Remote Sensing Imagery for Crop Yield Prediction Using Confidence-Weighted Ensemble Model with Explainable AI

1. Developed a Confidence-Weighted Ensemble model that outperformed Random Forest and XGBoost.

2. Engineered novel spectral-agronomic interaction terms, with the stress-health ratio identified as the most impactful predictor.

3. Achieved 86.46% prediction accuracy while maintaining model explainability—a critical bridge between high performance and actionable farming insights.