@Article{ee.2025.068422,
AUTHOR = {Siwakorn Banluesapy, Mahasak Ketcham, Montean Rattanasiriwongwut},
TITLE = {AI-Augmented Smart Irrigation System Using IoT and Solar Power for Sustainable Water and Energy Management},
JOURNAL = {Energy Engineering},
VOLUME = {122},
YEAR = {2025},
NUMBER = {10},
PAGES = {4261--4296},
URL = {http://www.techscience.com/energy/v122n10/64005},
ISSN = {1546-0118},
ABSTRACT = {Traditional agricultural irrigation systems waste significant amounts of water and energy due to inefficient scheduling and the absence of real-time monitoring capabilities. This research developed a comprehensive IoT-based smart irrigation control system to optimize water and energy management in agricultural greenhouses while enhancing crop productivity. The system employs a sophisticated four-layer Internet of Things (IoT) architecture based on an ESP32 microcontroller, integrated with multiple environmental sensors, including soil moisture, temperature, humidity, and light intensity sensors, for comprehensive environmental monitoring. The system utilizes the Message Queuing Telemetry Transport (MQTT) communication protocol for reliable data transmission and incorporates a Random Forest machine learning algorithm for automated irrigation decision-making processes. The Random Forest model achieved exceptional performance with 99.3% overall accuracy, demonstrating high model reliability. Six operational modules were developed and implemented with three distinct control methods: manual operation, condition-based automatic control, and AI-driven intelligent control systems. A comprehensive one-month comparative analysis demonstrated remarkable improvements across multiple performance metrics: a 50% reduction in both water consumption (from 140 to 70 L/day) and energy usage (from 7.00 to 3.50 kWh/day), a substantial 130% increase in water use efficiency, and a significant 50% decrease in CO<sub>2</sub> emissions. Furthermore, detailed factor importance analysis revealed soil moisture as the primary decision factor (38.6%), followed by temporal factors (20.3%) and light intensity (18.4%). The system demonstrates exceptional potential for annual energy conservation of 1277.5 kWh and CO<sub>2</sub> emission reduction of 638.75 kg, contributing substantially to sustainable development goals and advancing smart agriculture technologies.},
DOI = {10.32604/ee.2025.068422}
}