<![CDATA[This study aims to develop an Internet of Things (IoT)-based smart irrigation system capable of monitoring soil moisture, air temperature, and rainfall intensity in real time to improve water-use efficiency in agricultural practices. The research employed a research and development (R&D) design combined with a field-based experimental approach to evaluate system performance under dynamic environmental conditions. The system architecture consists of a multi-sensor configuration, including soil moisture sensors, a DHT22 temperature–humidity module, and a rain sensor, connected to an ESP32 microcontroller for data acquisition. Data were transmitted, stored, and visualized through a cloud-based IoT platform. The primary data for this study were collected over a seven-day testing period on a horticultural test plot. Findings indicate that the system successfully reduced water consumption by approximately 30–40% compared to traditional manual irrigation methods, while providing accurate automated responses based on real-time environmental data. Brief analysis shows that integrating three environmental parameters simultaneously enables more precise irrigation decision-making compared to single-parameter systems. The novelty of this research lies in the implementation of a multi-sensor, real-time adaptive system capable of responding intelligently to local weather variations. The implications of this study for the field of education include providing a practical reference for applying IoT in smart agriculture, serving as an instructional example in courses related to digital technology, automation, and precision agriculture, and supporting future research developments focused on data-driven farming technologies.]]>
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