English

Abstract

Rice crop yield prediction suggests the procedure of estimating the rice quantity which is harvested in a provided land region dependent upon several features like farming practices, weather conditions, and soil quality. The main aim of rice crop yield prediction is to offer farmers and agricultural planners correct crop yield calculations in progress, creating informed decisions assuming harvesting, marketing, and planting their crops. It supports farmers in optimizing their production and enhancing their profitability, but also improving food security by ensuring an even supply of rice for consumers. Deep learning (DL) approaches are utilized for predicting crop yield by leveraging the influence of neural networks for learning complex patterns and connections in data. This study presents a Teaching and Learning Based Optimization with Deep Learning for Rice Crop Yield Prediction (TLBODL-RCYP) technique. The proposed TLBODL-RCYP approach emphasizes the accurate forecasting of the rice yield using DL and hyperparameter optimizers. To accomplish this, the TLBODL-RCYP technique performs different preprocessing stages to improve the data quality. Besides, the TLBODL-RCYP technique employs a hybrid Convolution Recurrent HopField Neural Network (HCRHNN) model for yield prediction. At last, the TLBO algorithm was utilized to adjust the hyperparameter values of the HCRHNN technique and thereby enhance the predictive results. The experimental outcome investigation of the TLBODL-RCYP approach is tested with the Kaggle dataset, and the outcomes assured the improvized predictive results of the TLBODL-RCYP method over other recent DL techniques.