基于导数光谱与主成分分析的小麦籽粒赤霉病识别  

Recognition of wheat grain scab based on derivative spectrum and principal component analysis

作　　者：琚书存[1,2] 汪志存 林芬芳 谷春艳 潘正高 张东彦[2] JU Shucun;WANG Zhicun;LIN Fenfang;GU Chunyan;PAN Zhenggao;ZHANG Dongyan(Anhui Rural Comprehensive Economic Information Center,Hefei 230031,China;National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University,Hefei 230601,China;School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;Institute of Plant Protection and Agricultural Product Quality and Safety,Anhui Academy of Agricultural Sciences,Hefei 230031,China;School of Informatics and Engineering,Suzhou University,Suzhou 234000,China)

机构地区：[1]安徽省农村综合经济信息中心,安徽合肥230031 [2]安徽大学农业生态大数据分析与应用技术国家地方联合工程研究中心,安徽合肥230601 [3]南京信息工程大学遥感与测绘工程学院,江苏南京210044 [4]安徽省农业科学院植物保护与农产品质量安全研究所,安徽合肥230031 [5]宿州学院信息工程学院,安徽宿州234000

出　　处：《安徽大学学报（自然科学版）》2020年第6期43-50,共8页Journal of Anhui University(Natural Science Edition)

基　　金：安徽省科技重大专项(18030701209);安徽省科技攻关项目-农业领域(1804a07020124);农业生态大数据分析与应用技术国家地方联合工程研究中心开放课题(AE2018010)。

摘　　要：赤霉病是威胁我国食品安全的主要病害之一,为了给粮食加工和作物育种提供技术支持,迫切需要探索小麦籽粒赤霉病的识别方法.利用高光谱成像仪扫描健康和感染赤霉病的小麦籽粒获取高光谱图像.使用图像处理技术分离籽粒和背景后,用多元散射校正、1阶导数和2阶导数对光谱数据进行预处理.利用主成分分析提取小麦籽粒的光谱特征.使用不同模型对籽粒进行识别,比较多种处理组合的评估指标.结果表明:1阶导数—主成分分析—BP神经网络组合的总体分类精度最高,其值为91.67%.Fusarium head blight is one of the main diseases threatening China’s food safety.In order to provide technical support for grain processing and crop breeding,it is urgent to explore the rapid detection method of wheat grain scab.Hyperspectral imager was used to scan healthy and fusarium-infected wheat grain to obtain high-spectral images.After separating the grain and background based on image processing technology,the spectral data was processed by multivariate scattering correction(MSC)technique,first derivative spectrometry and second derivative spectrometry.Principal component analysis(PCA)was used to extract the features of wheat grain.Different models were used to recognize wheat grains.And the evaluation indicators of a combination of different process methods were compared with each other.The results showed that the combination of first derivative spectrometry,PCA and back propagation neural network(BPNN)had the highest overall accuracy which was 91.67%.

关 键 词：小麦籽粒 赤霉病 导数光谱法 主成分分析 

分 类 号：O433[机械工程—光学工程]