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@article{183144,
        author = {Raghunandan Jitendra rane and Varad Milind Sonavadekar and Adwait Vivek Kale and Rushikesh Rajaram Kadam},
        title = {DeepLeaf: Multilingual CNN Model for Mango Leaf Disease Detection and Remedies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {575-582},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183144},
        abstract = {Mango cultivation is integral to tropical agriculture; however, it is highly vulnerable to a range of leaf diseases that can significantly reduce both yield and quality. This paper presents DeepLeaf, a Convolutional Neural Network (CNN)-based system specifically developed to detect and classify various conditions of mango leaves, including Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, and healthy leaves. The model is trained on a meticulously curated image dataset of mango leaves and exhibits high accuracy in disease classification. To enhance accessibility for farmers, DeepLeaf is implemented through a visually appealing Streamlit web interface, featuring glassmorphism styling, real-time predictions, and remedy recommendations in English, Hindi, and Marathi. The system also provides language toggling and downloadable treatment guides. Experimental results validate the system's efficacy in practical applications, offering a promising tool for early intervention and sustainable mango farming practices.},
        keywords = {Convolutional Neural Network (CNN), Plant Disease Detection, Data Preprocessing, Deep Learning, Field Survey.},
        month = {August},
        }