Labor shortages due to climate change and rural aging threaten the sustainability of open-field crop production such as rice cultivation, and the need for smart irrigation systems capable of precise environmental control based on water quality is emerging. This study aims to design an AWS cloud-based automation system that can precisely control water temperature and dissolved oxygen (DO), which directly affect rice growth, and validate its effectiveness through testbed-based simulation. The designed system consists of a structure that transmits data collected from water temperature and DO sensors to the AWS IoT Core via LoRa communication, and automatically generates state judgments and control commands from lambda functions to control drain-age pumps, cold water valves, and oxygen circulators. The control algorithm includes threshold-based condition judgment and iterative control logic, and is designed to send alerts to users via CloudWatch when an abnormal situation occurs. In simulations performed on a test bed mimicking field conditions, water temperature was restored to 28.3 °C in approximately 7 min and DO to 4.1 mg/L in approximately 6 min, with all controls fully automated. This means that the system outperformed the existing manual method in terms of response time and control accuracy, confirming the feasibility of real-time control of the growth environment. This system is expected to reduce labor, ensure growth stability, and improve resource efficiency in rice cultivation, and lays the foundation for automated environmental control technology including various water quality factors. In the future, it is expected to further improve the precision and practicality of smart farm technology by linking with AI-based prediction models and expanding its application to various crops and environmental conditions.