Edge-Centric Collaborative Federated Learning for Irrigation Management of Paddy Fields Using Agriculture Sensor Data Processing

This letter explores the integration of federated learning (FL) techniques into edge networks to address the pressing issue of inefficient irrigation practices in paddy fields by proposing a prototype, equipped with various agriculture sensors and advanced data analytics. Deploying sensor networks directly in soil enables continuous data collection, creating a dynamic and responsive irrigation system. Leveraging this wealth of data, we employ advanced analytics techniques, such as synchronous FL and predictive modeling, to analyze historical trends and predict future irrigation requirements. FL, a decentralized machine learning paradigm, offers collaborative learning that can enhance the prototype by enabling inference at the edge. The experimental results of laboratory and field trials demonstrate the effectiveness of the proposed prototype in significantly improving irrigation management and enhancing overall crop productivity.