Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{205839,
        author = {Vijaya Kamble and Sarthak Mule and Shantanu Bawankar and Sharey Kapri and Mohit Chharra and Lavish Kanire},
        title = {Cotton leaf disease detection using federated learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9193-9197},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205839},
        abstract = {Cotton is one of the most important agricultural crops, and its productivity is significantly affected by diseases such as leaf blight, wilt, and boll rot. Early disease detection is essential to minimize crop loss and improve yield quality. Traditional manual inspection methods are time-consuming and often require expert supervision. Deep learning has emerged as an effective solution for automated disease classification, but centralized training approaches create challenges related to privacy, data ownership, and scalability. This research proposes a federated learning-based cotton disease detection framework that enables distributed model training without transferring raw image data. The proposed system uses ResNet-50 as the feature extraction backbone and applies the Federated Averaging algorithm for aggregating client-side model updates. Experimental evaluation demonstrates that the federated approach achieves reliable classification performance while maintaining privacy and supporting collaboration among multiple farms. The framework offers a scalable and practical solution for smart agriculture applications.},
        keywords = {Cotton Disease Detection, Federated Learning, Deep Learning, ResNet-50, Privacy Preserving AI, Smart Agriculture},
        month = {June},
        }