Predictive IoT enabled Irrigation System Leveraging Machine Learning to Save Water

Agriculture is one of the most water-intensive sectors globally, and with increasing water scarcity, optimizing irrigation practices is vital for sustainable crop production. Conventional irrigation techniques, commonly controlled by manual timing or set timers, do not know anything about the environment, and thus cannot use water efficiently or promote the health of crops in the most optimal way. The proposed study presents an idea of a predictive irrigation system built on the IoT and machine learning (ML) models to optimize the use of water based on real-time data about environmental conditions. The main sensors such as soil moisture, temperature, humidity and rainfall are installed and connected to Arduino microcontrollers and LoRa communication that provides low power and long-range transmission. The sensor data are obtained by means of Google Firebase and MongoDB and processed to produce the most important indicators, i.e., soil water deficit and water demand index by means of preprocessing and feature engineering. Three ML models, which include the Decision Tree, the XGBoost, and the Recurrent Neural Networks (RNN) are created to forecast future soil moisture and activate irrigation accordingly. RNN was the best performer among them with a prediction accuracy of 95.3, F1-score of 94.8 and a water saving of up to 39.6. In a 10-day simulated testbed, this system was proven effective and was able to keep the soil moisture in the optimal range with no spurious irrigation activations. This predictive, information-based strategy greatly promotes saving of water and minimizes the use of individual interventions. The framework proposed provides scaled and smart way to solve current agriculture and particularly in areas of water scarcity. There is a future plan to include adaptive online learning, crop model, and solar-powered node in future work to enhance the accuracy and sustainability of energy.