Deciphering Okra Leaf Diseases: Federated Learning CNN at the Frontier of Agricultural Science

Early detection of leaf diseases is crucial in agricultural research to maximise production and maintain crop health. With the use of Convolutional Neural Networks (CNN) and federated learning across five different clients, each of which defines five severity levels of the illness, the current research reveals a ground-breaking method for comprehending Okra leaf disorders. Each client dataset provided a different perspective on the disease’s symptoms, which ranged in severity from moderate (1–25%) to severe (76–100%). Our study is drawn to the decentralised machine learning paradigm. It allows each client to analyse its data locally without sending the raw data to a centralised server, respecting data privacy principles. Insights from local models are included in the global model, which is realised via federated averaging to provide a comprehensive, unified viewpoint on Okra leaf disease detection. Initial findings show potential. The ranges for the customers’ macro, weighted, and micro averages were 87.21% to 93.96%, 88.27% to 93.94%, and 88.28% to 93.94%, respectively. Cltt-4 and Cltt-5 showed outstanding detection metrics, highlighting the model’s resilience. The federated averaging approach demonstrated its effectiveness by combining customer insights to create a comprehensive model with improved accuracy and recall rate, making it a priceless tool for farmers and agronomists. This study combines CNN’s strength with federated learning’s adaptability to provide a fresh perspective on Okra leaf diseases. It opens the door to a time of well-informed, decentralised, and privacy-protecting agricultural treatments that might completely alter how conditions are managed.