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@article{180018,
        author = {Prof. Sachin Hiranwale and Ayush Khedekar and Aditya Solanke and Atharv Girme and Devanshu Ghulaxe},
        title = {Plant Disease Prediction Implementation paper},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {476-478},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180018},
        abstract = {This paper presents the design and 
implementation of a deep learning-based web 
application for plant disease prediction. Leveraging a 
convolutional neural network trained on an augmented 
version of the Plant Village dataset, our system can 
detect and classify 38 types of plant diseases from 
images. The model was deployed using Streamlit, 
allowing users to interactively upload plant images and 
receive real-time disease predictions. This solution aims 
to assist farmers and agricultural experts in diagnosing 
plant diseases efficiently and accurately through a user
friendly platform.},
        keywords = {},
        month = {May},
        }