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@article{172309,
        author = {R Harshitha and Laveena P and Meghana S and Suhrit S Rao and Kiranmayi M},
        title = {IOT Based Plant Nutrition Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2838-2843},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172309},
        abstract = {Through the collection and analysis of data on soil nutrient levels and environmental conditions, the IoT system can detect nutrient deficiencies, imbalances, or excesses, providing farmers with actionable insights for precision fertilization. The system employs machine learning algorithms to interpret sensor data and provide recommendations for optimizing plant nutrition and improving crop yield. Additionally, it supports real-time alerts, allowing farmers to take timely corrective actions, thereby reducing the use of chemical fertilizers and promoting sustainable agricultural practices.},
        keywords = {},
        month = {January},
        }