AI-Enabled IoT-Based Smart Irrigation Management System for Predictive and Efficient Water Usage

This paper presents the proposed design of an AI-enabled IoT-based smart irrigation management system for predictive and efficient water usage. The system integrates an ESP32 microcontroller with soil-moisture, temperature, humidity, water-level, and water-flow sensors to collect agricultural data. The collected measurements will be processed and transmitted to an IoT platform for real-time monitoring. A machine-learning model is intended to support soil-moisture prediction and irrigation decision-making. Automatic pump control and water-flow monitoring are also included in the proposed design to support efficient water management and identify abnormal operating conditions. The system is currently under development, and experimental evaluation will be required to measure prediction performance, control reliability, and actual water-saving potential.