竞争性自适应重加权算法和相关系数法提取特征波长检测番茄叶片真菌病害  

Detection of Fungal Disease on Tomato Leaves with Competitive Adaptive Reweighted Sampling and Correlation Analysis Methods

作　　者：王海龙[1] 杨国国 张瑜[2] 鲍一丹[1] 何勇[1] WANG Hai-long YANG Guo-guo ZHANG Yu BAO Yi-dan HE Yong(College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China Zhejiang Technology Institute of Economy, Hangzhou 310018, China)

机构地区：[1]浙江大学生物系统工程与食品科学学院,浙江杭州310058 [2]浙江经济职业技术学院,浙江杭州310018

出　　处：《光谱学与光谱分析》2017年第7期2115-2119,共5页Spectroscopy and Spectral Analysis

基　　金：教育部高校博士点基金项目(20130101110104);教育部留学回国人员科研启动基金项目;国家自然科学基金项目(31471417);国家(863计划)课题项目(2013AA102301);中央高校基本科研业务费专项资金项目(2014FZA6005)资助

摘　　要：基于竞争性自适应重加权算法(CARS)和相关系数法(CA)特征波长选择方法,提出了利用可见-近红外高光谱成像技术检测番茄叶片灰霉病的方法。首先获取380~1 023nm波段范围内80个染病和80个健康番茄叶片的高光谱图像,然后提取染病和健康叶片感兴趣区域(ROI)的光谱反射率值,作为番茄叶片灰霉病鉴别模型的输入来建立支持向量机(SVM)鉴别模型,训练集和验证集的鉴别率都是100%。研究进一步通过CARS和CA提取特征波长,分别得到5个(554,694,696,738和880nm)和4个(527,555,571和633nm)特征波长,然后分别建立CARS-SVM和CA-SVM鉴别模型。结果显示,CARS-SVM模型中训练集和验证集的鉴别率都是100%,CA-SVM模型中训练集和验证集的鉴别率分别是91.59%和92.45%。以上结果说明了从可见-近红外高光谱图像中提取的光谱反射率值用于检测番茄叶片的灰霉病是可行的。Detection of grey mold on tomato leaves using hyperspectral imaging technique based on competitive adaptive reweighted sampling（CARS）and correlation analysis werestudied in this paper.Hyperspectral images of eighty healthy and eighty infected tomato leaves were captured with hyperspectral imaging systemin the spectral region of 380~1 023 nm.Spectral reflectanceof region of interest（ROI）from corrected hyperspectral image was extracted with ENVI 4.7software.The support vector machine（SVM）model was established based on full spectral wavelengths.It obtained a good result with the discriminated accuracy of 100%in both training and testing sets.Two novel wavelength selection methods named CARS and CA were carried out to select effective wavelengths,respectively.Five wavelengths（554,694,696,738 and 880nm）and four wavelengths（527,555,571 and 633nm）were obtained.Then,CARS-SVM and CA-SVM models were established based on the new wavelengths.CARS-SVM modelobtained good results with the discriminated accuracy of 100%in both training and testing sets.CASVM modelalso performed well with the discriminated accuracy of 91.59%in the trainingset and 92.45%in thetesting set.It demonstrated that hyperspectral imaging technique can be used for detecton of grey mold disease on tomato leaves.

关 键 词：高光谱成像技术 竞争性自适应重加权算法 相关系数法 支持向量机 番茄 灰霉病 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]