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@article{178866,
        author = {Asmita Jadhav and Prof. Kumud Wasnik},
        title = {Plant Disease Detection using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {6356-6361},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178866},
        abstract = {Plant diseases are a significant menace to world agriculture, which causes significant harvest loss and economic difficulties. Conventional techniques for the identification of diseases are time-consuming and prone to erroneous influence. In this project, a solution is presented using a folding seller of Neuron Networks (CNN) for automatic plant disease detection and classification from leaf photos. Due to its capability of learning and deriving important features from visual information, CNN is very efficient in image-based operations like disease classification. The system proposed utilizes CNN to scan plant leaf images to detect symptoms of disease like color variations, stains, and texture changes. The process starts with image processing and then unique extraction through the CNN layer, completing the disease classification. The system’s functionality in image classification using trained models guarantees high accuracy when it comes to identifying a range of plant diseases. The project also presents a friendly interface where users upload images of leaves, are diagnosed with disease, and seek progression of disease procedures via a personalized account. The system under consideration combines extensions like user authentication and data storage to enable users to track the health of the system in the long term. The aim is to enormously enhance plant disease management, minimize human error, and push towards more sustainable farming practices.},
        keywords = {plants disease, image classification, machine learning model, convolutional nueral network, disease prediction},
        month = {May},
        }