A Hybrid Approach for Crop Prediction Using Bi-Directional LSTM-RNN

In terms of managing food production and fostering economic growth, crop growth conditions and productivity are crucial. Prompt and accurate yield estimations are pertinent for ensuring food security. Farming is bnased on multiple factors including crop rotations, consistencies in soil types, air, surface temperatures, patterns of precipitations and others in addition to soil’s elements like nitrogen, phosphate, and potassium. Selecting crops using Random Forest (RF), Decision Trees (DT), Artificial Neural Networks (ANN)) are a part of the current corpus of research in this field. In this work, the suggested model uses deep learning (DL) for enhanced crop predictions alog with recommendations on required soil components with costs.The proposed work constructs LSTM-RNN based on LSTM (Long-Short Term Memory) and RNN (Recurrent Neural Network) reinforcement model in order to change the learning balance necessary for Q-Learning Reinforcement count to compute yield. The three essential characteristics approach should be used for future predictions of crop yields. (1) The proposed LSTM-RNN model captures temporal interdependences of environmental factors and seeds’ genetic developments without knowledge on their genotypes (2) The model demonstrates that there was no loss in forecast accuracy when the yield prediction was applied to untested situations (3) When used in conjunction with the backpropagation technique, the model may show how weather patterns, weather prediction accuracy, soil conditions, and crop production variations are all related. It evaluates imputdata and assists farmers in crop forecasts snf thud increased revenue. Our results showed a trustworthy and promising method for raising yield Prediction.