Deep Feature Fusion with Vegetation Indices for Wheat Lodging Monitoring Using UAV Multi-Spectral Imagery

Lodging is a major agricultural hazard that can substantially reduce crop yields. Timely and accurate monitoring of winter wheat lodging is important for assessing potential yield losses, guiding field management, and mitigating further lodging damage. Recent advances in unmanned aerial vehicle (UAV) remote sensing and artificial intelligence have provided new opportunities for lodging assessment. In this study, a novel monitoring framework was proposed by integrating deep features extracted from UAV multi-spectral images with machine learning algorithms. Sensitivity analysis was conducted to identify vegetation indices (VIs), which are highly correlated with lodging. These sensitive VIs were combined with original multi-spectral bands, and YOLOv8, YOLO12, SAM1, and SAM2 were used for feature extraction. The SHAP method was applied to analyze feature importance and model interpretability. The results indicated that VARI, EXG, and MCARI were the most effective VIs for lodging monitoring. Furthermore, three feature representations, including a spectral feature set, deep features, and fused features, were evaluated. The highest accuracy was achieved using YOLO12 deep features combined with a BP classifier, reaching an accuracy of 98.20%, a precision of 98.38%, a recall of 98.56%, and an F1-score of 98.56%. Overall, incorporating deep features significantly improved monitoring performance. The proposed framework provides an accurate and effective approach for crop lodging monitoring using UAV multi-spectral imagery.