基于无人机数码影像的棉叶螨严重度监测

棉叶螨是影响棉花产量和品质的主要虫害之一。为快速、准确、有效地监测棉叶螨发生情况，利用无人机搭载数码相机获取数码影像，并计算多种可见光植被指数作为初选特征因子，然后采用ReliefF-Pearson特征降维方法选取最佳建模特征，分别构建偏最小二乘回归(PLSR)、BP神经网络(BPNN)、随机森林(RF)的棉花冠层叶片叶绿素相对含量(SPAD)值遥感估测模型和棉叶螨严重度遥感估测模型。结果表明，棉叶螨严重度与棉花冠层叶片SPAD值呈显著负相关关系。经过精度评价，确定RF模型具有最佳性能，模型验证的决定系数和均方根误差为0.74、2.13。该研究结果表明利用棉花冠层叶片SPAD值遥感估测模型可准确估测棉叶螨为害情况，为棉叶螨的无损监测和病虫害防治提供参考依据。

Severity monitoring of cotton spider mite based on UAV digital image

Cotton spider mite is one of the main pests affecting cotton yield and quality. In order to quickly, accurately, and effectively monitor the occurrence of cotton spider mite, a digital image was obtained using UAV equipped with a digital camera, and a variety of visible vegetation indexes were calculated as the primary feature factors. Then, the ReliefF-Pearson feature dimensionality reduction method was used to select the best modeling features, consisting of partial least squares regression (PLSR), BP neural network (BPNN), and the random forest (RF) remote sensing estimation model of cotton canopy leaf chlorophyll relative content (SPAD) and remote sensing estimation model of the severity of cotton spider mite. The results showed that there was a significant negative correlation between the severity of cotton spider mites and the SPAD value of cotton canopy leaves. Through accuracy evaluation, it was determined that the RF model had the best performance, whereby the determination coefficient and root mean square error of model verification were 074 and 2.13, respectively. The results showed that the remote sensing estimation model of SPAD value of cotton canopy leaves can accurately estimate the damage of cotton spider mites, and provide a reference basis for non destructive monitoring and pest control of cotton spider mites.