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@article{169911,
        author = {AMSA M and DEVA DHARSHINI V and JAYAWARDHINI V and NIDHARSHNAA S T},
        title = {AI-Driven Crop Disease Prediction and Management System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2341-2343},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169911},
        abstract = {Agriculture is vital for feeding the growing population, serving as an energy source, and combating global warming. Plant diseases significantly impact crop yield and quality, necessitating early detection. Traditional detection relies on experts analyzing leaf color changes, a labor-intensive, costly, and inconsistent method, especially for large fields. Deep learning, particularly Convolutional Neural Networks (CNNs), offers an efficient alternative for disease detection through image classification and segmentation. This approach can reduce costs, improve accuracy, and enable remote monitoring. Additionally, integrating severity analysis and fertilizer recommendations can enhance disease management and support sustainable farming practice},
        keywords = {Crop disease detection, deep learning, Convolutional Neural Networks (CNN), agricultural productivity, mobile application ,machine leaning, web application, early detection, real time monitoring},
        month = {November},
        }