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@article{162656,
        author = {Kiran Doiphode and Pratiksha Edake and Pranjal Chavan and Amruta Patil and Shreya Thombare},
        title = {Leaf Disease Detection System using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {10},
        pages = {819-822},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162656},
        abstract = {This study presents a mobile application that can be used to improve the identification and management of plant diseases. The app uses the best imaging technology to analyse leaf images captured by smartphones. Its main purpose is to help farmers and agriculturalists immediately detect and deal with threats to crop health.
The application has a user-friendly interface that makes it easy for people with low technical skills to save photos. Once a photo is uploaded, the app uses deep learning algorithms to analyse visual features and identify the presence of various leaf diseases. Provide users with immediate feedback and recommended actions to mitigate the impact of identified viruses.
The application aims to provide independent and reliable disease detection equipment, especially for small farmers with limited resources, using a widely used mobile device. The use of this technology is expected to improve early detection, reduce crop losses and aid permaculture practices.
},
        keywords = {leaf disease detection, mobile applications, image processing, machine learning, sustainable agriculture.},
        month = {},
        }