Crop Yield Estimation using Improved Salp Swarm Algorithm based Feature Selection

Crop yield estimation is the art of yield prediction before harvest and it is essential for planning and making conclusive agricultural policies. The forecasting of crop yield is essential in optimal nutrient management, crop insurance, crop market planning and harvest management. However, the crop yield estimation is considered as a challenging task because of huge amount of abundant information exists in the crop data. Therefore, an effective feature selection is required to be developed for removing the redundant attributes. In this research, an Improved Salp Swarm Algorithm (ISSA) based feature selection for an effective crop yield estimation. The Opposition Based Learning (OBL) and Local Search Algorithm (LSA) are incorporated in the ISSA's initializati on and exploitation phase for selecting optimum feature subset. The selected features from the ISSA are used to enhance the classification using Modified Long Short Term Memory (MLSTM) classifier. The performance of the ISSA-MLSTM is analyzed using accuracy, precision, recall, F -score, NashSutcliffe Efficiency Coefficient (NSEC), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The existing researches such as Ensemble approach and MLSTM are used to evaluate the ISSA-MLSTM. The accuracy of the ISSA-MLSTM is 99% that is high when compared to the MLSTM.