Improved Optimization Algorithm in LSTM to Predict Crop Yield

Abstract

Agriculture is the main occupation across the world with a dependency on rainfall. Weather changes play a crucial role in crop yield and were used to predict the yield rate by considering precipitation, wind, temperature, and solar radiation. Accurate early crop yield prediction helps market pricing, planning labor, transport, and harvest organization. The main aim of this study is to predict crop yield accurately. The incorporation of deep learning models along with crop statistics can predict yield rates accurately. We proposed an improved optimizer function (IOF) to get an accurate prediction and implemented the proposed IOF with the long short-term memory (LSTM) model. Manual data was collected between 1901 and 2000 from local agricultural departments for training, and from 2001 to 2020 from government websites of Andhra Pradesh (India) for testing purposes. The proposed model is compared with eight standard methods of learning, and outcomes revealed that the training error is small with the proposed IOF as it handles the underfitting and overfitting issues. The performance metrics used to compare the loss after implementing the proposed IOF were r, RMSE, and MAE, and the achieved results are r of 0.48, RMSE of 2.19, and MAE of 25.4. The evaluation was performed between the predicted crop yield and the actual yield and was measured in RMSE (kg/ha). The results show that the proposed IOF in LSTM has the advantage of crop yield prediction with accurate prediction. The reduction of RMSE for the proposed model indicates that the proposed IOFLSTM can outperform the CNN, RNN, and LSTM in crop yield prediction.