A Swarm based Bi-directional LSTM-Enhanced Elman Recurrent Neural Network Algorithm for Better Crop Yield in Precision Agriculture

Abstract

Agriculture plays a significant role in providing food in a country. It is a major industry in terms of revenue and contributes to the economic development of a country. Global warming and sudden changes in climatic conditions have hampered agricultural industry creating multiple challenges in crop cultivation affecting productivity of crops. In spite of recent changes agricultural practices, challenges exist. Current technological growths can help overcome challenges in this industry in terms of improving productivity. PF (Precision Farming) is a technological concept that can aid traditional farming practices into becoming more productive. Moreover, traditional methods are advantageous in crop yield predictions, but considering unknown environmental factors makes these methods achieve lesser yields. PF can forecast or suggest the right time for cultivation based on previous known data. DCNNs (Deep Convolution Neural Networks) is one MLT (Machine Learning Technique) that can effectively predict crop growths. Hence, this work aims towards contributions in this area by presenting a short-term crop yield prediction model called RDA-Bi-LSTM-EERNN based on Bi-directional LSTM-Enhanced Elman Recurrent Neural Networks Algorithm with Red Deer Algorithm. The proposed RDA-Bi-LSTM-EERNN algorithm is an altered version of Bi-LSTM-EERNN with RDA based optimizations. This works hybrid method was compared with traditional approaches for its predictive performances using a crop dataset. This work’s proposed scheme can greatly help farmers take valuable decisions as its experimental results were found to be satisfactory.