Detection of Pine Wilt Disease Using an Explainable Recognition Model Based on Fusion of Vegetation Indices and Texture Features from UAV Multispectral Imagery

Abstract

Highlights: What are the main findings? Fusing vegetation indices (VIs) and texture features (TFs) as input features significantly improved the accuracy of pine wilt disease (PWD) infection stage identification. SHAP analysis identified VARI, PSRI, DVI, ARI, NDRE, and NIR-M as core discriminative features, where VIs were more effective in distinguishing pre-visual to late infection stages, while TFs excelled at differentiating healthy and dead pine trees. What are the implications of the main findings? Confirmed the feasibility of low-cost, high-precision disease identification based on UAV multispectral imagery. The interpretable machine learning framework combining VI-TF fusion and SHAP analysis addresses the “black-box” limitation of traditional models, providing a reliable technical solution for precise PWD monitoring. Pine Wilt Disease (PWD) is a global destructive forest disease. It poses a serious threat to ecological security and forestry economy, and early detection of PWD is crucial for its prevention and control. Most current studies on identifying infected pine trees based on multispectral data only rely on Vegetation Indices (VIs). They fail to fully explore the role of Texture Features (TFs) in disease identification. Furthermore, existing models generally lack interpretability. To address these issues, this study proposes a machine learning classification framework integrating VIs and TFs. It also introduces the SHAP algorithm to clarify the contribution of key features to classification decisions. The results show that the method using fused VIs and TFs as input features performs significantly better than using single features. Among the four models evaluated, LGBM achieved the best performance (OA: 0.897, Macro-F1: 0.895), followed by LR (OA: 0.818, Macro-F1: 0.809), RF (OA: 0.790, Macro-F1: 0.786), and SVM (OA: 0.770, Macro-F1: 0.787) when using fused VIs-TFs. SHAP analysis further reveals that VIs such as Vegetation Atmospherically Resistant Index (VARI), Plant Senescence Reflectance Index (PSRI), Difference Vegetation Index (DVI), Anthocyanin Reflectance Index (ARI), and Normalized Difference Red Edge Index (NDRE), as well as TFs like NIR-Mean (NIR-M), play a dominant role in identifying disease stages. Among the VIs, VARI demonstrated the highest contribution, while NIR-M showed the most significant contribution among TFs. Specifically, VIs are more advantageous in distinguishing the pre-visual, early, middle, and late stages. In contrast, TFs contributed more to identifying healthy and dead trees. This study confirms that fusing VIs and TFs can effectively complement the physiological and structural information of pine canopies. Combined with the interpretable LGBM model, it provides a new technical path for the accurate monitoring of PWD.