赵川源,何东健,LEE Won Suk.柑橘黑斑病反射光谱特性与染病果实检测方法研究[J].农业机械学报,2017,48(5):356-362,355.
ZHAO Chuanyuan,HE Dongjian,LEE Won Suk.Reflectance Spectral Characteristics of Black Spot Disease and Disease Detection Method for Citrus[J].Transactions of the Chinese Society for Agricultural Machinery,2017,48(5):356-362,355.
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柑橘黑斑病反射光谱特性与染病果实检测方法研究   [下载全文]
Reflectance Spectral Characteristics of Black Spot Disease and Disease Detection Method for Citrus   [Download Pdf][in English]
投稿时间:2017-02-13  
DOI:10.6041/j.issn.1000-1298.2017.05.045
中文关键词:  柑橘黑斑病  光谱特性  特征选择  识别模型  主成分分析
基金项目:国家高技术研究发展计划(863计划)项目(2013AA102304)和国家自然科学基金项目(61473237)
作者单位
赵川源 西北农林科技大学 
何东健 西北农林科技大学 
LEE Won Suk 佛罗里达大学 
中文摘要:通过对光谱仪采集的340~1030nm柑橘健康与感染黑斑区域光谱进行分析,在探明健康和黑斑病不同症状光谱特性的基础上,提出主成分分析结合特征排序的方法,选择出可识别染病与健康样本的最优波长(525nm)建立SMO分类模型;基于序列浮动前向选择方法优选出4个特征波长(678、740、794、879nm),建立C4.5算法识别柑橘黑斑病3种症状的方法。试验结果表明,用525nm波长建立的SMO分类模型对健康和染病果样本的识别率达99.37%,硬斑型、破裂型和黑斑型症状的识别率分别为81.85%、71.88%和67.57%, 3种症状的平均识别率为73.77%,比前人方法提高了12.77个百分点。
ZHAO Chuanyuan  HE Dongjian  LEE Won Suk
Northwest A&F University,Northwest A&F University and University of Florida
Key Words:citrus black spot disease  spectral characteristics  feature selection  identification model  principal components analysis
Abstract:Citrus black spot (CBS), which is one of the most common fungal diseases of citrus, causes lesions on the rind and early fruit drop before its mature stage. This disease can significantly reduce crop yield, making blemished fruit unsuitable for market. The objective of this research was to study the reflectance spectral characteristics of healthy and infected citrus fruits to identify diseased fruit from healthy ones. A portable USB2000+spectrometer was used to acquire spectral reflectance of citrus fruit in the laboratory with wavelength ranged from 340nm to 1030nm. However, the spectra contained thousands of wavelengths, and many of them would be considered as redundant, which may even decrease the classification accuracy. To reduce the data dimensionality and select the useful bands for further application, principal components analysis (PCA) and four band ranking methods, i.e., T-test, Kullback—Leibler distance, Chernoff bound and receiver operating characteristic (ROC) were applied. One important wavelength (525nm) was selected and used to classify healthy and CBS infected fruits. Sequential minimal optimization (SMO), radical basis function network (RBF), and C4.5 classification methods were used to evaluate the performance of the selected band, and SMO achieved the highest accuracy of 99.37%. In order to compare the performance of classification accuracies according to optimal wavelengths selected by using different methods, two other methods, i.e., sequential floating forward selection (SFFS) and mutual information (MI), were applied. Wavelengths of 527nm and 917nm were selected based on SFFS, while the MI method selected 513~531nm as the optimal wavelength range, and the highest recognition accuracy was 99.06%, which was lower than that of using 525nm. Then SFFS was applied to find the optimal wavelengths for further distinguishing three CBS symptoms. C4.5 method was used to evaluate the performance of distinguishing CBS infected and healthy fruits based on selected wavelengths, and the highest overall classification accuracy was 73.77%.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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