FLyer: Federated Learning-Based Crop Yield Prediction for Agriculture 5.0

Abstract

Crop yield prediction is a significant area of precision agriculture. In this article, we propose a crop yield prediction framework named FLyer, based on federated learning and edge computing. In FLyer, the soil and environmental data are locally processed inside the edge servers, and the model parameters are transmitted between the edge servers and the cloud with encrypted gradients. LSTM is used as the local and global models for data analysis. As the LSTM model can capture the temporal dependencies and hold the sequential nature of the data, we use LSTM in FLyer. By encrypting the gradients, the gradient information leakage ratio is reduced, and data privacy is protected. For gradient encryption, we use AES-256, and for data encryption during local storage we use RSA and AES-256. The results demonstrate that FLyer diminishes the latency by $\boldsymbol{\sim}$39% and energy consumption by $\boldsymbol{\sim}$40% than the conventional edge-cloud framework respectively. The experimental results show that the global model in FLyer achieves above 99% accuracy, precision, recall, and F1-score in crop yield prediction. The results also present that the local models also achieve $\boldsymbol{>}$94% accuracy in crop yield prediction.