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@article{178510,
        author = {Yogeswara M and Praveen R and Sulaiman Ali A N and Sanjaykumar M and Dr Jamuna R},
        title = {Dynamic irrigation and Crop Prediction System using Machine Learning And IoT},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3459-3466},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178510},
        abstract = {This project proposes a smart agriculture solution leveraging the synergy of Internet of Things (IoT) technology and machine learning to optimize irrigation and predict crop outcomes. An intelligent network of interconnected sensors strategically deployed in the fie ld continuously monitors critical environmental parameters such as soil moisture and weather conditions in Coimbatore, Tamil Nadu. This real-time data is transmitted to a processing unit where machine learning algorithms analyze it in conjunction with historical agricultural data (including weather patterns, soil characteristics, and crop history). The predictive models forecast future soil moisture levels, estimate crop-specific water requirements based on growth stages, and potentially predict yields or risks like disease. Based on these predictions, the system autonomously controls irrigation actuators (valves, pumps) to deliver the precise amount of water needed, precisely when and where it's required. This dynamic irrigation approach minimizes water wastage, enhances resource efficiency, and ultimately aims to improve crop yields and promote sustainable agricultural practices in the region. The system may also incorporate a user interface for visualization and farmer interaction.},
        keywords = {Global Position System, Radio Frequency Identification, Global System for Mobile Communication, Sensors, IOT, Blynk, Nodemcu, Neo-6M GPS.},
        month = {May},
        }