Precision agriculture, as well as data-driven decision-making, requires accurate and interpretable crop yield prediction. Based on a large set of field and weatherrelated variables originating from several seasons, we compare various advanced ensemble learning models such as CatBoost, XGBoost, or Gradient Boosting. Hyperparameter tuning was performed by optimizing each model, ensuring that the model produced the best prediction performance. On the test set, CatBoost models constructed in this way achieve outstanding performance, outperforming XGBoost and Gradient Boosting with the coefficient of determination (0.9790), root mean square error (3.7054), and mean absolute error (2.3254), respectively. For an explainable knowledge, SHapley Additive exPlanations (SHAP) was used to show the significant environmental and agronomic factors that could influence model predictions. These findings demonstrate that the system not only can generate panicle stage predictions with state-of-the-art accuracy, but most importantly, also provides interpretable and actionabledemand solutions to agronomic practice (e.g., for ease of application in crop management), which is an important weapon for sustainable agriculture.