Walnut leaf necrosis causes leaf desiccation and premature abscission, substantially reducing photosynthetic efficiency, impairing fruit development, and ultimately leading to yield loss and quality deterioration. In severe cases, it accelerates branch senescence or even whole-tree mortality, resulting in considerable economic damage to the walnut industry. Rapid and accurate monitoring of this disease is therefore essential for sustainable production. This study aimed to characterize the different stages of walnut leaf necrosis using spectral analysis and develop classification models for stage-specific identification. Leaf samples representing healthy leaves and the early, middle, and late stages of necrosis were analyzed for spectral responses. Sensitive bands were identified using the variable importance in projection (VIP), successive projections algorithm (SPA), and the combined VIP-SPA method, and corresponding vegetation indices were constructed. The selected features were incorporated into classification models based on random forest (RF), extreme gradient boosting (XGBoost), and convolutional neural networks (CNNs). Results revealed that the red-edge (640–700 nm) and near-infrared (720–1000 nm) regions were identified as key diagnostic spectral ranges. Among the vegetation indices evaluated, the Simple Ratio Index (SRI) calculated from reflectance at 705.7 nm and 707.1 nm, the Normalized Difference Index (NDI) using the same band pair, and the Difference Index (DI) derived from 417.1 nm and 638.7 nm emerged as the most sensitive indicators of disease severity. Classification accuracies for different necrosis stages reached 0.9583, 0.9583, and 0.9333, respectively. These findings demonstrate that the identified spectral bands and vegetation indices provide robust tools for monitoring the progression of walnut leaf necrosis.
Si et al. (Tue,) studied this question.