The growing adoption of smart farming technologies has led to the generation of large volumes of agricultural data, particularly for crop health monitoring and disease prediction. However, concerns over data privacy and ownership hinder the development of centralized machine learning models in agriculture. This paper proposes a novel framework that leverages Federated Learning (FL) to enable collaborative crop disease detection across multiple farms without sharing raw data. Each participating farm trains a local model on its proprietary image or sensor dataset, and only the model updates are aggregated to form a global model. This decentralized approach preserves data privacy while still leveraging the benefits of collective learning. We evaluate the proposed framework using benchmark plant disease datasets and simulate its deployment on edge devices typical in rural areas. The results demonstrate that federated learning achieves competitive accuracy compared to centralized models while offering robust privacy guarantees. This research paves the way for scalable, privacypreserving AI solutions in precision agriculture, especially for low-resource and data-sensitive farming communities.
Federated Learning, Crop Disease Detection, Privacy-Preserving Machine Learning, Smart Farming, Precision Agriculture, Edge Computing, Decentralized AI, Agricultural Data Privacy, Deep Learning, Plant Health Monitoring