Efficient Detection of Cotton Verticillium Wilt by Combining Satellite Time-Series Data and Multiview UAV Images

As a crucial economic crop, the health status of cotton directly impacts farmers' income and the national economy. Therefore, timely and accurate detection and identification of cotton diseases and pests are of significant importance, aiding in reducing the adverse effects of diseases and pests on cotton yield and quality. The existing research struggles to address the balance between resource consumption and detection accuracy in cotton disease and pest detection. Moreover, diseases and pests often occur beneath the canopy, and the orthorectification of drone imagery may result in insufficient feature information and prolonged processing time, among other issues. To address the aforementioned issues, this article proposes a precise detection method for cotton Verticillium wilt based on unmanned aerial vehicle multiangle remote sensing guided by a satellite time-series monitoring model. Specifically, first, combining Sentinel-1 microwave and Sentinel-2 optical time-series images, we constructed a cotton Verticillium wilt monitoring model based on extreme gradient boosting algorithm to identify areas affected by the disease invasion. Subsequently, after identifying the blocks affected by the disease, we collected multispectral remote sensing data captured from multiple angles by unmanned aerial vehicles and compared different combinations of vegetation indices and bands. Finally, we constructed a precise classification model for cotton Verticillium wilt based on support vector machine radial basis function classification method. The experimental results indicate that the joint microwave and optical time-series monitoring model achieved overall accuracy (OA) of 81.73% and Kappa coefficient of 0.63, meeting the monitoring requirements of the first stage. Based on the SVM with RBF and the optimal band combination, the OA value of the comprehensive image captured at −58° angle reached 96.74%, with Kappa coefficient of 0.93, meeting the requirements of precise classification detection in the second stage.