Crop Yield Prediction using Regression Models in Machine Learning

Abstract

India's economy is heavily dependent on agriculture, and this study report tries to increase agricultural productivity by forecasting crop yields for a range of crops farmed there. This study is unique in that it forecasts agricultural yields for any chosen time period throughout the year by using simple factors like, district, area, season and State. The article forecasts agricultural production using modern regression techniques including Lasso, Kernel Ridge, and Elastic-Net Regression designs. The idea of Stacking Regression is also used to improve the performance of the designs and provide more accurate forecasts. This research provides a positive breakthrough for India's agricultural industry, with the potential to deliver major advantages for farmers and the larger economy. This study provides a useful tool for improving crop yield projections and eventually increasing agricultural output in the nation by employing cutting-edge analytical methodologies and simple input parameters. Informed decisions regarding crop cultivation, fertilization, and harvest may be made by farmers with the help of technology and data-driven insights, resulting in higher yields and more favorable economic consequences.