list, including location, area, volume, normalized intensity, etc. The researchers can use these spot features to do next analysis, such as spot matching.

A Laplacian of Gaussian-Based Approach for Spot Detection in 2-DE Images

13

3 Experimental Results In this section, the results for detection the protein spots in a gel image by those methods mentioned in Section 2 are presented. In our approach, we firstly inverted the original image, that is, the image background is dark, and the spots appear as light peaks rising from the background. We smoothed the inverted image by Gaussian low-pass filter, if the image is not sufficiently smoothed, it will find the edges of the spots incorrectly and generate spurious regional maxima. Fig. 4a shows the regional maxima labeled by red color. Fig. 4b and Fig. 4c show the Laplacian magnitude image and the directional image which defines the initial central core regions of a gel. The central core regions are propagated to adjacent pixels until they reach the maximum value of the Laplacian magnitude image. These final regions are called the propagated central core region and, after some minor corrections, we can get the optimized propagated central core region in Fig. 4d, is then effectively used as a mask for quantitating that spot, and the background image in Fig. 4e. Finally, we can get the final result as Fig. 4f, described the spot contour by red color.

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 4. The process of the protein spots detection with the improved Laplacian of Gaussian approach: (a) regional maxima on original image, (b) central core regions, (c) laplacian magnitude image, (d) final propagated central core regions that are optimized, (e) background image, (f) final result.

14

F. He et al.

As shown in Fig. 4f, we detected 374 protein spots, almost protein spots can be detected and their contours are very clear based on the proposed approach. Fig. 5 shows the result of the Laplacian of Gaussian algorithm, which detected 506 protein spots without morphological grayscale reconstruction algorithm to restrict regional maxima, however, affected by the stripe noise and artifacts, some of them are the spurious spots. On the other hand, the proposed approach is less sensitive to noisy.

Fig. 5. The final result of traditional Laplacian of Gaussian algorithm

4 Conclusions We present an improved protein spot detection approach which can effective detects and quantifies the protein spots in 2-DE images. The spot detection approach applies morphological grayscale reconstruction algorithm to restrict regional maxima, and the Laplacian of Gaussian algorithm to detect spot regions. The 2-DE image often contains artifacts and noise, and they will lead to spurious spot detection. The morphological grayscale reconstruction algorithm can restrict those spurious regional maxima very well, and reduce the impact of noisy in spot detection. The experimental results show that the proposed approach is more reliable, precise and less sensitive to noise than the traditional Laplacian of Gaussian algorithm and it offers a good performance in our gel image analysis software. Future works include further experimentation, optimization and parallelization of the proposed approach, and its integration in a complete user-friendly software application. Also variation of the proposed approach will be used to detect all spots in heavily polluted 2-DE images. Acknowledgments. This work was supported by Postgraduate Innovation Fund of Jiangxi Province (YC09A112), Postgraduate Innovation Fund of Nanchang Hangkong University (YC2009008), Scientific Research Fund of Jiangxi Provincial Education Department (GJJ09183) and Jiangxi Nature Science Fund (No.2008GZS0032).

References 1. Safavi, H., Correa, N.: Independent Component Analysis of 2-D Electrophoresis Gels. Electrophoresis 29, 4017–4026 (2008) 2. Peer, P., Corzo, L.G.: Local Pixel Value Collection Algorithm for Spot Segmentation in Two-Dimensional Gel Electrophoresis Research. Comparative and Functional Genomics, 1–9 (2007)

A Laplacian of Gaussian-Based Approach for Spot Detection in 2-DE Images

15

3. Berth, M., Moser, F.M.: The State of the Art in the Analysis of Two-Dimensional Gel Electrophoresis Images. Appl. Microbiol. Biotechnol. 76, 1223–1243 (2007) 4. Rye, M.B., Alsberg, B.k.: A Multivariate Spot Filtering Model for Two-Dimensional Gel Electrophoresis. Electrophoresis 29, 1369–1381 (2008) 5. Srinark, T., Kambhamettu, C.: An Image Analysis Suite for Spot Detection and Spot Matching in Two-Dimensional Electrophoresis Gels. Electrophoresis 29, 706–715 (2008) 6. Morris, J.S., Clark, B.N., Gutstein, H.B.: Pinnacle: a Fast, Automatic and Accurate Method for Detecting and Quantifying Protein Spots in 2-Dimensional Gel Electrophoresis Data. Bioinformatics 24, 529–536 (2008) 7. Rye, M.B., Færgestad, E.M., Martens, H.: An Improved Pixel-Based Approach for Analyzing Images in Two-Dimensional Gel Electrophoresis. Electrophoresis 29, 1382–1393 (2008) 8. Soille, P.: Morphological Image Analysis: Principles and Applications, 2nd edn. Springer, Heidelberg (2003) 9. Van Belle, W., Sjøholt, G., Anensen, N.: Adaptive Contrast Enhancement of TwoDimensional Electrophoretic Protein Gel Images Facilitates Visualization, Orientation and Alignment. Electrophoresis 27, 4086–4095 (2006)

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains Chun-Hua Xiao1,2, Shao-Kun Li1,2,3,*, Ke-Ru Wang1,2, Yan-Li Lu2, Jun-Hua Bai2, Rui-Zhi Xie2, Shi-Ju Gao2, Qiong Wang2, and Fang-Yong Wang1 1

Key Laboratory of Oasis Ecology Agriculture of Xinjiang Construction Crop/ Center of Crop High-Yield Research, Shihezi 832003, Xinjiang, China 2 Institute of Crop Science, Chinese Academy of Agricultural Sciences / National Key Facility for Crop Gene Resources and Genetic Improvement, NFCRI, Beijing 100081, China 3 Department of Crop Culture, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R. China Tel.: +86-010-68918891 [email protected]

Abstract. The spectral signatures of crop canopies in the field provide much information relating morphological or quality characteristics of crops to their optical properties. This experiment was conducted using two winter-wheat (Triticum aestivum) cultivars, Jingdong8 (with erect leaves) and Zhongyou9507 (with horizontal leaves). We analysiced the relation between the direction spectral characteristics and the laeves nitrogen content(LNC). The result showed that the spectral information observed at the 0° angle mainly provided information on the upper canopy and the lower layer had little impact on their spectra. However, the spectral information observed at 30° and 60° angles reflected the whole canopy information and the status of the lower layer of the canopy had great effects on their spectra. Variance analysis indicated that the ear layer of canopy and the topmost leaf blade made greater contributions to CDS. The predicted grain protein content (GPC) model by leaf layers spectra using 0° view angle was the best with root mean squares (RMSE) of 0.7500 for Jingdong8 and 0.6461 for Zhongyou9507. The coefficients of determination, R2 between measured and estimated grain protein contents were 0.7467 and 0.7599. Thus, grain protein may be reliably predicted from the leaf layer spectral model. Keywords: wheat canopy, leaf distribution, direction spectra, view angle, model.

1 Introduction The spectral characteristics of a crop canopy are determined not only by biophysical and biochemical features and also plant structural attributes. Leaf optical properties are a main factor for canopy spectral. *

Corresponding author.

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 16–29, 2011. © IFIP International Federation for Information Processing 2011

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

17

Crop canopy spectral characteristics represent important information needed to guide crop management. Crop composition and structure are difficult to assess by traditional spectral measurement with vertical canopy direction. The direction spectrum includes a great deal of crop canopy information. Multiangle data significantly improved the accuracy of recovering forest parameters when inverting 3-D optical models (Kimes et al., 2002) but forest vertical structure can not be captured accurately using only the 4 spectral bands in the nadir view or all view angles with a single spectral band (Kimes et al.,2006). Directional radiances in or near the principal plane of the sun provides information that leads to more accurate prediction of canopy structure parameters than from other azimuth planes (Gobron et al., 2000). Canopy emissivity increased with increasing view angle due to the greater proportion of vegetation observed at off-nadir view angles; when the proportion of leaves was lower than that of soil, canopy emissivity grew with increasing view angle (Sobrino et al., 2005). It should be noted that these models assume a Lambertian behaviour for soil and vegetation surfaces. Although vegetation surfaces show a near Lambertian behaviour, bare soil surfaces do not and the angular variation on emissivity can not be neglected. Lidars, multiangle radiometers, radars and imaging spectrometers have been identified as systems that can capture information in the vertical dimension. This requires a capability to remotely measure the vertical and spatial distribution of forest structural parameters that are needed for more accurate models of energy, carbon, and water flux over regional, continental, and global scales. Thus, we examined the utility of hyperspectral data for the quantitative characterization of vertical wheat structure. Most remote sensing systems provided an image of the horizontal scope, but could not provide the vertical information on biochemical distribution in a crop canopy, thereby reducing the accuracy of measurement. Multi-angle data can increase the precision of forest parameters (Kimes,et al., 2006). The distribution of tissues in a crop canopy has certain characteristics - biochemical distribution is different because of transfer of matter during the growth stage. Leaves in a wheat canopy are composed of under, middle, upper layer and ear layers (Wang et al.,2004). The spectral characteristic differed among canopy leaves because of the different reflection and scatter, so their effect on canopy spectra was different (Wang et al., 2004). Various biochemical (foliar lignin and nitrogen) and biophysical factors influencing canopy reflectance signatures have been studied in previous works. Information on biochemical parameters is important and the multi-angle spectra which provide information on different directions can facilitate a more exact prediction of biochemical parameters. To date, there are no studies on the relative importance of vertical distribution of wheat leaves that determines canopy reflectance across the shortwave (350–2500nm) spectrum. The contribution of each leaf layer relative to all other factors has also not been adequately determined. Yet, it is the interaction of these factors, including their potential covariance or unique behavior, that must be understood if advances in remote sensing are to be achieved.

18

C.-H. Xiao et al.

In this study, the leaf slice method was used to characterize a wheat canopy, evaluate the spectral response of leaf vertical distribution in the canopy, determine the characteristics of spectral curve for different leaf layers and develop a methodology for predicting the biochemistry of the canopy.

2 Materials and Methods 2.1 Preliminary Experiment A field experiment was conducted in the Experimental Station of the Institute of Crop Science of the Chinese Academy of Agricultural Sciences, Beijing (39º57'55" N, 116º19'46" E) in the 2005-2006 growing season. The soil was a silt clay loam containing 1.16% organic matter, 42.6 mg kg-1alkali-hydrolysable N, 26.5 mg kg-1 available phosphorus and 139.4 mg kg-1 available potassium. Two winter wheat cultivars were used: Jingdong8, an erect leaf plant-type and Zhongyou9507, a lax leaf plant-type. Four N fertilizer (urea, 46%N ) treatments were set up with four randomized replications of each cultivar: N0, no N fertilization, N1, 150 kg hm-2 pure N fertilization, N2, 300 kg hm-2 (rationally fertilized), N3, 450 kg hm-2 (excessively fertilized). The N rates were applied in three splits at pre-sowing (50% of the total amount), reviving stage (25% of the total amount) and jointing stage (25% of the total amount). All the treatments were fertilized with the same amounts of P (P2O5, 144 kg hm-2) and K (K2O, 75 kg hm-2) at pre-sowing. The leaf slice method of wheat canopy: whole (plant) wheat samples 0.5m long and 0.8m wide were chosen; based on the vertical distribution of wheat canopy, the samples were measured off the whole wheat canopy (WWC), ear layer of canopy (ELC), inverse first leaves layer of canopy (ILLC-1), inverse second leaves layer of canopy(ILLC-2), inverse third leaves layers of canopy (ILLC-3) and inverse fourth leaves layer of canopy (ILLC-4). Canopy layers were severed with a scissors from the ear layer to lower layer (Fig. 3).

，

2.2 Measured Traits and Methods All canopy spectral measurements were taken from a height of 1.3 m above ground (the height of the wheat was 90 cm at maturity), under clear sky conditions between 10:00 and 14:00 (Beijing local time), using an ASD FieldSpec Pro spectrometer (Analytical Spectral Devices, Boulder, CO,USA) fitted with a 258 field of view fiber optics, operating in the 350–2500 nm spectral region with a sampling interval of 1.4 nm between 350 and 1050 nm and 2 nm between 1050 and 2500 nm and with spectral resolutions of 3 nm at 700 nm and10 nm at 1400 nm. A 40 cm ×40 cm BaSO4 calibration panel was used for calculation of reflectance. The spectra were measured with view angles of 0, 30,60,90,120, 150, and 180° to the line vertical to the wheat row using the Simple multi-angle spectral measurement equipment (Fig.1) after every layer was removed (N2 treatment). The model spectra were measured with a view angle of 0°

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

19

using other treatments at ear layer (EL), upper leaves layer (ULL) and lower leaves layer(LLL)(Fig.2). Leaf samples from EL,ULL,LLL were taken almost synchronously with the spectral measurements. Measurements were conducted at jointing, heading, anthesis, milking and waxing stages. These samples were oven-dried at 70 and nitrogen content was determined by the Kjeldahl technique (Bremneretal.,1981) using a B-339 DistillationUnit (BUCHIAnalyticalLtd, Flawil, Switzerland). Wheat grain protein content estimated from the formula : Pro% =6.25 × Nitr (% ).

℃

sun

Ear layer

Upper layer

1/2H H

Lower layer

Fig. 1. Simple multi-angle spectral measurement equipment

1/2H

Fig. 2. Sketch of measured method by layer

2.3 Data Analysis The hyperspectral data were analyzed using the Matlab6.5 software and quantitative data were analyzed using an analysis of variance (ANOVA) procedure.

3 Results 3.1 The Spectral Curves Following Removal of Different Leaf Layers The lower leaves of the canopy changed the spectral reflectivity (Fig.3) with different view angles at 350-700nm, 800-1300nm and 1400-1800nm. This is important for pigment content within the visible wave band (350-700nm). The reflectivity of near infrared (800-1300nm) is influenced by canopy characteristics. The wavebands (1400-1800nm) provide information on the water content. In this paper, we analyzed the characteristics of visible (350-700nm) and near infrared (800-1300nm) wavebands.

20

C.-H. Xiao et al.



Jingdong8



5HIOHFWLRQ

 5 HI O HF WL R Q



  

Zhongyou9507

    

  e e











:DYHOHQWKQP e e e e









:DYHOHQJWKQP e e

e

e e

e e

e

Fig. 3. Spectra of removing the forth leaf layer at different angles for Jingdong8 and Zhongyou9507 wheat 

5HIOHFWLRQ

5HIOHFWLRQ

Jingdong8

     









          

Zhongyou9507



:DYHOHQJWKQP ,//& ,//&

(/& ,//&

,//& ::&

,//& ::&







:DYHOHQJWKQP ,//& ,//& (/&

 ,//&

Fig. 4. Spectra of removing different leaf layers at 60°angles for Jingdong8 and Zhongyou9507

Comparing the direction spectral characteristic of the two wheat canopies, the spectral response of Jingdong8 with different view angles was more significant than that of Zhongyou9507 at visible and near infrared wavebands; the max reflectivity of Jingdong8 was 31% higher than that of Zhongyou9507 at 350-700nm and 22.4% higher at 800-1300nm. The spectral curve of doing away with different leaves at the 60° view angle showed that the leaf influenced the curve of visible and near infrared wavebands (Fig.4). The change for Jingdong8 was more significant than for Zhongyou9507; the spectral reflectivity of Jingdong8 was 11.2% higher than that of Zhongyou9507 at 350-700nm and 26.8% higher at 800-1300nm. To further analyze the relation between canopy spectra and leaves, we selected six spectral reflectivities at 450, 550, 670, 980, 1090, 1200nm. 3.2 Analysis of Spectral Reflectivity of Leaf Layers at Different View Angles As shown in Fig.5, for Jingdong8, the spectral reflectivity of the whole canopy was similar with the leaf layer removal treatment, ILLC-4; doing away with ELC and ILLC-1 reduced the reflectivity. The changes in reflectivity at 550nm were more

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

WWC

21

ELC

ILLC-1

ILLC-2





Fig. 5. Spectral characteristics following removal of different leaf layers in Jingdong8

obvious than at 450 and 670nm. In visible wave bands, changes were evident for 980, 1090, 1200nm in near infrared wave bands. At different view angles, the reflectivity of the 90° view angle was the lowest and with increasing distance from the vertical measurement, the reflectivity increased, especially in visible wave bands than in near infrared. The reflectivity changes due to ILLC-1 and ELC were more obvious than those of other leaf layers. The reflectivity changes due to LLC-3 at 1090nm were 8.8% at 0° view angle and 48.6% for ILLC-1. At 90° view angle, the changes were 5.6 and 40.7%; at 30°, they were 12.9 and 27.7% and at 60° they were 12.2 and 34%, respectively. The change was less pronounced at the 180, 150 and 120° view angles. Compared to the 90° view angle, the reflectivity at 30 and 60° had more information on the lower leaves, which were

22

C.-H. Xiao et al.

important for reflectivity of the canopy. The reflectivity at 0° contained very important information on upper leaves. The canopy spectral reflectivity of Zhongyou9507 was lower than that of Jingdong8 (Fig.6) at1090nm; the reflectivity due to ILLC-3 at 0° view angle changed 4.4%, while that due to ILLC-1 changed14.7%; at 90° view angle, the changes were 5.9 and 46.8%; at 30°, they were 16.9 and 44.4% and at 60° they were 16.8 and 41.2%. Compared with the traditional 90° view angle, the lower leaves were important for canopy spectra at 30 and 60° view angles; the upper leaves were important at 0° view angle although the upper leaves of Zhongyou9507 had less influence on canopy spectra than those of Jingdong8.

WWC

ILLC-1

ILLC-3

ELC

ILLC-2

ILLC-4

Fig. 6. Spectral characteristics following removal of different leaf layers in Zhongyou9507

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

23

3.3 Analysis of Variance (ANOVA) of Spectral Reflectivity for Different Leaf Layers The ANOVA of mean reflectivities across six wavelengths showed that the reflectivity of Jingdong8 was lower than that of Zhongyou9507 (Table 1). For Jingdong8, the response of canopy spectra due to ILLC-1 and ELC were significant at the 0° view angle; at 30° as for 60°, the effects of ELC, ILLC-1, ILLC-2 and ILLC-3 were significant; at 90°, the ELC and ILLC-1 were important and at 120, 150 and 180° view angles, the ELC and ILLC-1 were significant. The ELC and ILLC-1 were important for canopy spectra and the influence of lower leaves were less. For the view angles of 0°, 90°, 120°, 150°, 180°), the under leaves were less influential; at 30 and 60°, the response of lower leaves were significant. The canopy spectral responses of different leaves for Zhongyou9507 differed from that of Jingdong8; at 0 and 90° view angles, the ELC and ILLC-1 effects were significant at 30 and 60°, the ELC, ILLC-1, ILLC-2 effects were significant. At the same view angle, the ELC and ILLC-1 were more important than the lower leaves. The spectral response of lower leaves was related to the view angle. With vertical and horizontal measurements, the influence of lower leaves were less than that of other view angles. Table 1. Canopy spectral characteristic of different leaf layers of Jingdong8 and Zhongyou9507

Average value Angles°

Treatments

0

WWC ILLC-3 ILLC-4 ILLC-2 ILLC-1 ELC WWC ILLC-4 ILLC-3 ILLC-2 ILLC-1 ELC WWC ILLC-4 ILLC-3 ILLC-2 ILLC-1 ELC

30

60

0.2254 0.2110 0.2046 0.1974 0.1690 0.1508 0.2265 0.2196 0.1951 0.1886 0.1591 0.1487 0.2064 0.2021 0.1816 0.1645 0.1412 0.1225

Jingdong8 a A a A a AB ab AB bc BC c C a A ab A bc AB c AB d BC d C a A a A ab AB bc ABC bc BC c C

Zhongyou9507 0.1657 a 0.1643 a 0.1662 a 0.1573 a 0.1354 b 0.1308 b 0.1713 a 0.1646 a 0.1619 ab 0.1398 bc 0.1276 cd 0.1143 d 0.1784 a 0.1553 ab 0.1503 ab 0.1323 bc 0.1052 c 0.0974 c

A A A A B B A AB AB BC C C A A AB ABC BC C

24

C.-H. Xiao et al.

Table 1. (continued)

Average value Angles°

Treatments

90

WWC ILLC-4 ILLC-3 ILLC-2 ELC ILLC-1 WWC ILLC-4 ILLC-3 ILLC-2 ILLC-1 ELC ILLC-4 ILLC-3 ILLC-2 WWC ILLC-1 ELC WWC ILLC-4 ILLC-3 ILLC-2 ILLC-1 ELC

120

150

180

0.1588 0.1726 0.1451 0.1292 0.1081 0.1040 0.1645 0.1642 0.1511 0.1341 0.1139 0.1051 0.1734 0.1637 0.1547 0.1454 0.1325 0.1166 0.2254 0.1879 0.1883 0.1725 0.1388 0.1360

Jingdong8 a A a AB ab AB ab AB b B b B a A a A ab AB abc AB bc AB c B a A ab AB abc AB abc ABC cd BC d C a A ab AB ab ABC bc BC c BC c C

Zhongyou9507 0.1766 a 0.1657 a 0.1532 abc 0.1276 abcd 0.1137 bcd 0.1084 d 0.1765 a 0.1692 ab 0.1616 abc 0.1432 bcd 0.1255 d 0.1325 cd 0.1679 ab 0.1692 a 0.1568 bc 0.1672 ab 0.1515 c 0.1555 c 0.1678 a 0.1599 a 0.1555 a 0.1464 a 0.1461 b 0.1537 b

A AB AB AB B B A AB AB AB AB B A A A AB AB B A A A A B B

Note: Means followed by different lower case letters differ significantly at P< 0.05; those followed by different upper case letters differ significantly at P<0.01.

3.4 Correlation of Spectral Parameters of Leaf Layers with Leaf Nitrogen Content (LNC)

△

The correlation between canopy spectral parameters, spectral grads and corresponding nitrogen content in different leaf layers (Table 2) was significant between RVI[670, 890] and leaf layer grads at the flowering stage for Jingdong8 and milky stage for Zhongyou9507.

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

25

Table 2. Correlation coefficients between nitrogen contents at different layers and spectral characteristics ( SC ) Growth stage

Cultivar

content

680nm

NDVI [857,1210]

PRI

PPR

(570,531)

(550,540)

OSAVI

RVI [670,890]

ULNC

-0.2058

0.6835*

0.1929

-0.4270

-0.6850*

LLNC

-0.2016

0.6437*

0.2082

-0.3300

-0.5669

0.5361

LNC

-0.2779

0.6431*

0.5443

-0.5240

-0.3256

0.7710**

Zhongyou

ULNC

0.0627

-0.2781

0.0508

-0.6561*

-0.0322

0.6171*

9507

LLNC

0.5923

-0.0223

0.2141

-0.5069

0.1373

0.5710

LNC

-0.2234

-0.4238

-0.6304*

-0.4990

-0.1748

-0.6335*

ULNC

0.3290

0.5788

0.6957*

-0.5719

0.4431

0.5831

LLNC

0.3656

0.5690

0.4514

-0.5913

0.4368

0.6097*

LNC

0.5610

0.3882

-0.4454

-0.3284

-0.5557

0.6627*

Zhongyou

ULNC

0.2041

0.1279

0.1432

-0.5355

0.2822

0.4570

9507

LLNC

0.4674

-0.4111

-0.5394

0.6751*

-0.2721

0.5118

LNC

-0.1453

-0.5269

-0.6252*

-0.4591

-0.1190

-0.7888**

Jingdong8 Anrhesis

Milky

LSCP

nitrogen

Jingdong8

ripe

0.6900*

* and ** : significant correlations at 0.05 and 0.01 probability levels, respectively. ULNC: upper layer nitrogen content; LLNC: lower layer nitrogen content;

△LNC: the difference between

ULNC and LLNC. LSCP: spectral characteristics of leaf layers. NDVI: normalized difference vegetation index; PRI: pigment ratio index; PPR: plant pigment ratio; OSAVI: optical soil adjusted index; RVI: ratio of vegetation index.

3.5 Model Relating Grads of Spectral Parameter and Those of Nitrogen Content across Leaf Layers The following models were developed from the siginificant correlations between grads of spectral parameter and those of nitrogen content in leaf layers: Flowering stage of Jingdong8:

△LNC (%) = 35.2842△RVI+0.3822 R2=0.8267**, n=24

(1)

Milking stage of Zhongyou9507:

△LNC (%) = -8.0692△RVI+1.5506 R2=0.8099**, n=24

(2)

3.6 Leaf Layer Spectral Model for Protein Content in Wheat Grains A prediction model for GPC based on spectra of leaf layers was developed as follows: a) Jingdong8 at the flowering stage:

△RVI + 15.6975

GPC (%) = 12.0168 2

R =0.7727**, n=24

(3)

26

C.-H. Xiao et al.

b) Zhongyou9507 at the milk stage: GPC (%)=-8.9028 2

△RVI+18.7397

R =0.7838**, n=24

(4)

The conventional spectral model using 90°view angle was also developed as follows: a) Jingdong8 at the flowering stage: GPC (%) = 11.98RVI +16.71 R2=0.6537**, n=24

(5)

b) Zhongyou9507 at the milk stage: GPC (%) = 19.21RVI +13.59 R2=0.6659**, n=24

(6)

The determinant coefficients, R2 of models (5) and (6) were 18 and 17% higher than those of models (3) and (4), hence, the predicted GPC model of leaf layer spectral was better. 3.7 Validation of the Predicted GPC Model of Leaf Layer Spectra

⌟ᅮؐ0HDVXUHGYDOXH

⌟ᅮؐ0HDVXUHGYDOXH

The root mean square error (RMSE) was used to test the reliability and estimation accuracy of these models for grain protein content. The RMSE of Jingdong8 was 0.7500 while that of Zhongyou9507 was 0.6461. The determinant coefficients, R2 between measured and estimated grain protein contents were 0.7467 and 0.7599, respectively (Fig. 7). These result indicated that prediction of grain protein using the leaf layer spectral model is reliable. 

5   

    

Jingdong 8

 

  5   

    

Zhongyou 9507

 

















乘⌟ؐ(VWLPDWHGYDOXH

















乘⌟ؐ(VWLPDWHGYDOXH

Fig. 7. Estimated and measured grain protein contents (GPC) in Jingdong 8 and Zhongyou 9507

4 Conclusions The crop canopy has a three-dimensional structure whose characteristics are determined by species composition and the three-dimensional distribution of leaves. The biochemical content has some regulatory role in the distribution of the canopy space. The horizontal

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

27

radiation balances are likely to occur if the internal variability of derived surface products, the typical distances that photons may travel horizontally within such 3-D media extend to spatial scales that are similar to or larger than those of the measuring sensor In order to maintain the energy balance of the overal forest domain, lacal canopy volumes with rather large positive net horizontal fluxes must also exist,thus underscoring the importance of properly locating local flux measurement equipment(Jean-Luc Widlowski, 2006). The more accurate modeling of energy are required for remotely measure the vertical and spatial distribution of forest structure. The potential of using a multi-angle spectral sensor to predict forest vertical structure were researched, the results showed the multi-angle spectral provided a relatively direct measure of vertical structure(D.S.Kimes 2006). The anisotropic scattering of both the vegetation canopy and the background is taken into consideration in a two-layer model of the bidirectional reflectance of homogeneous vegetation canopies(S.Liangrocapart, 2002). In this study, two wheat varieties differing in plant-type were studied using the leaf layer slice method by analyzing the spectral changes for different leaf layers at different view angles in the plane of vertical wheat line. We noted that: (1) The spectral characteristics varied in 300-700nm, 800-1300nm and 1400-1800nm wave bands because of removal of leaf layers and differences in view angle. The change in the visible wave bands was less than that in near infrared wave bands and that in Jingdong8 was less than that in Zhongyou9507. (2) Compared with the traditional 90° view angle, the spectral information of under leaves were more at 30 and 60° than other view angles; for upper leaves the information was more at 0° view angle. The spectral influence of upper leaves of Zhongyou9507 at 0° view angle was less than that of Jingdong8. The upper leaves of the lax-leafed wheat captured much radiation because of their horizontal orientation, the proportion of upward leaves was bigger than that of downwards leaves. In contrast, the radiation characteristics were complicated in the erect leaf plant-type. (3) The predicted GPC model by leaf layer spectra using 0° view angle were feasible, the RMSE was 0.7500 for Jingdong8 and 0.6461 for Zhongyou9507. The determinant coefficients, R2 between measured and estimated grain protein contents were 0.7467 and 0.7599. Thus, the leaf layer model would be reliable for predicting grain protein content. In precision agriculture, it is important to accurately measure the state of a crop’s growth using remote sensing techniques. In this study, we analyzed the canopy spectral characteristics of two wheat types and observed that canopy spectra were influenced by leaf layers and view angles; the spectral response of under leaves varied with different view angles. A quantitative study of different leaf layers and their spectra is needed for precise crop management. Since results were obtained only in Peking region, more experiments should done in other regions.

Acknowledgments This work was supported with grants (2006AA10Z207, 2006AA10A302) from the National High Tech R&D Program and (30760109) from the National Natural Science Fundation of China and (2007BAH12B02) National Key Technology R&D Program.

28

C.-H. Xiao et al.

References [1] Dorigo, W.A., Zurita-Milla, R., de Wit, A.J.W., Brazile, J., Singh, R., Schaepman, M.E.: A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. International Journal of Applied Earth Observation and Geoinformation 5, 1–29 (2006) [2] Kimes, D.S., Ranson, K.J., Sun, G., Blair, J.B.: Predicting lidar measured forest vertical structure from multi-angle spectral data. Remote Sensing of Environment 100, 503–511 (2006) [3] Sobrino, J.A., Jiménez-Muñoz, J.C., Verhoef, W.: Canopy directional emissivity: comparison between models. Remote Sensing of Environment 99, 304–314 (2005) [4] O’Neill, A.L., Kupiec, J.A., Curran, P.J.: Biochemical and reflectance variation throughout a sitka spruce canopy. Remote Sensing of Environment 80, 134–142 (2002) [5] Wang, Z.J., Wang, J.H., Huang, W.J., Ma, Z.H., Wang, B.H., Zhao, C.J., Zhao, M.: The properties of temporal and spatial distributions relationship between leaf nitrogen and grain quality leaf nitrogen and the winter wheat. Acta Agronomica Sinica 7, 700–707 (2004) (in Chinese) [6] Wang, J.H., Wang, Z.J., Huang, W.J., Ma, Z.H., Liu, L.Y., Zhao, C.J.: The vertical distribution characteristic and spectral response of canopy nitrogen in different layer of winter wheat. Journal of Remote Sensing 7, 309–315 (2004) (in Chinese) [7] Johnson, L.F.: Nitrogen influence on fresh-leaf NIR spectra. Remote Sensing of Environment 78, 314–320 (2001) [8] Serrano, L., Peñuelas, J., Ustin, S.L.: Remote sensing of nitrogen and lignin in mediterranean vegetation from AVIRIS data: decomposing biochemical from structural signals. Remote Sensing of Environment 81, 355–364 (2002) [9] Cho, M.A., Skidmore, A.K.: A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sensing of Environment 101, 181–193 (2006) [10] Li, Y.X., Zhu, Y., Tian, Y.C., Yao, X., Qin, X.D., Cao, W.X.: Quantitative relationship between leaf nitrogen accumulation and canopy reflectance spectra in wheat. Acta Agronomica Sinica 2, 203–209 (2006) (in Chinese) [11] Xue, L.H., Cao, W.X., Luo, W.H., Jiang, D., Meng, Y.L., Zhu, Y.: Diagnosis of nitrogen status in rice leaves with the canopy spectral reflectance. Scientia Agricultura Sinica 36(7), 807–812 (2003) (in Chinese) [12] Lu, Y.L., Wang, J.H., Li, S.K., Xie, R.Z., Gao, S.J., Ma, D.L.: Identification of wheat canopy structure using hyperspectral data. Scientia Agricultura Sinica 4(9), 668–672 (2005) [13] 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) (in Chinese) [14] Liu, W.-d., Xiang, Y.-q., Zheng, L.-f., Tong, Q.-x., Wu, C.-s.: Relationships between rice LAI, CH.D and hyperspectra data. Journal of Remote Sensing 4(4), 279–283 (2000) (in Chinese) [15] Wu, C.-s., Xianh, Y.-q., Zheng, L.-f., Tong, Q.-x.: Estimating chlorophyll density of crop canopies by using hyperspectral data. Journal of Remote Sensing 4(3), 228–232 (2000) (in Chinese) [16] Zhang, X., Liu, L.-y., Zhao, C.-j., Zhang, B.: Estimating wheat nitrogen concentration with high spectral resolution image. Journal of Remote Sensing 7(3), 176–181 (2003)

A Leaf Layer Spectral Model for Estimating Protein Content of Wheat Grains

29

[17] Widlowski, J.-L., Pinty, B., Lavergne, T., Verstraete, M.M., Gobron, N.: Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption. Remote Sensing of Environment 103, 379–397 (2006) [18] Walter-Shea, E.A., Privette, J., Cornell, D., Mesarch, M.A., Hays, C.J.: Relations between directional spectral vegetation indices and leaf area and absorbed radiation in alfalfa1. Remote Sensing of Environment 61, 16–177 (1997) [19] Kuusk, A., Nilson, T.: A directional multispectral forest reflectance model. Remote Sensing of Environment 72, 244–252 (2000) [20] Gemmell, F., McDonald, A.J.: View zenith angle effects on the forest information content of three spectral indices. Remote Sensing of Environment 72, 139–158 (2000) [21] Bousquet, L., Lachérade, S., Jacquemoud, S., Moya, I.: Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment 98, 201–211 (2005) [22] Gao, F., Zhu, Q.J.: The advance in multi-angle remote sensing of vegetation canopy. Scientia Geographica Sinica 11, 346–355 (1997) [23] Lu, Y.L., Li, S.K., Wang, K.R., Xie, R.Z., Gao, S.J., Wang, G.: Prediction of grain protein content using ear layer spectral reflectance data. Acta Agron Sinica 2(32), 232–236 (2006) [24] Lu, Y.L., Li, S.K., Wang, J.H.: Prediction of grain protein content of wheat based on the ear level spectrum. In: 25th Anniversay IGARSS (International Geoscience and Remote Sensing Symposium) 2005, Seoul Korea, July 25-29 (2005) [25] Xiao, C.H., Li, S.K., Wang, K.R., Lu, Y.L., Xie, R.Z., Gao, S.J.: Prediction canopy leaf nitrogen content of winter wheat based on reflectance spectra in different directions. Acta Agron Sinica 33(7), 1141–1145 (2007)

A New Color Information Entropy Retrieval Method for Pathological Cell Image Xiangang Jiang1, Qing Liang2, and Tao Shen2 1

Information Engineering School of East China, Jiaotong University, Nanchang, Jiangxi, China (330013) [email protected] 2 Basic Science School of East China Jiaotong University, Nanchang, Jiangxi, China (330013) [email protected]

Abstract. For the distribution characteristics in a slice of pathological cell image, the system transforms them into the characteristic vector by quantization and clustering in HSV color model, it promotes the concerned isolated pixel color description into the color feature of its neighborhood region color histogram. By choosing appropriate neighborhood window size, it uses color information entropy method to gain efficient primary classification of gastric slice image and the similar retrieval results from the sample library images. Experiments show that this method has a better image classification and retrieval result. Keywords: Mutual information, Histogram, Color moments, Information entropy, CBIR.

1 Introduction With the unceasing development of the modern imaging technology and information processing technology, the image numbers in medical scopes are more and more. By the submitting image, how to use the existing technical methods to retrieve and classify the comparative images from the qualitative sample library is crucial, which is used for clinical diagnosis and contrast, it is becoming one of the research task for efficient medical diagnosis. In allusion to the pathological cell images, this paper makes use of neighborhood color moment histogram to extract color information and utilizes the theory of information entropy to realize shortcut image retrieval.

2 Shannon Mutual Information Representation of mutual information derives from information theory, which is the measure of correlations of two variables. If use H to represent entropy, I represent an image, a represent the color values of an image, suppose the probability density D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 30–38, 2011. © IFIP International Federation for Information Processing 2011

A New Color Information Entropy Retrieval Method for Pathological Cell Image

function of

31

I is pi = p(a = i), i = 1, 2,..., m , then the Shannon Entropy of I is

H (I ) :

1 pi (i)

H ( I ) = ∑ pi (i) × log i∈I

Where as

(1)

pi (i) = 0 , supplementary definition: pi (i) × log

To two images

1 1 = 0 , as: lim+ p log = 0 . p → 0 p pi (i)

I1 and I 2 ,their color values are described by 256 kinds of vectors,

their joint entropy is defined as: H ( I1 , I 2 ) =

255 255

1

∑ ∑ P ( i , j ) log P ( i , j ) i=0 j=0

ij

(2)

ij

Where Pij (i, j ) is the representation of color joint probability density distribution. Their Shannon mutual information I ( I1 , I 2 ) =

I ( I1 , I 2 ) is defined as:

255 255

∑ ∑ P ( i , j ) log i=0 j=0

ij

Pij ( i , j )

(3)

p i (i ) p j ( j )

= H ( I1 ) + H ( I 2 ) − H ( I1 , I 2 ) Where

pi (i ), p j ( j ) is the color probability density distribution of I1 and I 2 , Pij (i, j ) is

color joint probability density distribution, H ( I1 , I 2 ) is Shannon joint entropy. In the image registration and retrieval, Shannon mutual information is a representation of information amount of interactivity implicated in two images. When I ( I1 , I 2 ) = 0 , it shows that

I1 and I 2 are independent mutually, if the value of I ( I1 , I 2 ) is bigger,

which means the similarity is higher in the color distribution of two images.

3 Color Space Conversions Among many color models, RGB models can be better matched with the fact that people can fiercely percept the tricolor such as R, G, and B, furthermore, it is easy to express in software developing language. However, RGB color model is not accorded to the human beings’ visual perception dealing with distinguishing color similarity. Therefore, many researchers put forward some other color models accorded to visual perception, such as HIS, HSV, Luv, Lab and so on. HSV is more commonly used, its three-component separately represent Hue, Saturation and Value. This paper uses the HSV color model to define color vector. Firstly the transfer formula between RGB and HSV is given as follows. Suppose a pixel values are r, g, and b in RGB color model, in

32

X. Jiang, Q. Liang, and T. Shen

which r , g , b ∈ [0...255] , then the transformed values (h,s and v) in HSV color model are calculated as follows follows: r' =

Then

： Suppose v' = max(r , g , b)

and define

r ' , g ' , b ' as

v'−r v'−g v'−b , g' = , b' = v'− min(r, g, b) v'− min(r, g, b) v'− min(r, g, b)

v = v ' 255 ,

s = (v ' − min(r , g , b)) v ' ⎧5 + b , r = max(r , g , b) ⎪ ' ⎪1 − g , r = max(r , g , b) ⎪1 + r ' , g = max(r , g , b) ⎪ h' = ⎨ ' ⎪3 − b , g = max( r, g , b) ⎪3 + g ' , b = max(r , g , b) ⎪ ⎪⎩5 − r ' , else '

(4)

and g = min( r , g , b) and b = min( r , g , b) and b = min( r , g , b) and g = min( r , g , b) and r = min(r , g , b)

h = 60 × h'

Where r ' , g ' , b ' ∈ [0,1] , h ∈ [0, 360] , s, v ∈ [0,1] . In allusion to Fig.1, make use of the transfer formula above firstly, in the HSV color model h component is mainly concentrated in range [0,100] and [300, 360] , but the distribution of s and v component are relative uniformity. According to the characteristics, it will use the method in reference [1] for quantization and clustering of colors. Suppose the color vectors of pixel (i , j ) are pij ∈ [0, 255] , which will be used for image retrieval features.

4 Neighborhood Color Moment Mutual Information 4.1 Description of Color Moment A. single pixel color moment Another method of expressing and analyzing color distribution is the calculation of color moment. The mathematical foundation of this method being reasonable is that any color distribution can be represented by its moments. In addition, as the color distribution information is mainly concentrated in lower-order-moments, so it is enough to express the color distribution of an image only by its first moment, second moment and third moment of colors. Comparing to color histogram, this method is good at the aspect that no needs to quantize the color characteristics. The lower-order-moments of color are expressed in mathematics as follows:

A New Color Information Entropy Retrieval Method for Pathological Cell Image

⎧ 1 N μ = ⎪ ∑ pj N A j =1 ⎪ 1 ⎪⎪ 1 N 2 2 p μ − ( ) ) ⎨σ = ( ∑ j N A j =1 ⎪ 1 ⎪ 1 N 3 3 ⎪s = ( p μ − ( ) ) ∑ j N A j =1 ⎪⎩ Where

33

(5)

p j is the definition of color value of pixel j , N A represents the whole number

of pixels in an image. B. Neighborhood color moment As the ability to distinguish based on lower-order-moments of single pixel color is suboptimal, it doesn’t make comprehensive consideration to its local color distribution of pixels. So in this paper, it considers the neighborhood of pixels as an analysis unit to calculate the corresponding color moments and it concerns the local color spatial distribution information reflected by pre-three lower-order-moments in a pixel’s around window. Suppose pij is the color value of concerned pixel (i, j ) , which is quantified and clustered in HSV color model, then its first, second and third order central moment are as follows: N −1 M −1 i+ j+ ⎧ 2 2 1 ⎪μ M = pmn ∑ ∑ ⎪ ij M × N m = i − N −1 n = j − M − 1 ⎪ 2 2 ⎪ N −1 M −1 i+ j+ 1 2 2 ⎪⎪ M 1 ( p m n −u ijM ) 2 ] 2 ⎨ σ ij = [ ∑ ∑ M × N m = i − N −1 n = j − M −1 ⎪ 2 2 ⎪ N −1 M −1 i + j + ⎪ 1 2 2 ⎪sM = [ 1 ( p m n −u ijM ) 3 ] 3 ∑ ∑ ij ⎪ M × N m = i − N −1 n = j − M −1 ⎪⎩ 2 2

(6)

The moving window is respectively selected as 3 × 3 , 5 × 5 , 7 × 7 , the range of numerical values in extraction of three lower-order-moments of all pixels (i, j ) is limited in [0, 255] . In allusion to each pixel in an image, it makes use of quantization and clustering color histogram of image referred above to obtain nine neighborhood color moments, (for a window 3 × 3 ), which are described in the region [0, 255] just by rounding all float numbers. It makes the global statistics and normalization to the entire image and gets normalization histograms of nine neighborhood lower-order central moments.Fig.2 shows as: the normalization histograms of the first, second and third order central moment in 3 × 3 neighborhood window, where the red curve represents H ( μ 3 ) , the green one represents H (σ 3 ) , the blue one represents

H ( s 3 ) and the black one represents the color histogram by quantified and clustered color vectors.

34

X. Jiang, Q. Liang, and T. Shen

Fig. 1. Pathological cell image

Fig. 2. The compared histograms of the first, second, third order moment and the entire image’s quantified and clustered color vectors in the 3 × 3 neighborhood window

To 256 colors in the picture, it can make a statistics separately for the frequency of the first, second and third order central moment in an image, suppose the probability distribution of the k order central moment is qik , then:

qik = N ik N A Where

(7)

N ik represents the occurrence times of the k order color central moment, N A

is the representation of all pixel numbers in an image. Define the color joint probability density distribution of the k order central moment of two images by the same methods, which is supposed as qijk , where i, j = 0,1,...255 , k = 1, 2,3 . 4.2 The Calculation of Mutual Information of Each Order Multi-neighborhood Color Moments In order to define the mutual information of multi-neighborhood color moments, instead qik , qijk of pi (i ) , Pij (i, j ) to obtain the definition of entropy and mutual information of multi-neighborhood color moments. Then the definition of entropy of each order multi-neighborhood color moments is: H(I ) = ∑qik (i) × log i∈I

1 qik (i)

(k = 1,2,3)

(8)

The mutual information of each order multi-neighborhood color moments between two images I1 , I 2 is defined as: 255 255

qijk (i, j)

i=0 j =0

qik (i)q jk ( j)

I ( I1, I2 ) = ∑∑qijk (i, j)log

= H ( I1 ) + H ( I 2 ) − H ( I1 , I 2 )

(k = 1,2,3)

(9)

A New Color Information Entropy Retrieval Method for Pathological Cell Image

35

In all, the classification and retrieval for the pathological cell images can be achieved not only by the mutual information of each order multi-neighborhood color moments, but also by the mutual information of synthesis vector normalized by each order neighborhood color moment. 4.3 Similarity Definition According to the theory of mutual information, the similarity of two images is defined as follows:

S ( I1, I 2 ) =

I ( I1 , I 2 ) H ( I1 ) + H ( I 2 )

(10)

Proof, 0 ≤ S ( I1 , I 2 ) ≤ 1 . First, because of nonnegativity of mutual information and the definition of information entropy referred, it shows that I ( I1 , I 2 ) ≥ 0 , H ( I1 ) ≥ 0 , H ( I 2 ) ≥ 0 ,each term of numerator and denominator in formula(10) is nonnegative, therefore, S ( I1 , I 2 ) ≥ 0 . Next prove S(I1, I2 ) ≤ 1, Because of the definition of color entropy, it shows H ( I1 , I 2 ) ≥ 0 and it can be easy to know I ( I1 , I 2 ) ≤ H ( I1 ) + H ( I1 ) according to formula(9), and then S ( I1 , I 2 ) ≤ 1 .So, on the basis of the analysis above, The function S ( I1 , I 2 ) satisfies the definition of similarity, it can be used to describe the color similarity measure of two images.

5 The Experimental Analysis and Conclusions In allusion to three hundred images downloaded from www.phoenixpeptide.com &www.microscopyu.com, sixty cancer images are included and the other ones are all normal. It clearly shows that the main distinctions between cancer images and normal ones are the differences of their color distribution by comparing and analyzing their information entropy of color moment. In the experiment, it employs a new color information entropy retrieval method which promotes the concerned isolated pixel color description into the color feature of its neighborhood region color histogram and makes use of the theory of information entropy to gain the retrieval and primary classification to a further compared classification between the key image and the qualitative sample library. There are three test patterns designed in this experiment the first one do an experiment of retrieval based on color mutual information of single pixel; the second one do an experiment of retrieval by making use of the isolated mutual information of the pre-three order color moment in window size of 3 × 3 , 5 × 5 , 7 × 7 separately; the third one: do an experiment of retrieval based on the mutual information of the normalized characteristics vector of the three order color moments. This system uses the method called “based on the key image” that it gives a submitting image and finds out the similar images from the sample image library, while

36

X. Jiang, Q. Liang, and T. Shen

a retrieval process finished, the system will return the most similar eight images from the qualitative sample image library to achieve the primary qualitative analysis between the submitting image and the qualitative images from the sample image library. This experiment adopts image shown in Fig.1 as the key image. This system implements the retrieval and classification of stomach cell images in a development environment of Windows XP by Delphi 7.0, precision is used for the estimation of the experimental efficiency and it is defined as:

Pr ecision =

a a+b

(11)

Where ' a ' represents the similar image numbers with the key image retrieved from the sample image library; ' b ' represents the image numbers of dissimilarity. In allusion to the image in Fig.1, it uses the method of the mutual information of the single pixel to achieve retrieval and obtain the most similar eight images are showed in Fig.3, Fig.4 shows the retrieval results by employing the method based on the mutual information of the first order color moment in the window size of 5 × 5 . Each color mutual information retrieval efficiency are showed as the table 1.

Fig. 3. The retrieval result by the method based on mutual information of a single pixel

Fig. 4. The retrieval result by the method based on mutual information of the first order color moment with a window size of 5 × 5

A New Color Information Entropy Retrieval Method for Pathological Cell Image

37

Table 1. The comparison of each method of color mutual information

Retrieval method Size of window

Types of color mutual information Single pixel color The first order color moment The second order color moment

3*3

5*5

Precision of Consuming time of using three Consuming using three order order color time(ms) color moment moment synthesis ms synthesis (%)

（ ）

80.65

6141

88.25

8828

90.19

8875 91.23

18012

The third order color moment

90.19

9234

The first order color moment

87.88

8625

The second order color moment The third order color moment The first order color moment

7*7

Precision (%)

The second order color moment The third order color moment

91.30

92.56

12375

92.65

13025

89.31

8968

92.45

92.86

93.17

16875

26412

36247

17400

From the above table, we know that the retrieval efficiencies of the methods of mutual information of each order color moment regarding neighborhood region are higher than the one based on the single pixel, meanwhile, the retrieval efficiencies relative to the color moment order trend to be positive growth, the way based on synthesis mutual information of each order neighborhood color moment has the best precision. At the point of consuming-time, the method of each color moment is longer relatively to the one based on the single pixel and it is in direct proportion to the order. In allusion to the retrieval method of mutual information of color moment in different

38

X. Jiang, Q. Liang, and T. Shen

neighborhood sizes, while the geometric size of each tissue in an image is small, it will be better to choose the smaller window neighborhood, which has a high retrieval efficiency and low consuming-time; otherwise select a bigger neighborhood window to the bigger tissue in an image, it would avoid the characteristic fuzzy of each tissue in an image by adopting a flexible window size.

References [1] Chen, Q., Tai, X., Ba, T.: Gastroscope image retrieval based on histogram of neighborhood color moments. J. Computer Engineering and Applications 44(11), 205–208 (2008) [2] Wang, Y.: Expression on the Basis of the Timber Surface Color Characteristic of the Histogram and Color Moment Method. J. Forestry Science & Technology 31(5), 56–58 (2006) [3] Dong, W., Zhou, M., Geng, G.: Image Retrieval Technique Based on combined features. J. Computer Applications and Software 22(10), 34–36 (2005) [4] Fan, Z., Liu, E., Xu, B.: Application of Mutual Information in Image Retrieval. J. Journal of University of Electronic Science and Technology of China 36(6), 1311–1314 (2007) [5] Huang, X., Chen, G.: Color image retrieval based on color and texture. J. Popular Science & Technology 126(2), 13–15 (2010)

A New Palm-Print Image Feature Extraction Method Based on Wavelet Transform and Principal Component Analysis Jia wei Li and Ming Sun* China Agriculture University, Beijing China [email protected]

Abstract. In recent years, as one of the biometric identification technology, palm-print identification has received many reseachers’ attention. To solve the key problem of palm-print recognition -- feature extraction, we propose a new method, which based on wavelet transform and principal component analysis. In general, we use wavelet transform to deal with palm print images and extract high-dimensional wavelet energy features, then reduce the dimensionality of high-dimensional wavelet energy features through principal component analysis, and remain the original feature energy maximally. The features extracted by this method not only reflect palm-print images’ information maximally, but also achieve the goal of data dimensionality reduction. Experiments show, the correct recognition rates of new method are much higher than those traditional methods such as LDA [1], PCA [2], 2DPCA [3], ICA [4] and so on. Keywords: Palm-print Recognition, feature extraction, wavelet transform, Principal component analysis.

1 Introduction Recently, researchers have done much research on palm-print recognition technology, and proposed many methods of palm-print feature extractions. In these methods, there are mainly methods which based on the structure of feature extraction, such as pointfeature and line-feature; the time-frequency [5] analysis of feature extraction, such as fourier transform, wavelet transform [6,7]; the subspace method of feature extraction, such as PCA,ICA. However, the method effect of feature extraction which based on the point-feature and line-feature of palm-print will be unsatisfied in the situation of low resolution of palm-print images. Similarly, the feature extraction which based on Fourier transform gets high dimensional data which is not advantaged to the real time recognition algorithm. Therefore, we propose a new method which based on wavelet transform and PCA to extract features of palm-print more effective. *

Author for correspondence: [email protected]

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 39–46, 2011. © IFIP International Federation for Information Processing 2011

40

J.w. Li and M. Sun

2 Two-Dimensional Wavelet Transform Firstly, we use edge detect algorithm and corner detect algorithm to locate the ROI (region of interest) of palm-print .Figure 1 shows the original image, the edge of palm, the corner of palm edge and ROI of palm-print. As we know, the palm-print image is a kind of image which is similar to the texture of cycle image, different regions of the ridge orientation and spatial frequency represent the inherent characteristics of palm-print, so we use 2-D wavelet transform to decompose the image which provide the spatial and frequency domain positioning properties and separate the energy characteristics of multi-resolution palm-print image. Let I ( x, y ) denote the ROI of palm-print, according to equation (1) [8], transform the

I ( x, y ) by using 2-D wavelet.

Fig. 1. An example of the preprocessing of palm-print (a) Original Palm-print (b) the edge and corners of palm-print(c) the chopped region of palm-print (d) Region of interest (128*128 pixels) w ( a , b1 , b2 ) =

Where

1 a

+∞ +∞

∫ ∫ I ( x , y )ψ (

−∞ −∞

x − b1 y − b2 , )dxdy a a

(1)

a is scale parameter, b1 , b2 are translation parameters, ψ ( x, y ) is 2-D

wavelet transform functions.

A New Palm-Print Image Feature Extraction Method

41

As the Haar wavelet have the characteristics of orthogonal, compactness and generalized linear phase, so in this paper, we use Haar wavelet to transform the ROI image, and decompose the image in three directions. Haar wavelet can be defined as: 1 ⎧ ⎫ ⎪1, 0 ≤ x < 2 , 0 ≤ y < 1; ⎪ ⎪ ⎪ 1 ⎪ ⎪ ψ H ( x, y ) = ⎨ − 1, ≤ x < 1, − 1 < y ≤ 0; ⎬ 2 ⎪ ⎪ ⎪ 0, else ⎪ ⎪ ⎪ ⎩ ⎭

(2)

Decomposition algorithm [7] is as follows: ⎧ d k0 ( l , i ) ⎪ ⎪ 1 ⎪ d k (l, i) ⎪ ⎨ 2 ⎪ d k (l, i) ⎪ ⎪ d 3 (l , i ) ⎪⎩ k

=

∑ ∑ y

x

=

∑ ∑

=

∑ ∑

y

x

y

=

x

∑ ∑ y

x

( x, y )h ( x − 2l)h ( y − 2i) ⎫ ⎪ ⎪ d k1 + 1 ( x , y ) g ( x − 2 l ) h ( y − 2 i ) ⎪ ⎪ ⎬ d k2 + 1 ( x , y ) h ( x − 2 l ) g ( y − 2 i ) ⎪ ⎪ d k3 + 1 ( x , y ) g ( x − 2 l ) g ( y − 2 i ) ⎪ ⎭⎪ d

0 k +1

(3)

The result of wavelet transformed palm-print image is shown in figure 2.

Fig. 2. An example of three level wavelet decomposition of palm-print image

Then we construct the wavelet energy feature of the decomposed image,

H i Vi and

Di are the i level wavelet decomposition in the horizontal direction, vertical and diagonal directions of the details of the image. The i level wavelet energy in the corresponding directions of the image can be defined as [9,10]: E ih = E iv =

E

d i

=

M

N

∑ ∑ [H x =1 y =1 M

N

x =1

y =1

∑ ∑ M

( x , y )] 2

[V i ( x , y ) ] 2

(4) (5)

N

∑ ∑ x =1

i

y =1

[ D i ( x , y )]2

(6)

42

J.w. Li and M. Sun

These energy features reflect the edge intensity of the i level decomposed wavelet image in horizontal, vertical and diagonal directions. The wavelet decomposition level coefficients on each component of the vector of energy are defined as follows:

vi = ( Eih , Eiv , Eid )i ,2,",M

(7)

Where M is the number of the whole level of wavelet decomposition. Obviously, the energy vectors vi reflect global feature of the palm-print image, and can not describe the information of different regions of the palm. To solve this problem, we divide each detailed image into S × S disjoint blocks, and calculate the wavelet energy of every sub-image. Finally, we use the energy vector of every block to construct multi-resolution texture information of palm-print. Palm-print texture feature vector can be defined as v :

1 1 1 M M M v = (v (1) , v (2) " , v (3×S ×S ) ," , v (1) , v (2) ," , v (3×S ×S ) )

(8)

i v ( j ) ( j = 1,", 3 × S × S ) describes the energy of every block of three detailed images ( H i Vi Di ), which

Where M is the largest level of wavelet decomposition,

， ，

is decomposed by wavelet. Finally, we normalize

v as follows:

1 M M V = (V(1)1 ,V(2) , ", V(1)M , V(2) , ", V(3* S *S ) )

V(ij ) =

i v ( j ) M 3* S * S

∑∑ k =1

v

k (1)

(i = 1, " , M ; j = 1, " , 3* S * S )

(9) (10)

l =1

According to equation (10), we define the i level wavelet energy feature as follows: i i V i = (V(1)i , V(2) , ", V(3* S *S ) )

(11)

The i level wavelet energy feature which reflects palm-print image in the different scale, location, and different texture features in different directions is calculated by the i level wavelet coefficients. Then we get energy vector by construct every level wavelet energy feature as follows:

V = (V 1 ,V 2 , ",V M )

(12)

The wavelet feature which composed by every level of energy wavelet reflects texture feature of palm-print image at different resolutions, different regions and different directions.

A New Palm-Print Image Feature Extraction Method

43

3 Principal Component Analysis As wavelet feature vector V is high dimensional, so we use principal component analysis to deal with high dimensional features. The key is to find the PCA transform axis. And let’s introduce PCA briefly. Suppose the variable x has N n×1

samples, xk ∈ R (k = 1, 2, " , N ) , We define its mean vectors as covariance matrix can be defined as follows:

C=

1 N

u , then

T

N

∑ ( xk − u )( xk − u ) ∈ R n×n

(13)

k =1

Here we define

Z = [ x1 − u, x2 − u, " , xN − u ] ∈ R n×n

(14)

Then we can get

C= We assume

1 ZZ T N

(15)

Wpca = [ w1 , w2 , " , wn ] , w1 , w2 ,", wn as the eigenvector of C ,

then we can choose the number of dimensions of transformed data we want, if we set the dimension r ,then we get r eigenvectors ( w1 , w2 ," , wr )which correspond r eigenvalues. In this paper, we reduce the dimensions of the training samples’ feature vectors V by using PCA transform, and get Wvpca

= [ wv1 , wv 2 ," , wvr ] in PCA feature space.

The training samples’ feature vectors V are mapped to feature space, eigenvalues of Wvpca can be defined as follows:

avpca = [ av1 , av 2 ," avr ] .

(16)

We use PCA to deal with every level wavelet energy features, and get reduced dimensional

1 2 M V pca i = (V pca , V pca , ", V pca ) , we call V pca i as PWEF .

4 Experiments and Results Analysis We test this feature extraction method on Polyu-Online-Plam-print Database. In the experiment ,we use 200 persons palm-print images ,and choose 9 palm images of every person randomly .To every palm ,we use 6 images as training set ,other 3 images as testing set. We use Haar wavelet to deal with these images samples, and get 5 level wavelet decomposed energy blocks ,every block is divided into 4*4 subblocks, the vector V ’s dimension is 240 ,and then use PCA to deal with every block,

44

J.w. Li and M. Sun

finally ,we get every level’s PWEF ,the dimension of PWEF is 40. We use the mean value of every sample’s PWEF as the corresponding person’s template. In order to investigate the performance of the proposed approach, each sample in the database is matched against the other samples. The matching between palm-prints which are captured from the same palm is defined as a genuine matching. Otherwise, the matching is defined as an impostor matching. A total of 120,000 matching have been performed, in which 600 matching are genuine matching. We do this operation 5 times by using 5 levels PWEF . Figure 3 shows the genuine and impostor matching scores distribution. The scores of the genuine match of the first ,second ,third , forth, fifth level PWEF corresponding concentrates at 15,38,26,23,20, the scores of the impostor match of the first ,second ,third , forth, fifth level PWEF corresponding concentrates at 35,50,58,60,70. These two peaks are clearly separated and the distribution curve of the genuine matching scores intersects very little with that of impostor matching scores. Therefore, the proposed approach can very effectively discriminate between palm-prints.

Fig. 3. The Five Levels

PWEF

Distributions of Genuine and Impostor Matching Scores

In the processing of recognition, we use the nearest neighbor classifier to calculate the distance between testing sample and every training sample. The result of recognition is shown in table 1.

A New Palm-Print Image Feature Extraction Method

45

Table 1. Correct Recognition Rate, Training and Recognition Time

Method

PCA 2DPCA LDA ICA PCA&LDA 1 level PWEF 2 level PWEF 3 level PWEF 4 level PWEF 5 level PWEF

correct recognition rate 93.2% 92.26% 96.7% 95.75% 97.8% 98.45% 95.35% 94.57% 97.67% 99.22%

Training time

recognition time

18.4313s 17.2537s 22.7625s 15.1397s 22.7625s 13.2356s 12.9683s 13.3987s 13.4735s 13.1848s

0.1094s 0.1503s 0.1950s 0.1538s 0.2210s 0.1235s 0.1300s 0.1247s 0.1267s 0.1295s

We use the same training set to test PCA, 2DPCA, LDA, ICA, PCA & LDA and find that recognition accuracy is improved by nearly three percentage points, the time of recognition improved 0.07s averagely. From the experiment of palm-print recognition, we get the ROC in figure 4 which reflects the performance of the proposed feature extraction algorithm.

Fig. 4. The ROC Curve of the Proposed Approach

From the ROC Curve, we can see equal error rate reach about 0.60%, so the recognition accuracy are able to meet our expected accuracy. And it fully illustrated PWEF feature extraction method in palm-print recognition system’s advantage.

5 Conclusion In this paper, we applied the combination of wavelet transform and PCA on palmprint image feature extraction. Firstly, we use edge detection algorithm and corner

46

J.w. Li and M. Sun

detection algorithm to locate the ROI of palm-print, secondly, we transform the ROI with Haar wavelet, thirdly, we use PCA to deal with the transformed ROI image and get the PWEF. Through the recognition experiment, the correct recognition rate increased nearly 3% and recognition time reduced about 0.07s.So the proposed method can describe palm-print texture features, and has a wide application in the field of biometric technology.

References [1] Connie, T.J., Andrew, T.B.: An automated palm-print recognition system; Image and Vision computing, 501–515 (2005) [2] Lu, G., Zhang, D., Wang, K.: Palm-print recognition using eigenpalms features. Pattern Recognition Letters 24(9210), 1463–1467 (2003) [3] Li, Q., Qiu, Z., Sun, D., et al.: Online palm-print identification based on improved 2DPCA. Acta Electronica Sinica 33(10), 1886–1889 (2005) [4] Wang, J., Zhang, Z., Zhang, Y., et al.: A guantitative analysis for complicated spectra based on independent component analysis. Journal of Optoelectronics ·Laser 18(6), 741– 745 (2007) [5] Wu, X., Wang, K., Zhang, D.: A novel approach of palmline extraction. In: Proceedings of Third International Conference on Image and Graphics, pp. 230–233 (2004) [6] Tico, M., Immonen, E., Ramo, P., et al.: Fingerprint recognition using wavelet features. In: Proceedings of 2001 IEEE International Symposium on Circuits and Systems, vol. 2, pp. 21–24 (2001) [7] Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, New York (1999) [8] Mallet, S., Hwan, W.: Singularity detection and processing with wavelets. IEEE Transactions on Information Theory 38(2), 617–643 (1992) [9] Wu, X., Wang, K., Zhang, D.: Wavelet Energy Feature Extraction and Matching for Palm-print Recognition. Journal of Computer Science and Technology 20(5), 411–418 (2005) [10] Wu, X., Wang, K., Zhang, D.: Wavelet based Palm-print Recognition. In: Proceedings of the International Conference on Machine Learning Cybernetics, pp. 1253–1257. IEEE, USA (2002)

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content in Grapevine Leaves with Visible/Red-Infrared Hyperspectral JiangLin Qin1,2,*, Donald Rundquist2, Anatoly Gitelson2, Zongkun Tan1, and Mark Steele2 1

Guangxi Institute of Meteorological Disaster-Reducing Research; No.81, Minzu Avenue, Nanning, Guangxi, P.R. China. 530022 2 Center for Advanced Land Management Information Technologies (CALMIT), University of Nebraska-Lincoln, Lincoln, NE 68583-0973

Abstract. The anthocyanin(Anth) content in leaves provides valuable information about the physiologocal status of plant. Thus, there is a need for accurate, efficient, practical methodologies to estimate this biochemical parameter. Hyperspectral measurement is a means of quickly and nondestructively assessing leaf Anth in situ. Wet chemical methods has traditionally been used for this purpose. Recently, NIR(near-infrared)/green, red/green, anthocyanin reflectance index(ARI), and a modified anthocyanin refelctance index(MARI) was been used to estimate the anthocyanin content. In this paper, a an artificial-intelligence technique model was introduced to establish the relationship between the anthocyanin content and reflectance of 400-750nm spectum, variation of species and growth stages. The objective of this study was to test the overall performance and accuracy of this new nondestructive techniques for estimating Anth content in grapevine leaves. Although Anth in validation data set was widely variable, the new methods were capable of accurate predicting Anth content in grapevine leaves with a root mean square error below 1.65 mg/m2, which is lower than that of MARI or ARI [20]. It documents the facts that such an approach is more suitable for developing simple hand-held field instrumentation for accurate nondestructive Anth estimation and for analyzing digital airborne or satellite imagery to assist in making informed decisions vineyard management. Keywords: Grapes, Hyperspectral, SVM (Support Vector Machine), Anthocyanin.

1 Introduction / Background Very often, in grapevine leaves, significant accumulation of anthocyanin is as a result of a number of environmental stresses such as strong light, UV-B irradiation, low temperature, drought, wounding, bacterial and fungal infections, nitrogen and phosphorus deficiencies, certain herbicides and pollutants. It may serves as indicators of grapevine leaf senescence and stress in grapevine, their detection and quantitative assessment can *

Corresponding author. Tel.: +86 771 5850120; Fax: +86 771 5865594. E-mail address: [email protected]

D. Li, Y. Liu, and Y. Chen (Eds.): CCTA 2010, Part IV, IFIP AICT 347, pp. 47–62, 2011. © IFIP International Federation for Information Processing 2011

48

J. Qin et al.

provide important information about response and adaptation of grapevine to environmental stresses. Anthocyanins have traditionally been determined through wet-chemical methods, including pigment extraction in a solvent,spectrophotometric determination of absorbance to Anth solution, and conversion from measured absorbance to Anth content[5].However, laboratory procedures are laborious, time-consuming, and destructive to leaves. Anthocyanins, Chlorophylls , carotenoids, and other pigments participate in light absorption in particular bands and can readily be assessed with absorption and reflectance spectroscopy. During past few decades, considerable progress in development of nondestructive methods for estimating physiological state of vegetation has been achieved. But much attention was given to the estimation of Chl and Car content, not much is known about Anth estimation, and lots of them are Linear/or simple nonlinear model. Decades ago, multivariable approach[1] is developed by the USDA, but one of its assumption is the relationships between reflectance and chemical concentration are near-linear. Three bands model[6,7,4] is very popular recently year, but it is very simple non-linear model, and it can not cover the variation in species and in growth stages and even in environmental stresses, only Three bands is far from enough to be used. Very recently, NIR(near-infrared)/green, red/green, anthocyanin reflectance index(ARI, based on refelctances in bands within the green and the red-edge regions[5]), and a modified anthocyanin refelctance index(MARI, based on reflectances in green,red edge, and NIR[5,6]) was been used to estimate the anthocyanin content[20]. However, this research will propose a new approach which cover advantages in lots of methods, include Three bands model and multivariable approach, and e.g, it depends on your research on your problem. It can perfectly model the relationship between Anth content and reflectance of spectra, even environmental factors.

2 Purpose of the Paper The objectives of the current study was to investigate the performance of a reflectance-based nondestructive technique to estimate Anth in grape leaves that may contain anthocyanin , chlorophylls and carotenoids, and that may contain the scattering of leaf surface. And specifically to (1) identify the optimal position of spectral bands and their width as they relate to Anth content in grape;(2) identify the position of spectral bands as they relate to chlorophyll, carotenoids content so as to remove the afects of this two pigment in estimating Anth in grape leaves;(3)select near-infrared (NIR) band so as to remove the scattering of the grape leaf internal structure and leaf surface;(4) develop a quantitative model that accurately estimates Anth content in grapevine leaves fully employ hyperspectral reflectance at selected narrow portions of the visible spectrum (400-750 nm); (5) evaluate the performance of the new techniue.

3 Material and Methods 3.1 Selected Grape Cultivars Five grape cultivars were investigated as our research: DeChaunac, Edelweiss, Lacrosse, St.Croix, Stpepin. The red cultivars included DeChaunac and Saint Croix, while the whites were Edelweiss and Lacrosse. DeChaunac, a red French-American hybrid developed by Albert Seibel and introduced into Canada in 1946, is a hybrid of

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content

49

Seibel 5163 and Seibel 793. The vine is vigorous and more disease resistant than other French hybrids, and is cold hardy to temperatures of -26° C (-15° F). Saint Croix, a red Vitis riparia cross between ES-283 and ES-193, was introduced by Elmer Swenson in 1981. Saint Croix is a vigorous vine with known resistance to black rot and is cold hardy to temperatures of -35° C (-32° F). Edelweiss, a white Vitis labrusca cross between Minnesota 78 and Ontario cultivars, was introduced by Wisconsin grape breeder Elmer Swenson in 1980. It is known as a vigorous vine resistant to foliage diseases and is cold hardy to temperatures of -34° C (-30° F). Lacrosse is a white winegrape originated by by Elmer Swenson in Osceola, Wisconsin and introduced in 1983. It is a hybrid of (Minn 78 × Seibel 1000) × Seyval, with a medium sized berry, medium cluster and is very winter hardy. It produces a high quality, fruity, non-labrusca wine and the medium vigor vine has good tolerance to 2,4-D and resistance to disease. Stpepin. 3.2 Field Sampling of Leaves 144 leaves were sampled during seven fields campaigns undertaken on: June 26,2006 to June 28,2007. Individually leaves were selected based upon the variety they were and how healthy they looked. The selected leaves were cut from the canopy, immediately sealed in a plastic bag with a small amount of water, and paced in a cooler with ice. After fields sampling was completed, the leaves were transported to the lab, and areas of homogeneous pigmentation on each were identified and delineated with a permanent marker. In order to facilitate record-keeping, high resolution digital photographs were acquired for each group of sampled leaves using an olympus C-175 4.0 megapixel camera.

Fig. 1. Leaves, as photographed by means of the digital camera, were selected to provide samples of the various cultivars. Measurement areas were delineated on the leaves themselves. These Saint Croix leaves were sampled on July 10, 2007.

50

J. Qin et al.

3.3 Pigment Extraction Analytical extraction of anthocyanins is problematic since the pigment in solution absorbs at 525 nm, the same band used for analytical chlorophyll extraction. To separate absorption by the two pigments, a process of pheophytinization is conducted. The process is performed by acidifying the neutral solution to convert chlorophylls to pheophytins, which while in solution, will absorb at 654 nm and 648 nm. In a neutral solution, anthocyanins are colorless. The acidification causes the red anthocyanin absorption feature to develop. This, in addition to the conversion of chlorophylls to pheophytins, facilitates analytical extraction of anthocyanin. For each leaf sample, two to three 1-cm diameter discs were cut from the marked area. Pigments contents were calculated in a two step procedure. The punches were weighed and ground in 100% methanol using a mortar and pestle. The leaf tissue was ground until the pulp turned white in color and all pigments were suspended in the solution. The resulting homogenate was centrifuged in test tubes for 6 minutes. Absorption spectra of the solution were recorded using a cary spectrophotometer. The cary spectrophotometer was configured to measure absorption of the sample at 1 nm intervals between 400 nm and 800 nm. chlorophylls a and b and carotenoid contents were calculated from the spectra using coefficients described by Lichtenthaler [15]. Once chlorophyll absorption spectra were collected, chlorophylls were converted to pheophytins by adding HCL to each sample to produce a 99.9% methanol 0.1% HCL solution. Absorption spectra of the converted solution were recorded using the cary spectrophotometer and absorption by anthocyanins was calculated using coefficients described by Lichtenthaler [15]. Laboratory analytical anthocyanin of 144 leaves yielded an adequate range of pignment concentrations, ranging from 1.34 mg/m2 to 64.74 mg/ m2 with a mean of 11.71 mg/ m2. The range of Anthocyanin in this study was similar to that observed by Gamon and Surfus[3] . 3.4 Measuring Leaf Reflectance Spectral-reflectance measurements were collected, at leaf level, for each of the five grape cultivars noted above using “leaf clip” with 2.3 mm diameter bifurcated fiber optic attached to both an ocean optics UBS2000 radiometer and to an ocean optics LS-1 tungsten halogen light source. The ocean optics UBS2000 radiometer uses a charged coupled device(CCD) to measure radiance with a spectral resolution of 1.5 nm across 2024 individual spectral channels ranging from 350nm to 1010nm in wavelength. The instrument has a 12 bit radiometric resolution; thus it records levels of reflectance ranging from 0 to 4095. The an ocean optics LS-1 light source uses a regulated power supply and a tungsten halogen filament bulb burning at 3100K to output a steady beam of light with a spectral range between 260 and 2500nm. In this study, the light source was turned on at least 15 minutes prior to scanning to allow the bulb and filament to warm and stabilize. The plastic leaf clip, used to position the ocean optics fiber against individual grape leaves, consisted of a black polyvinyl chloride(PVC) attachment and a bifurcated glass fiber optic with a diameter of 2.3 mm. This fiber optic is transmissive between 400 and 1000 nm and was positioned at a 60° angle relative to the leaf surface in

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content

51

order to minimize the spectral range of the instrument, was held against the abaxial side of the leaf to reduce extraneous reflectance from the reflect light being transmitted through the leaf. The ocean optics radiometer was calibrated prior to each data-collection session using a Labsphere spectral reference panel(North Sutton, NH) with a nominal reflectance of 99% between 250 and 2500nm. First, the reflectance panel was held tightly against the optic, and a spetral scans were taken prior to each data-collection session. The sensor was operated using the CALMIT Data Acquistion Program, which uses a single calibration scan collected at the time nearest to the acquisition of target scans to compute reflectance. To insure that a calibration scan used for reflectance calculation was accurate, at least three additional reflectance-panel scans were recorded for each dataset. The fourth calibration scan was plotted and compared to the plots of the previous three, it was used in subsequent processing. The reflectance spectrum was calculated as a ratio of leaf radiance of the calibration standard at wavelength . A total of 6 reflectance measurements were acquired within the marked homogenous area of each leaf using the ocean optics UBS2000 radiometer. Care was taken to distribute the location of the spectral scans throughout the entire marked area superimposed on the leaf in order to acquire an accurate representation of greenness. No data were taken at leaf edges where stain was present due to ink from the darker. The average of the six scans per sample was calculated in order to establish a single representative reflectance spectrum per leaf. Each spectral profile was numbered to correspond with the leaf from which it was acquired.

4 The Procedure for Developing the Quantitative Model 4.1 To Create Variable Set for the New Model 4.1.1 Confirming Sensitive Spectrum and/or Factors The relationship between the measured anthocyanin content and spectral reflectance at visible wavelengths (400-750 nm) is a complex non-linear form. The first task in developing an appropriate quantitative model to summarize that relationship is to determine which specific wavelengths in the visible spectrum have the greatest sensitivity for detecting subtle changes in anthocyanin content. The sensitivity calculation first makes use of the standard error in the regression of two datasets. In our case, the first dataset was the anthocyanin content associated with the sampled leaves corresponding to each of the five cultivars at every one of 4 growth stages. Each datacollection campaign in the field was considered as a separate growth stage (5 total field campaigns during 2006 and 2007). For the year of 2006, the first stage represents May 26, 2006,19 samples for Edelweiss and 25 samples for StCroix; the second stage represents June 1 and 9,2006, 10 samples for StCroix, Edelweiss, stpepin; the fourth stage represents September 19,2006,22 samples for stcroix. For the year of 2007, the second stage represents June 5, 2007, the third stage represents June 28,2007, every stage includes 6 samples for DeChaunac, Edelweiss, Lacrosse, StCroixper.The anthocyanin content datasets for the five cultivars were regressed against visible spectral reflectance, with the latter interpreted for our study as the wavelength region between 400 and 750 nm. The purpose of the many regression

52

J. Qin et al.

calculations was to select the most useful portions of the visible range for estimating Anthocyanin content. As an illustration of the procedure, consider Fig.2 where the data collected on June 28, 2007 are shown. Notice that the x-axis is the visible spectrum as defined above. The y-axis represents the standard error of the respective calculations associated with all 5 field campaigns. The solid black line represents the result for all samples combined (4 grape cultivars times 6 scans per leaf) for one growth stage. The red-line represents the series of standard errors for the DeChaunac grape cultivar, green represents Edelweiss, turquoise represents Lacrosse, and purple represents Saint Croix. The lowest points along the respective lines indicate the spectral regions or wavelengths where the greatest sensitivity occurs.

Fig. 2. The graphic illustrates the variation in the sensitivity of the various spectral regions to changes in Anth content. The solid black line represents the result for all samples combined (4 grape cultivars 6 times scans per leaf) for one growth stage. The red-line represents the series of standard errors for the DeChaunac grape cultivar, green represents Edelweiss, turquoise represents Lacrosse, and purple represents Saint Croix. The data shown here are from leaves sampled on June 28, 2007.

The next step in the procedure was to calculate the correlations (R) and coefficients of determination (R2) between anthocyanin and reflectance for those locations along the lines with the lowest standard errors. This was done merely to confirm the selection of wavelengths that will be used in the final model. The result of this latter calculation was the selection of fifteen spectral regions for use in the modeling procedure. The fifteen selections include the following (wavelengths in nm): 400.3 to 404.8;450.22 to 453.19;494.61 to 499.38;533.65 to 536.18; 550.62 to 553.14;641.13

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content

53

to 643.55;676.2 to 679.95;682.33 to 686.07;689.8 to 692.51; 696.56 to 699.93; 716.71 to 719.71;724 to 726;721.04 to 722.71;739.94 to 742.57;748 to 750. Next, the reflectances for the individual spectral ranges noted above are integrated by means of averaging these reflectance over the wavelength ranges (e.g., 400.3 to 404.8 nm, which contains band centers at 400.3;400.68;401.06; 401.43; 401.81; 402.19; 402.57; 402.95; 403.32; 403.7; 404.08; i.e., eleven individual channels). The averages became input variables to the developing model, along with two other factors: 1) grapevine cultivar (each cultivar was labeled, and that label, expressed numerically); and 2) growth stage of the vine (i.e., each stage was labeled, and that label, expressed numerically). The procedure was repeated for the other 16 dataacquisition dates. The quantitative model was developed by using the seventeen factors to estimate anthocyanin content. The sensitive channel is also variation between grape species: From the Fig. 2, we find very apparent variation in sensitivity between grape species. St.Croix is much more variable, there are many lower points on its sensitive curve, they are about at 499, 554, 625, 661, 693,750 nm. Normally situation, DeChaunac and Edelweiss is much more sensitive, and Lacrosse is lower sensitive, but it is not always right, we still need to confirm by caculating coefficient r2-value. The lower points is also different between grape species, for example, there is a lower point at 693.52 nm for St.Croix, but that similar lower point for Lacrosse is changed to point 686.75 nm, and for DeChaunac and Edelweiss, there is similar lower point may be changed to 721.71 nm and 737.96 nm. Another example, there is a lower spectral fields about in 625661 nm for St.Croix; but it is changed to spectral fields 652.88 to 686.41 nm for DeChaunac; and it is changed to 532.9 to 640.43 nm for Lacrosse; and there is not apparent similar lower spectral fields for Edelweiss. Then, by detail analysis the datum and figures of every growth stage, we also find significantly different sensitivity between grape species. But unfortunately, if we only pay attention to the rough-black line, e.g if we ignore the situation of the difference between grape species , you shouldn’t get those findings, the result will became worse. The sensitivity of spectral channels is changed and/or shifted according to growthstage. From growth stage of June 5,2007 to that of June 28,2007, the shape of all the sensitive curve is changed a lot. Taken St.Croix and DeChaunac as example, the similar lower points at about 550 and 680 nm is still retained, but the exact point is changed and corresponding sensitivity is also changed. And the shape of all curve is changed. While in June 5,2007, the St.Croix is very sensitive at point 550nm, but DeChaunac is not very sensitive at 550 nm. However, while the growth stage move to June 28, 2007, the most sensitive channel of St.Croix and DeChaunac is changed to point at 693.86 and 506 nm separately. Normally, the sensitivity of separate species is much more high than that of no considering species difference situation: Detail analysis of the datum and graphs of all growth stage shows that in the most situation and/or growth stages, the sensitive curve of separate species is usually under the curve of the rough-black one. It indicates that modeling on separate species will be much more effective, or we should use some kind of non-linear technique and taking consider of species and growth stage difference to get more effective model.

54

J. Qin et al.

4.1.2 Interaction of Anthocyanin, Chlorophylls, Carotenoids and Other Foliar Chemistry in Absorption and Reflectnce Absorption feature are broadened by multiple scattering and often interfere with one another. Organic compounds also absorb in similar wavebands, so that a wavelength is never uniquely related to a chemical. For instance, both of Anth, Chl and Car is absorb at about 550,680 nm, it makes the problem very intractable. From Fig.3a&3b, we can see that in some cases, while the Anth content is very lower, but Chl and/or Car maybe very high. For example, Edelweiss in June 5, 2007, Ant content is 2.36 mg/m2, but Chl and/or Car content is 454.5 and 97.6 mg/ m2 separately. However, the same grape species Edelweiss in May 26, 2006, Ant content is the similar 2.364 mg/ m2, but Chl and/or Car content is 24.49 and 11.22 mg/m2 separately. So the similar Anth content, but Chl and Car content is variable a lot, from 454.5 and 24.49 mg/m2 for Chl and from 24.49 to 11.22 mg/m2 for Car. As for sensitive channel of at 550.62 to 553.14 and 676.2 to 679.95 nm, the reflectance is 11.78 and 3.2 for Edelweiss in June 5, 2007 individually, and is 23.57 and 5.19 for Edelweiss in May 26,2006 separately, it is drastically variable from 11.78 to 23.57 for band of 550.62 to 553.14 nm and from 3.2 to 5.19 for band of 676.2 to 679.95 nm. So, in case of Anth lower content, the reflectance of spectrum maybe drastically variable, and so as for the absorption. The relationship between them is a very complex non-linear form, non-linear adjustment is necessary.

Fig. 3. The graphic illustrates that the reflectance and absorption of the Anth content affects by Chl and Car content. Both of Figures illustrates that in lower Anth content, the Chl and Car variates sharply, especially for the Anth content ≤15mg/ m2.

After researching above in detail, we confirm following sensitive spectrum as part of our modeling factors. There are totally fifteen spectral fields (see above), each spectral field takes the average value of the nearest several channels. Some of them are Chl or Car sensitive spectrum. For instance, 550.62 to 553.14,641.13 to 643.55, 689.8 to 692.51,696.56 to 699.93.So that the affection of Chl and/or Car can be compensated in the new model.

A Non-linear Model of Nondestructive Estimation of Anthocyanin Content

55

4.1.3 Introduce of the Secondary Index We introduce Secondary Index, such as MARI to improve accuracy of the model. Here is the format of MARI MARI λNIR = [1/ρλGreen – 1/ρλRe] ×ρλNIR

(1)

Here we select λGreen as 550.62 to 553.14 nm spectrum, λRe as 696.56 to 699.93 nm spetrum; and λNIR as 721.04 to722.71,739.94 to 742.57,748 to 750 nm separately. So we get three MARI indices, label as MARI721 , MARI739 , MARI750 separately. We found that there is variation on MARI between grapevine species and growth stage. We also select 1/ρλGreen and 1/ρλRe and NIR700/Visible676.2 as the secondary index, totally six factors [6,7,4]. Table 1. r 2-value between Anthocyanin content Vs part of Indices Index r

2

MARI750 0.658

550.62 to553.14 0.2506

696.56 to699.93 0.0155

748 to750 0.5275

1/ρ550.62 to 553.14 0.61

1/ρ696.56 to 699.93 0.0693

Fig. 4. The graphic illustrates that in some case, 3 band model/or MARI can improve the Linear feature between Anth content and original data space ℜm in local fields, e.g. sub- dimension. It can makes a asymptote form of Fig.4b to be a near linear form of Fig.4a. It also makes that the scattering points in Fig.4b shrinks closed to the curve in Fig.4a. Especially in lower Anth content points. The data shown here are from leaves sampled on June 9,2006 of Stpepin.

From Fig.4a&4b which the data is taken from June 9,2006 of Stpepin, we can see that by introducing 3 band model/or MARI as index, the Linear feature between Anth content and original data space ℜm in local fields has been improved; in lower value points of Anth content, the points become much more closed to the curve, e.g. shrunk to the curve(Fig.4a), which shows that Errors will become more little. Those MARI indices will make that the learning procedure converges much faster. By introducing six secondary indices, we have got improvement on Eri (See Eq.(5)) percentage Error, e.g. Eri divides by its measured Anth content. After introducing

56

J. Qin et al.

secondary Indices, the average value of top ten samples of most high Eri percentage Error has been decreased, from 221.77% to 80.73% in our learning and predict-testing procedure, and the sample number of the most high Eri Error percentage (here it is 7 samples which Eri Error percentage 100% in our learning and testing procedure) has also been decreased to 3 samples. Those improvements will be very important in lower value point of Anth content. We can infer that the new model cover advantages in many methods, include MARI, ARI,3 bands model, because we can introduce as many indices as possible into the new model. And by learning procedure, the new model optimizes its accuracy and ability of its generalization or ability of prediction. We can infer that the new model cover advantages in many methods, include MARI, ARI,3 bands model, because we can introduce as many indices as possible into the new model. And by learning procedure, the new model optimizes its accuracy and ability of its generalization or ability of prediction. 4.2 SVM Approach to Establishing Non-linear Model Support Vector Machine is a kind of high-degree non-linear method [25, 10,11], it also possesses good robustness and lots of another good feature, those characteristics make it become one of the most prominent machine learning technique for high dimension sparse data. The principle of the technique is summarized below. Taking measured Anthocyanin content Yi and its corresponding factors <xi1,xi2,…..,xim>T, or we can note factor vector as Xi , it is belong to ℜm, here m = 25 represents factors number, i≤n represents sample number, as a pair dataset, normally it is called as sample. The learning task is defined a fixed array X of n points(X1, X2,…., Xn). Each data point has a desired Yi. For training, the learner receives the labels Yi for a random subset Yl , Yl ⊂ [1, 2, …, n]

⎜ Yl ⎜ = l

(2)

Of l