A privacy preserving federated learning framework for rice leaf disease classification and severity estimation using indian field data

This study presents a privacy-aware federated learning framework for rice leaf disease classification and severity estimation using heterogeneous field data collected across Indian agricultural regions. Conventional deep learning approaches rely on centralized data aggregation, which raises concerns related to data privacy, ownership, and scalability in distributed farming environments. To address these challenges, the proposed framework integrates a ResNet-18-based convolutional neural network enhanced with a Convolutional Block Attention Module for discriminative feature extraction, along with a model-contrastive federated learning strategy to mitigate client drift under non-independent and identically distributed data conditions. In addition to disease classification, a severity estimation module is incorporated using Otsu-based segmentation to quantify the proportion of infected leaf area, enabling a more comprehensive assessment of disease progression. The framework is evaluated across multiple federated settings with varying numbers of clients to analyse convergence, robustness, and scalability. Experimental results demonstrate that the proposed model achieves a classification accuracy of 95.49% and a severity estimation accuracy of 94.57% in a five-client setup, while maintaining stable performance above 91% and 92%, respectively, even with increased client participation. High macro-averaged F1-scores and well-defined severity confusion matrices further validate the reliability of the model. The results highlight the effectiveness of the proposed approach in enabling scalable, privacy-aware, and severity-informed disease monitoring, making it suitable for real-world precision agriculture applications.