Optimizing Agricultural Productivity: Machine Learning Approaches for Enhanced Crop Yield Forecasting

The primary aim of the paper is to predict crop yield through the use of data mining techniques with an emphasis on climatic and soil parameters from 2010 to 2020. A dataset of potato crop yields in North Gujarat was taken up for research to discuss the relationship between environmental variables, such as temperature, rainfall, and humidity, and soil nutrients like pH, nitrogen, phosphorus, and potassium which influence productivity patterns. Several predictive modeling methods were evaluated, including decision trees, random forests, and support vector machine algorithms. Random Forest, and Gradient Boosting, were applied to determine the significance of such factors in generating variation. Feature selection methods demonstrated that temperature, rainfall, and soil nitrogen are the most important predictors for crop yield, thereby confirming the existing literature on climatic and soil health effects influencing agriculture.This peaked with the RF model at an R-squared of 0.87, showing that it could capture the complex, nonlinear relationships among yield and environmental conditions. The general insight that has characterized the integration of climatic and soil information into predictive models is of utmost significance for actionable insights in precision agriculture. This study will be useful for decisions in agriculture by pinpointing some essential environmental predictors for crop management strategies and optimization of resources use, hence sustainability in agricultural practice.