A Novel Federated Deep Learning Framework for Privacy-Preserving Computer Vision in Smart Agriculture Applications

Smart agriculture applications increasingly rely on computer vision technologies for tasks such as crop monitoring, disease detection, and yield prediction. However, the deployment of these technologies raises significant privacy concerns as they often involve collecting and processing sensitive data from farmers’ fields and operations. This paper presents a novel federated deep learning framework specifically designed for privacy-preserving computer vision in smart agriculture. The proposed framework, AgriFedVision, enables collaborative training of robust computer vision models while ensuring that raw image data remains on local devices. The architecture incorporates differential privacy mechanisms, secure aggregation protocols, and model compression techniques optimized for agricultural applications. We evaluate AgriFedVision on three real-world agricultural tasks: plant disease detection, weed identification, and crop yield estimation using datasets collected from ۳۲ farms across different geographic regions. Results demonstrate that our framework achieves ۹۴.۲% of the accuracy of centralized approaches while reducing communication overhead by ۷۶% and providing formal privacy guarantees with ε = ۳.۸. Field tests show that AgriFedVision is deployable on resource-constrained edge devices commonly found in agricultural settings, with inference times suitable for real-time applications. Our approach addresses the unique challenges of agricultural data heterogeneity and seasonal variability through adaptive federated optimization techniques. This work represents a significant step toward privacy-preserving smart agriculture systems that can benefit from collective intelligence without compromising individual farmers’ data privacy.

Keywords:

federated learning , privacy-preserving machine learning , computer vision , smart agriculture , edge computing , differential privacy