<![CDATA[Mushroom cultivation methods are continually being refined to meet increasing demands for quantity and quality. However, frequent weather fluctuations often pose challenges. They can influence the optimal growth of mushrooms and the baglog's nutrient-chemical. This study aims to implement precision agriculture by developing a weather-baglog parameters monitoring system based on IoT-MQTT-Nodered technology. It seeks to analyze and evaluate the dominant parameters influencing ideal oyster mushroom cultivation room conditions using machine learning classification models and capability process analysis. Sample data was collected from an oyster mushroom cultivation room using a 24-hour monitoring system over seven days. The monitoring tool's system design comprises three parts: multi-sensor data acquisition, communication protocol to the server, and smartphone-based data monitoring. The results demonstrate the system's effectiveness, mobile-access, and durability in monitoring and acquiring weather-baglog parameters data. The best model shows that light, temperature, and humidity are the dominant parameters influencing the ideal oyster cultivation room. Capability process analysis reveals that the dominant parameters in the cultivation room are currently less than ideal. The implications for improvement are needed an IoT-based control system to regulate them and make them ideal. This finding has been tested as an effective, mobile-access, durable, and data-centering monitoring system.]]>
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