基于高光谱数据的棉田虫害鉴别研究
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广东省科技计划项目(2017A020208046,2017B010117010);国家重点研发计划项目(2016YFD0200700);国家发展和改革委2014年北斗卫星导航产业重大应用示范发展专项项目(20142564);广东省教育厅重点平台及科研项目(2015KGJHZ007);广州市科技计划项目(201707010047)


Identification of pests in cotton field based on hyperspectral data
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    目的 快速、准确、无损伤地鉴别棉花虫害类别,以便针对性制定植保施药方案。方法 对棉花叶片高光谱数据进行采集和分析。采用波段范围为350~2 500 nm的FieldSpec®3便携式光谱分析仪,分别获取受蚜虫和红蜘蛛危害的棉花叶片以及正常棉花叶片的高光谱数据。采用K-近邻和SVM算法区分受红蜘蛛和蚜虫侵害的叶片以及正常叶片。为进一步优化虫害识别模型、提高识别精度,利用主成分分析方法(PCA)进行特征降维,并利用网格搜索法进行参数寻优。结果 使用K-近邻算法和SVM算法构建了虫害识别模型,2种模型的识别率分别为86.08%和89.29%;引入PCA进行特征降维并使用网格搜索进行参数寻优后,可以提高虫害识别率,K-近邻算法和SVM算法的识别精度分别达到88.24%和92.16%。结论 利用高光谱数据可以区分受蚜虫和红蜘蛛侵害以及正常的棉花叶片;结合PCA降维和网格搜索法,能够提高识别率且不需要获得具体的特征波段;对于受蚜虫和红蜘蛛侵害以及正常的叶片识别,基于径向基核函数的SVM算法优于K-近邻算法。

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    Objective To identify cotton pests quickly and accurately without destruction,and formulate pertinently a plant protection spraying plan.Method Hyperspectral data of cotton leaves were collected and analyzed. FieldSpec®3 portable spectrum analyzer with a wavelength range of 350-2 500 nm was used to obtain hyperspectral data of cotton leaves including normal leaves and leaves infected by aphids and red spiders. K-nearest neighbor and SVM algorithm were used to distinguish above leaves. In order to further optimize pest identification of the model and improve the recognition accuracy, the principal component analysis method (PCA) was used for feature dimension reduction, and the grid search method was used for parameter optimization.Result The models of pest identification were constructed by K-nearest neighbor algorithm and SVM algorithm,and recognition rates of two models were 86.08% and 89.29% respectively. Recognition rate increased after introducing PCA for feature dimension reduction and using grid search for parameter optimization. The recognition accuracies of K-nearest neighbor algorithm and SVM algorithm reached 88.24% and 92.16% respectively.Conclusion Hyperspectral data can be used to distinguish aphid or red spider-infected leaves and normal cotton leaves. Using PCA dimensionality reduction and grid search method, the recognition rate can increase without obtaining specific characteristic bands. For identifying aphid- or red spider-infected leaves and normal leaves, SVM algorithm based on radial basis kernel function is better than K-nearest neighbor algorithm.

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王小龙,邓继忠,黄华盛,邓宇森,蒋统统,钟兆基,张亚莉,文晟.基于高光谱数据的棉田虫害鉴别研究[J].华南农业大学学报,2019,40(3):97-103

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  • 收稿日期:2018-07-25
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  • 在线发布日期: 2019-04-23