Predicting Crop Yields in Indian Agriculture Using Machine Learning

Predicting Crop Yields in Indian Agriculture Using Machine Learning

Teja Ram Mohan Sai Vemulapalli1, Kalyan Vanam2, S.Rupak Balaji3, Pavan Sudheer Varma K4 Bachu Anush Kumar5, Vikas Mangotra6, 1,2,3,4,5,6Department of Computer Science and Engineering, Lovely Professional University,Jalandhar Punjab, India.

1tejarammohansai@gmail.com, 2vanamkalyan050@gmail.com, 3rupakbalaji0510@gmail.com, 4sudheervarama7677@gmail.com, 5anushrao.326@gmail.com, 6mangotra.vikas@gmail.com.

Abstract— Agriculture provides the backbone of India's economy both within and between. The paper below provides a brief insight into the creation of a fresh model is currently in progress to estimate the variability in the crop yields across various regions in India. Via this simple setting-based approach, such parameters as year, district, season and area can be used to predict the amounts of crop outputs for certain years. By employing sophisticated regression techniques, Kernel Ridge, Lasso, and ENet algorithms are used, to improve the yield prediction precision. Additionally, it implies a technique of Scaled Regression which is specifically designed to improve the accuracy of the data resulting as long-term predictions.

Key terms: Crop yield forecasting, Lasso algorithm, Kernel Ridge algorithm, ENet algorithm, Stacked Regression.