Crop Recommendation with BiLSTM-MERNN Algorithm for Precision Agriculture

The term “precision agriculture” (PA) refers to the application of high-tech sensors and analysis tools to increase agricultural output and assist with management decisions. Deep learning, also referred as DL, has lately emerged as a potential remedy for information assertions and CPU vision difficulties. It offers a lot of promise and can be applied to agriculture just like it does to other industries. With this goal in mind, this study makes a case for establishing a crop recommendation system dependent on DL. This location is responsible for collecting a great amount of historical data on agricultural productivity, climatic conditions, and the preprocessing of said data. After that, crop recommendations are generated using an algorithm known as the Bidirectional LSTM-Modified Elman Recurrent Neural Network (BiLSTM-MERNN). A crop dataset is used to test how well the proposed method performs compared to other methods already in use, demonstrating the method’s applicability.