Smart Irrigation System With Automatic Watering for plants based on sensor readings using IoT and ML

Water scarcity has become a significant challenge in modern agriculture, increasing the need for smarter and more efficient irrigation methods. This paper introduces an IoT-based smart irrigation system supported by machine learning to automatically regulate and optimize the water supply for plants. The system continuously collects real-time data-including soil moisture, temperature, humidity, and pH -to assess when irrigation is required. By using a hybrid model that combines Decision Tree and Random Forest algorithms, the system accurately predicts the ideal watering schedule. Through cloud integration, users can remotely monitor and control irrigation using a dashboard interface. Experimental findings show that the proposed system reduces water consumption by nearly 35% compared to traditional irrigation approaches while sustaining healthy soil moisture levels. Overall, the solution highlights how IoT and machine learning can contribute to more sustainable and data-driven agricultural practices.