[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.

Helminthosporium leaf blotch (HLB) is a serious disease of wheat causing yield reduction globally. Usually, HLB disease is controlled by uniform chemical spraying, which is adopted by most farmers. However, increased use of chemical controls have caused agronomic and environmental problems. To solve these problems, an accurate spraying system must be applied. In this case, the disease detection over the whole field can provide decision support information for the spraying machines. The objective of this paper is to evaluate the potential of unmanned aerial vehicle (UAV) remote sensing for HLB detection. In this work, the UAV imagery acquisition and ground investigation were conducted in Central China on April 22th, 2017. Four disease categories (normal, light, medium, and heavy) were established based on different severity degrees. A convolutional neural network (CNN) was proposed for HLB disease classification. The experiments on data preprocessing, classification, and hyper-parameters tuning were conducted. The overall accuracy and standard error of the CNN method was 91.43% and 0.83%, which outperformed other methods in terms of accuracy and stabilization. Especially for the detection of the diseased samples, the CNN method significantly outperformed others. Experimental results showed that the HLB infected areas and healthy areas can be precisely discriminated based on UAV remote sensing data, indicating that UAV remote sensing can be proposed as an efficient tool for HLB disease detection.