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@article{176884,
        author = {M VENKATESAN and M NEERAJA and K GREESHMA and V SUNEETHA and S AVINASH REDDY and U MANU VARDHAN},
        title = {IDENTIFICATION OF FERTILIZER BASED ON NPK VALUES AND CROP RECOMMANDATION USING ML ALGORITHM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7616-7620},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176884},
        abstract = {FertiForecast is an intelligent system designed to identify the most suitable fertilizer for crops based on soil and environmental parameters using machine learning algorithms. The system integrates a Raspberry Pi with multiple sensors, including an air quality sensor, temperature sensor, soil moisture sensor, pH sensor, and NPK sensor, to continuously monitor essential soil and environmental conditions. These sensor values are transmitted to a machine learning model, which analyzes the data and predicts the appropriate fertilizer needed for optimal crop growth. By leveraging predictive analytics, the system recommends fertilizers tailored to specific crops such as chili (mirchi), groundnut, tomato, and paddy, ensuring efficient nutrient management and improved agricultural productivity. This approach helps farmers make data-driven decisions, enhancing yield quality while minimizing excess fertilizer usage and environmental impact.
Keywords: Fertilizer recommendation, NPK levels, machine learning, Raspberry Pi, pH sensor, air quality monitoring, precision farming.},
        keywords = {Precision Agriculture, Machine Learning, Fertilizer recommendation, Soil Nutrient Analysis.},
        month = {April},
        }