A Stacking Ensemble Framework Leveraging Synthetic Data for Accurate and Stable Crop Yield Forecasting
Year: 2025
Type: article
Abstract: With the rapid increase in world’s population and changing climate patterns, accurate crop yield forecasting is essential to ensure food security and sustainable agriculture. This study presents a yield prediction framework consisting of Stacking Ensemble Model (SEM) and its Optimized variant (OSEM), which integrates real-world agricultural data with synthetic data generated using the Prophet time-series model. The ensemble comprises Random Forest, XGBoost, Decision Tree, and K-Nearest Neighbors as base learners, with an Extra Trees Regressor as the meta-learner. OSEM incorporates hyperparameter tuning and dimensionality reduction using Singular Value Decomposition to enhance predictive performance and reduce feature noise. Extensive experiments on globally distributed and region-specific crop datasets demonstrate that OSEM consistently outperforms both SEM and state-of-the-art baselines, achievingR2= 0.996 and MAE = 0.185 t/ha. Stability and convergence analysis, supported by Wilcoxon and Friedman tests, further confirm the robustness and reliability of the proposed model for scalable crop yield forecasting.
Source: IEEE Access
Authors: Muhammad Waqar, Yong-Woon Kim, Yung-Cheol Byun
Institution: Jeju National University
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4412605231