Utilizing interpretable machine learning algorithms and multiple features from multi-temporal Sentinel-2 imagery for predicting wheat fusarium head blight

Wheat Fusarium head blight (FHB) severely affects wheat yields, and predicting its occurrence and spatial distribution is essential for safeguarding crop production. This study presents an interpretable machine learning method designed to predict FHB by leveraging multi-temporal and multi-feature information obtained from Sentinel-2 imagery. During the regreening and grain-filling stages, we extracted vegetation indices (VIs), texture features (TFs), and color indices (CIs). Single-temporal features were derived from the grain-filling stage, while multi-temporal features combined data from grain-filling and regreening stages. The synthetic minority over-sampling technique (SMOTE) was employed to adjust the class imbalance, while the most significant characteristics were found using the sequential forward selection (SFS) approach. The extreme gradient boosting (XGBoost) model, optimized using simulated annealing (SA) algorithm and explained via SHapley Additive exPlanation (SHAP) method, integrated VIs, TFs, and CIs as input features. The presented model demonstrated exceptional results, achieving a prediction accuracy of 89.9 % across multi-temporal and a Kappa coefficient of 0.797. It outperformed random forests (RF), backpropagation neural networks (BPNN), and support vector machines (SVM) model. This study indicates that an interpretable machine learning approach, which utilizes both multi-temporal and multi-feature data, is effective in forecasting FHB, thereby providing a valuable tool for agricultural management and disease prevention strategies. • Multi-temporal and multi-feature data are to develop an FHB prediction model. • SMOTE is applied to stabilize the unequal learning data. • Simulated annealing algorithm is to optimize the XGBoost hyperparameters. • SHAP is used to enhance the model's interpretability.