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@article{177062,
        author = {Santhosh Rajaa R and Vishvashankar R and Gughan V M and S. Chandrakala},
        title = {Advancing Plant Leaf Disease Classifications With Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {87-91},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177062},
        abstract = {Most of the population in the world depends on agriculture, and Plant diseases pose a significant threat to global agricultural production, highlighting the immediate need for reliable and scalable diagnostic methods. This Study is the automation of Plant disease classification based on leaf imagery using Convolutional Neural Network (CNN), which is then deployed as a website. The proposed method demonstrates a high level of accuracy in identifying and differentiating various plant leaf characteristics. The study outlines the overall workflow, including dataset collection and preprocessing, CNN model architecture, Performance comparison, and Deployment. Integrating agricultural practices with a CNN-based system can provide timely and accurate disease detection, enhancing productivity and sustainable crop management.},
        keywords = {Plant Disease Classification, Convolutional Neural Network (CNN), Deep Learning, Computer Vision.},
        month = {April},
        }