Integrating Federated Learning and Explainable AI for Plant Disease Detection: A Systematic Review of Recent Advances in Precision Agriculture

Early detection of plant and crop diseases is vital for achieving sustainable agriculture and global food security. Traditional inspection methods are often slow and subjective, whereas Artificial Intelligence (AI) techniques offer fast, scalable, and objective alternatives. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, this systematic review synthesizes 145 studies published between 2023 and 2025 that employ Machine Learning (ML), Deep Learning (DL), Explainable AI (XAI), and Federated Learning (FL) for plant and crop disease classification. The studies are organized by crop species, imaging modality, and model architecture to evaluate performance in terms of accuracy, robustness, interpretability, and privacy preservation. Results reveal that XAI techniques such as Grad-CAM, LIME, and SHAP enhance transparency and trust, while FL enables decentralized and privacy-aware collaboration across distributed agricultural datasets with only minimal reductions in model accuracy compared to centralized training. Despite strong results in controlled conditions, many models struggle to generalize under real-field variability due to data imbalance and environmental factors. Emerging directions include lightweight edge architectures, domain adaptation, and unified explainable-federated frameworks. Overall, this review identifies FL and XAI as complementary drivers of transparent, privacy-preserving, and scalable AI systems for sustainable precision agriculture.