Optimizing the estimation of cotton leaf SPAD and LAI values via UAV multispectral imagery and LASSO regression

• A comprehensive correlation analysis integrating vegetation indices and texture features extracted from UAV multispectral imagery was conducted to investigate their relationship with canopy SPAD and LAI, and LASSO regression was employed to identify the most informative variables for inversion modeling. • Estimation models of SPAD and LAI were constructed using machine learning algorithms including Least Squares Method (LSM), Grey Wolf Optimizer–Support Vector Machine (GWO-SVM), and Ant Colony Optimization–Random Forest (ACO-RF), offering a robust approach for quantifying physiological responses of cotton to Verticillium wilt stress. • By leveraging multi-source feature fusion and nonlinear ensemble modeling, the proposed framework achieved high-accuracy estimations of SPAD and LAI, demonstrating the advantages of synergistic modeling for precision monitoring in disease-affected cotton fields. Cotton (Gossypium spp.) is a vital economic crop both globally and particularly in Xinjiang, China, where its growth status is closely linked to chlorophyll content and leaf area index (LAI). Chlorophyll content is commonly measured using the soil plant analysis development (SPAD) value. This study employed multispectral remote sensing data collected by a DJI Mavic 3M unmanned aerial vehicle (UAV) to investigate the spectral responses of canopy SPAD and LAI in cotton fields affected by Verticillium wilt in southern Xinjiang. SPAD was strongly negatively correlated with the red band (r = –0.784) and positively correlated with the red-edge (REG) band (r = 0.498), while LAI showed the strongest correlation with the near-infrared (NIR) band (r = 0.673) and a moderate correlation with the REG band (r = 0.435). Among various vegetation indices (VIs), the photochemical reflectance ratio (PPR) exhibited the highest correlation with SPAD (r = 0.84), and the excess green (EXG) index showed the strongest correlation with LAI (r = 0.92). Inversion accuracy was highest during the boll stage. The least squares method (LSM) achieved coefficient of determination ( R 2 ) values of 0.58 for SPAD and 0.57 for LAI, while combining VIs and texture features through least absolute shrinkage and selection operator (LASSO) regression improved accuracy to 0.711 and 0.751, respectively. Comparative modeling using LSM, grey wolf optimizer–support vector machine (GWO-SVM), and ant colony optimization–random forest (ACO-RF) revealed that ACO-RF consistently outperformed the other models, particularly in capturing nonlinear relationships and multi-feature interactions. The ACO-RF model achieved R 2 values of 0.898 (root mean square error, RMSE = 1.523) for SPAD and 0.893 (RMSE = 3.308) for LAI. These findings demonstrate that integrating spectral and textural features with optimized machine learning models can significantly enhance the accuracy, scalability, and cost-effectiveness of Verticillium wilt monitoring in cotton, thereby supporting early disease detection and precision agricultural management.