A Comparative Analysis of Different Machine Learning Approaches for Crop Yield Prediction

Crop yield prediction plays a pivotal role in agricultural planning and decision-making, influencing farmers, policymakers, and supply chain stakeholders. This paper conducts a comparative analysis of machine learning techniques for crop yield forecasting, emphasizing the accuracy, resilience, and adaptability of each approach to varying crop types and environmental conditions. A novel integration of machine learning and grid search hyperparameter optimization is proposed, utilizing an extensive historical dataset. Key algorithms, including Random Forest and Support Vector Machines, are applied, with rigorous data preprocessing to address missing values and outliers. Through evaluation metrics like Mean Absolute Error and R-squared, we assess model effectiveness, with Random Forest emerging as the top performer. This study's contributions lie in enhancing predictive accuracy and providing actionable insights for precision agriculture. Experiment result shows that Random Forest and XGBoost regressors stand out as strong performers for this dataset, with the Random Forest model achieving the highest score of 97%.