AGRIFOLD: AGRIculture Federated learning for Optimized Leaf disease Detection

Abstract

Efficient and accurate detection of plant leaf diseases is essential for protecting crop health and promoting sustainable and precision agriculture practices. However, the decentralized nature of agricultural data, combined with the inherent limitations of centralized Machine Learning (ML), presents significant challenges for developing scalable, privacy-preserving solutions. In this paper, we introduce AGRIFOLD, a Federated Learning (FL) framework designed to enable collaborative training of a lightweight Convolutional Neural Network (CNN) across diverse and distributed datasets while maintaining data privacy. By integrating an Efficient Channel Attention (ECA) mechanism into the VGG16 architecture, AGRIFOLD significantly improves classification accuracy and enhances interpretability through heatmaps that highlight regions affected by diseases. We evaluate the FL model using various aggregation methods, including FedAvg, FedProx, SCAFFOLD, FedBN, and FedDF, obtaining good accuracy levels for all tested aggregation strategies, with SCAFFOLD achieving the best overall performance. The model’s lightweight design, optimized through ablation and pruning techniques, facilitates deployment on resource-constrained edge devices. Additionally, to further support farmers’ decision-making, the framework incorporates a natural language processing-based recommender system that provides tailored treatment suggestions. Comprehensive experiments conducted on 12 heterogeneous datasets demonstrate high classification accuracy across 9 distinct leaf disease classes and healthy leaves, underscoring the practical potential of FL-based solutions for sustainable, real-world agricultural applications. The AGRIFOLD source code is available at https://github.com/MODAL-UNINA/AGRIFOLD .

Topics & Concepts