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@article{180537,
        author = {Rohan Fargade and Ayush Gunjal and Atharva Patil and Urmila Dalal},
        title = {Smart Irrigation Systems: Integrating IoT, Automation, and Machine Learning for Efficient Water Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2511-2520},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180537},
        abstract = {Agriculture remains a critical sector for global sustenance, yet it faces major challenges due to increasing water scarcity, climate change, and dependence on manual labor for irrigation management. Traditional irrigation methods often lead to overwatering or underwatering, resulting in water wastage and reduced crop productivity. This paper presents the development and implementation of a Smart Irrigation System that leverages Internet of Things (IoT) technologies to automate and optimize the irrigation process. The proposed system uses soil moisture sensors to monitor real-time soil conditions, a water level sensor to track tank capacity, and a weather forecasting API to make intelligent decisions about watering schedules. A web and mobile-based interface allows farmers to set irrigation parameters such as motor on/off timings, watering duration, and specific field targeting. In addition, a manual override mechanism is integrated to ensure uninterrupted operation in case of automation failure.
The system is built around a microcontroller (e.g., ESP32/Raspberry Pi), interfaced with sensors, motor relay, and cloud communication services. Real-time data from the field is continuously uploaded and visualized on a user-friendly dashboard. By combining automation with environmental sensing, this solution significantly reduces water consumption, increases efficiency, and enables remote management of irrigation tasks. Experimental results demonstrate that the system can adapt to varying soil conditions and weather changes, providing timely irrigation while saving valuable resources. The proposed model is scalable, cost-effective, and adaptable to various types of crops and farm sizes, making it a strong step toward precision agriculture and sustainable farming.},
        keywords = {IoT, Soil Moisture Sensor, Water Level Monitoring, Weather Forecasting, Precision Agriculture, Automation, Sustainable Farming, Remote Monitoring, Microcontroller, Web-Based Control, Water Conservation},
        month = {June},
        }