Quantum ensemble regression using entanglement-aware variational circuits for food crop yield prediction

Crop yield forecasting is critical for agricultural planning, resource distribution, and global food security. Nevertheless, the available literature is largely based on classical machine learning models or isolated simulation methods, which are not always effective at modeling the intricate non-linear processes between climatic variability, soil characteristics, and environmental conditions. To overcome these shortcomings, this study presents a superior predictive model, Entanglement-Aware Variational Quantum Ensemble Regression with Uncertainty-Aware Ensemble Weighting (EA-UAQER). The given approach will combine reference to quantum-inspired ensemble learning and uncertainty-sensitive weighting schemes to improve predictive generalization, stability, and resilience to heterogeneous data regimes. An experimental assessment was conducted using a publicly available Kaggle crop yield dataset with a variety of soil and meteorological characteristics. Computational libraries were developed in Python to train, validate, and assess model performance. The EA-UAQER model produced better results with an Root Mean Squared Error (RMSE) of 0.124, Mean Absolute Error (MAE) of 0.089, R 2 of 0.985, and finally Mean Absolute Percentage Error (MAPE) of 1.84% as compared to other traditional benchmark models such as the Random Forest, Support Vector Machine, and Artificial Neural Network. These findings indicate that the framework can model complex non-linear relationships with lower prediction variance and uncertainty. Through the integration of quantum-inspired optimization with ensemble intelligence, the project will provide a scalable, robust solution for next-generation intelligent agricultural forecasting systems to enable data-driven decision-making and sustainable crop management and food production strategies.