Teaching and Learning based Optimization with Deep Learning Model for Rice Crop Yield Prediction

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