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@article{162943,
        author = {Nikitha G S and Princy Sengar and Rahul Kumar and Ratnesh Kumar Puskar and Rishu Raj},
        title = {PLANT DISEASE PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {11},
        pages = {933-939},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=162943},
        abstract = {The escalating threat posed by plant diseases to global agriculture underscores the urgency for robust prediction systems to mitigate crop losses. This study presents a novel approach to plant disease prediction employing machine learning techniques. Leveraging a comprehensive dataset encompassing plant characteristics, environmental factors, and disease symptoms, a predictive model was developed and evaluated. The methodology involved data collection, preprocessing, feature extraction, and model training using state-of-the-art algorithms. The results demonstrated a significant predictive capability, with an accuracy of [insert accuracy percentage]. This research contributes to the advancement of precision agriculture by offering an effective tool for early disease detection and proactive management strategies. Moreover, it sheds light on the potential of leveraging machine learning in agricultural systems for sustainable food production.},
        keywords = {processing, remote sensing, crop heath monitoring, precision agriculture, crop protection, machine learning, data mining, classification algorithm, disease management.},
        month = {},
        }