Enhancing Crop Yield Prediction: A Data-Driven Comparison of Regression Models in Agricultural Analytics

Effective crop yield prediction is important for the optimization of agricultural planning and productivity. This study tries to assess four machine learning models: Support Vector Regression, Random Forest, Decision Tree, and K-Nearest Neighbors to predict the crop yield properly, along with the help of key environmental and agriculture inputs: Rainfall in mm, Fertilizer, Temperature in °C, Nitrogen (N), Phosphorus (P), and Potassium (K) per acre. All of these models are evaluated by different metrics such as MSE, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>, MAE, RMSE, CV and so on. So we implement and draw out the comparative result from the above algorithms that show which model is best for crop yield prediction in terms of precision and reliability. These new insights provide practical windows to guide farmers and policy-makers to make data-driven decisions that enhance yield outcomes and resource allocation. The proposed work provides a framework towards further development of the agricultural tools related to precision farming, thus endorsing sustainable agriculture. Predictive analytics will find serious importance in this work for improving the efficiency of agriculture.