Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images
Qiong Zheng, Wenjiang Huang, Huichun Ye, Yingying Dong, Yue Shi, Shuisen Chen
Abstract
Yellow rust is the most extensive disease in wheat cultivation, seriously affecting crop quality and yield. This study proposes sensitive wavelet features (WFs) for wheat yellow rust monitoring based on unmanned aerial vehicle hyperspectral imagery of different infestation stages [26 days after inoculation (26 DAI) and 42 DAI]. Furthermore, we evaluated the monitoring ability of WFs and vegetation indices on wheat yellow rust through linear discriminant analysis and support vector machine (SVM) classification frameworks in different infestation stages, respectively. The results show that WFs-SVM have promising potential for wheat yellow rust monitoring in both the 26 DAI and 42 DAI stages.