Modeling Temporal Resistance Assessment of Cotton to Verticillium Wilt Using Airborne Hyperspectral Data and Disease Progression Rates

Verticillium wilt (VW) is a soil-borne disease that threatens cotton growth and yield. Resistance assessment is crucial for breeding VW-resistant varieties. Drone remote sensing technology has been used to achieve high-throughput resistance evaluation based on late-stage disease severity. However, the timing and progression of disease onset vary considerably among varieties with different resistance levels, and current methods do not adequately address the influence of the disease development rate during the early stage, making it difficult to systematically assess variety resistance levels. We employed temporal differential feature analysis methods (Cohen’s d and Sequential Backward Selection), combined with dynamic development rates, to identify hyperspectral characteristics that indicate the dynamic responses of resistant cotton varieties during VW disease progression. We observed that the bottom-up development characteristics of VW resulted in challenges in early-stage disease evaluation, as symptoms were not apparent on the upper leaves. After incorporating evaluation features that combined the dynamic disease development rate, the accuracy of the evaluation model during the early stage of the disease significantly improved, with a precision of 100%, a recall of 66.7%, and an F1-Score of 80%, effectively distinguishing varieties with different resistance levels. This study presents an efficient and accurate screening method for assessing VW resistance in cotton, thereby establishing a reliable foundation for informed disease-resistant breeding strategies.