2026 Volume 19 Issue 1 Pages 51-59
This study utilized unmanned aerial vehicle (UAV)-based multispectral remote sensing to estimate the leaf area index (LAI) of cotton under Verticillium wilt stress. Key spectral bands (B12, B9, B8) and vegetation indices—transformed vegetation index (TVI), difference vegetation index (DVI), and enhanced vegetation index (EVI)—were identified as strongly correlated with LAI. A support vector regression (SVR) model utilizing these features achieved the best estimation performance (validation: R2 = 0.877, RMSE = 0.284). Furthermore, a radial basis function kernel support vector machine (SVM-RBF) classifier attained the highest accuracy in mapping canopy parameters (overall accuracy = 94.05 %, Kappa = 0.916). The proposed framework offers a viable technical solution for large-scale, real-time monitoring of cotton Verticillium wilt.
