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@article{180836,
        author = {Kanthi Varun Kumar and S. Meghana and K. Veera Reddy and P. Pawan Kumar and D.Mamatha},
        title = {Real-Time Plant Disease Detection – Using Deep Learning and CNN Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2769-2774},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180836},
        abstract = {This project presents a real-time plant disease detection system aimed at reducing crop loss and promoting early diagnosis through automated analysis. It employs deep learning techniques, particularly Convolutional Neural Networks (CNN), to detect and classify plant diseases based on visible symptoms present on leaves. The system captures live images via webcam, processes them using a trained CNN model, and identifies whether the plant is healthy or diseased. By analyzing image patterns of infected areas, the model facilitates timely interventions using appropriate treatments. The implementation compares multiple algorithms for accuracy and supports an interactive GUI to provide immediate, reliable feedback to users. This system addresses the critical need for fast, accurate, and accessible disease diagnosis in agriculture.},
        keywords = {Plant Disease Detection, Convolutional Neural Network (CNN), Deep Learning, Real-Time Monitoring, Image Classification, Precision Agriculture, Computer Vision, Machine Learning, Leaf Image Analysis, AI in Agriculture, Agricultural Automation, Crop Health Monitoring, Web-based GUI, Python Tkinter, Early Diagnosis System.},
        month = {July},
        }