Research on the classification model of rubber leaf powdery mildew disease severity based on hyperspectral multi-dimensional feature fusion.

Rubber powdery mildew, caused by the fungal pathogen Oidium heveae Steinm., is a prevalent disease in rubber plantation regions worldwide. This disease significantly impacts the growth and yield of rubber trees, leading to substantial economic losses within the rubber industry. In recent years, due to climate change and adjustments in planting structures, both the geographical spread and severity of the disease have increased. Consequently, there is an urgent need to develop efficient remote sensing monitoring methods for early warning and effective management. To fully exploit disease information within hyperspectral data, this study first extracted spectral features using three methods: spectral mathematical transformations (MT), continuous wavelet transformation (CWT), and vegetation indices (VIs). Subsequently, correlation analysis (CA), least absolute shrinkage and selection operator (LASSO), and principal component analysis (PCA) were employed to select optimal features from each set, resulting in the construction of nine independent basic feature sets. To further enhance model performance, features selected by these three strategies (CA, LASSO, and PCA) were combined to form three fused feature sets. Finally, all basic and fused feature sets were input into a Random Forest (RF) model to evaluate the impact of different feature combinations on the accuracy of disease severity classification. The results revealed that, among the spectral data processing methods, CWT performed the best. Among the feature selection methods, PCA was the most effective. The feature fusion methods significantly improved model performance. Specifically, the fused feature set based on PCA selection (PCA_ALL) achieved the highest classification accuracy, with an overall accuracy (OA) of 98.89% and a Kappa coefficient of 0.98. This OA was 8.89% higher than that of CA_ALL and 4.42% higher than the best-performing basic feature set (PCA_CWT). This study establishes a remote sensing monitoring framework for classifying rubber leaf powdery mildew severity based on the fusion of multi-dimensional hyperspectral features. This framework not only lays a technical foundation for the transition of the natural rubber industry from experience-based control to intelligent decision-making but also provides crucial parameters for large-scale dynamic disease monitoring using UAV and satellite platforms.