Crop diseases substantially reduce agricultural productivity and compromise food security by lowering both yields and produce quality. Timely and accurate disease diagnosis is therefore vital for effective crop management; However, conventional deep learning methods typically depend on centralized data collection, which creates concerns related to privacy, security, and data ownership. To overcome these limitations, this research presents a privacy-preserving crop disease detection framework built on Federated Learning, MobileNetV2, and Gradient-weighted Class Activation Mapping (Grad-CAM). The framework supports collaborative training across multiple distributed clients without exchanging raw agricultural data. Crop leaf images from the Crop Disease Detection Dataset were preprocessed and allocated among federated clients to emulate heterogeneous real-world agricultural settings. MobileNetV2 was selected as the classification backbone because of its lightweight design and computational efficiency, while the Federated Averaging algorithm was applied to combine local model updates into a global model. In addition, Grad-CAM was incorporated to generate visual explanations by emphasizing disease-affected regions that influence model predictions. Experimental results confirmed the effectiveness of the proposed approach, with an accuracy of 95.93%, precision of 96.13%, recall of 95.93%, and F1-score of 95.94%. These findings show that the system preserves strong classification performance even under non-independent and non-identically distributed data conditions while maintaining data privacy. The Grad-CAM outputs also enhanced model transparency and interpretability. Overall, the proposed framework provides an efficient, scalable, and trustworthy approach for automated crop disease diagnosis and may contribute to the development of intelligent, privacy-aware agricultural systems for precision farming applications.

This research presented a privacy-preserving and explainable crop disease detection framework that integrates Federated Learning, MobileNetV2, FedAvg aggregation, and Grad-CAM visualization. The proposed system enables collaborative model training across multiple clients without sharing raw agricultural data, thereby preserving data privacy while maintaining high classification performance. Experimental results demonstrated that the framework achieved an overall accuracy of 95.93%, precision of 96.13%, recall of 95.93%, and F1-score of 95.94%, confirming its effectiveness for crop disease diagnosis under Non-IID federated environments. Furthermore, Grad-CAM visualizations improved model interpretability by highlighting disease-affected regions responsible for predictions, enhancing user trust and transparency.

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