Exploring the Impact of Different Learning Layers within Recurrent Neural Networks on Crop Yield Prediction

Objective: Modern farming practices have found that agriculture data analytic is a strong instrument that is altering the way farmers analyze and manage crop production. With the aid of this initiative, farmers will be able to determine the yield of their crops prior to cultivation, assisting them in making the appropriate selections. The study aims to optimize the efficiency and accuracy of agricultural yield prediction by including RNNs in the pipeline for analysis. Methodology: The application of Recurrent Neural Network (RNN) algorithms for agricultural data analytic and crop yield estimation is examined in this article. RNNs use the skills of sequential data processing to examine and model the complex temporal patterns found in agricultural information. The experiment on analysis all the three levels of RNN memory to find fix the optimal model to handle the multiple crops of various season.. By employing thirteen identical crop yield data from the past and present. Findings: Through extensive experimentation on agricultural datasets encompassing diverse crop types, geographic regions, and environmental conditions. The 8-layer model produces the best and optimal prediction of the crops with 98% accuracy.