A Robust Machine Learning Framework for Estimating Crop Production Using Ensemble and Quantile Techniques

This study proposes a novel data-driven approach for crop yield prediction by integrating Extreme Gradient Boosting (XGBoost) with Quantile Regression. The comprehensive dataset includes multiple environmental and soil attributes (rainfall, effective accumulated temperature, and multi-layer soil parameters), as well as management factors such as conservation tillage practices, yield, and emergence rates. Additionally, creative computing enriches feature engineering to uncover emergent patterns. By leveraging XGBoost's robust ensemble learning capability, the model efficiently captures complex interactions among climate, soil, and farming techniques, while Quantile Regression refines yield forecasts by providing interval estimates that quantify predictive uncertainty. To evaluate performance, more innovative measures were supplemented including the Continuous Ranked Probability Score, to assess predictive distribution quality. Additionally, the Prediction Interval Coverage Probability was calculated to determine interval reliability, with improved calibration reflecting the model's capacity to address nonlinearities. Experimental results show that this hybrid framework significantly reduces forecast errors and provides well-calibrated uncertainty bounds, outperforming traditional regression approaches. Moreover, interval-based predictions help decision-makers evaluate risks under dynamic environmental conditions. The proposed system thus represents a valuable decision-support tool for optimizing agricultural practices in resource-limited settings. Further enhancements may be achieved by integrating real-time data streams and advanced neural network architectures to improve spatiotemporal modeling and adaptability. This research contributes to precision agriculture by providing a scalable, high-accuracy predictive framework that informs farmers and policymakers in developing more effective strategies for crop management, resource allocation, and food security in volatile agricultural environments.