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@article{165084,
        author = {Suraj Damre and  Tanvi Shingi and Shivani Bhombe  and  Susmita Sharma  and Kumar Abhinav  and  Ravikiran Sapate},
        title = {Plant Disease Detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {11},
        number = {1},
        pages = {466-470},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=165084},
        abstract = {This project focuses on the development of an advanced plant disease detection system using machine learning techniques. The primary objective is to create a robust model capable of accurately identifying and classifying diseases affecting various plant species. Leveraging a diverse dataset of plant images, we employ state-of-the-art convolutional neural networks (CNNs) to extract intricate patterns and features associated with different diseases. This model aims to contribute to precision agriculture by enabling early detection and intervention, thereby assisting farmers in preserving crop health and optimizing yield. The project's methodology involves comprehensive data pre-processing, including image augmentation and normalization, to enhance the model's ability to generalize across different plant conditions. The CNN model is trained, validated, and tested on a carefully curated dataset, and its performance is evaluated using metrics such as accuracy, precision, and recall. Preliminary results indicate promising levels of accuracy, showcasing the potential impact of the developed system on improving disease diagnosis efficiency in agriculture. In conclusion, this project not only addresses the critical issue of plant disease detection but also underscores the broader implications of leveraging machine learning for sustainable agriculture. By providing an effective tool for early disease identification, the system has the potential to empower farmers with timely information, aiding them in making informed decisions to mitigate crop losses and contribute to global food security.},
        keywords = {},
        month = {},
        }