Comparative Analysis of Machine Learning and Deep Learning Approaches for Crop Yield Prediction with Ensemble Learning and Hyperparameter Optimization

Abstract

Accurate crop yield prediction is essential for improving agricultural productivity, ensuring food security, and enabling data-driven decision-making in precision agriculture. Traditional agricultural prediction systems primarily rely on conventional machine learning approaches and statistical methods, which often exhibit limited predictive capability when handling heterogeneous agricultural datasets and complex non-linear relationships among environmental and agricultural variables. Furthermore, many existing systems lack computational validation, ensemble learning integration, and optimization mechanisms, thereby limiting prediction reliability and generalization capability.

This study presents a comparative analysis of crop yield prediction methodologies developed using multiple Machine Learning (ML) and Deep Learning (DL) techniques and evaluates their effectiveness against existing agricultural prediction systems. Initially, a computational framework was developed using multiple predictive models, including Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, Support Vector Regressor (SVR), and Artificial Neural Network (ANN). Experimental findings demonstrated that ANN achieved superior predictive capability with an R² score of 0.94 and RMSE of 227.99, outperforming conventional machine learning approaches.

To further improve prediction performance, stacking and hybrid ensemble methods integrating ML and DL models were implemented. The Hybrid Ensemble (ANN + Stacking) model demonstrated improved predictive performance with an R² score of 0.94 and RMSE of 220.54, indicating enhanced robustness and reduced prediction error compared to standalone models. Additionally, hyperparameter optimization techniques, including GridSearchCV and RandomizedSearchCV, were employed to optimize predictive model parameters. The optimized ANN model achieved the highest performance with an R² score of 0.95 and significantly reduced RMSE of 4.79, demonstrating substantial improvement in predictive accuracy and model generalization.

Comparative analysis with existing literature revealed that the proposed framework significantly outperformed conventional agricultural prediction systems by integrating systematic preprocessing, multiple ML and DL models, ensemble learning, and hyperparameter optimization. The findings validate that advanced computational methodologies substantially enhance crop yield prediction performance and contribute toward intelligent agricultural decision-support systems for farmers, agronomists, and policymakers.