<![CDATA[Rice storage quality is heavily influenced by warehouse environmental conditions, particularly temperature, humidity, and gas/smoke. Manual monitoring has limitations in continuity, response speed, and data traceability. This study implements a smart rice warehouse prototype based on the Internet of Things (IoT) and Digital Twin (DT) using ESP32, DHT22 sensors, MQ-2 sensors, a ThingsBoard dashboard, and actuators (fan module, buzzer, and RGB LED). Built via the prototyping method, the system applies worst-case scenario fuzzy logic to classify environmental conditions into Safe, Warning, or Danger based on the most critical parameter. Testing across eight scenarios evaluated high temperature, high humidity, high gas levels, and simultaneous hazardous conditions in two warehouse zones. Results demonstrate the system updates the DT dashboard at an average interval of 5 seconds and automatically triggers actuators during abnormal conditions. These findings confirm that integrating IoT, DT, and fuzzy logic effectively supports warehouse monitoring and early warning systems for rice storage.]]>
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