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@article{187232,
        author = {H KRUPA and Gulla Harika},
        title = {Deep Learning–Driven Mobile Application for Automated Mulberry Leaf Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5191-5199},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187232},
        abstract = {The extended research is making use of ensemble deep learning and intelligent integration for the detection of mulberry leaf diseases. While the YOLO family finds strange things rapidly, the complicated properties are found by NasNetMobile and Xception models, making the categorization more accurate. The usage of these models together in farming makes it easier and faster in finding diseases. A Flask-based frontend with user authentication allows academics and farmers to test and view disease forecasts easily in a secure and interesting manner. This larger framework would make smart agriculture more accurate, easier to use, and usable in real time.},
        keywords = {Ensemble model, deep learning, flask front end, disease detection, smart agriculture, Xception, YOLO, CNN, NasNetMobile, mulberry leaf disease.},
        month = {November},
        }