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@article{175838,
        author = {Tejas Sunil Pawar and Aryan Vijay Lasure and Ritesh Madhukar Patil and Trupti Ashok Kadam and Yogesh Manohar Gajmal},
        title = {Deep Learning-Based Real Time Plant Leaf Disease Prediction And Recommendation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6807-6812},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175838},
        abstract = {Agricultural productivity is significantly impacted by plant diseases, which lead to reduced crop yield and economic losses for farmers. Traditional disease detection methods, such as manual inspection, are time-consuming and often unreliable. This research presents a deep learning-based mobile application that provides real-time plant leaf disease prediction and treatment recommendations. Utilizing a Convolutional Neural Network (CNN) model, the system achieves 96.91% accuracy, outperforming traditional models. The application employs Android studio for the frontend, a REST API for backend processing, and TensorFlow Lite for on-device inference, ensuring efficient disease detection without reliance on external APIs. This research demonstrates the potential of integrating deep learning and mobile technology to enhance agricultural practices, offering farmers a practical solution for early disease management.},
        keywords = {},
        month = {April},
        }