A Comprehensive Study on Multi-Factor Crop Yield Prediction

This study investigates the complex dynamics affecting crop production prediction using a multi-faceted method that incorporates temperature, rainfall, and pesticide application. With the use of cutting-edge machine learning models including S VR, Random Forest, Lasso, and XGBoost, the study carefully examines how these variables affect agricultural productivity. The yield-to-rainfall ratio is a new statistic established to measure agricultural production efficiency in relation to average rainfall patterns. The generated models are rigorously optimized using grid search approaches, which demonstrate their strong predictive power. The study reveals the complex links between crop yields, climatic conditions, and pesticides through detailed visualizations and correlation analyses. Among the regression models, the XGBoost model performs better than the others and is found to be the best predictor. The findings provide useful insights for maximizing crop output in a range of environmental circumstances and greatly advance our understanding of important factors influencing agricultural productivity. A fresh viewpoint on crop efficiency is offered by the yield-to-rainfall ratio, which makes it easier to identify crops and areas with the highest productivity.