Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

Abstract

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.