Efficient Crop Yield Prediction of Kharif Crop using Deep Neural Network

Abstract

The rapid expansion of population and varying environmental-climate condition forces us to concentrate on securing food sources. In India agriculture is the prominent domain, which requires immediate attention as it plays a basic source of food products. The present work focuses on forecasting Bajra Crop yield in the Rewari district of Haryana using a novel approach of the DNN-LSTM technique. The experimental results are estimated using Root mean square error (RMSE), and Mean Square Error (MSE), compared with the existing machine learning techniques. The outcomes reveal that Deep Neural Network provides a better forecast in comparison to earlier traditional methods and provides a lower RMSE value of 81.91. This study will be helpful for farmers in making decision policies for the Kharif crop season.