<![CDATA[Composting offers a practical pathway for recycling organic waste, yet maintaining favorable process conditions and translating composting into structured experiential learning remain challenging. This study designed and conducted a preliminary field evaluation of an automated, sensor-enabled composting system intended to process vegetative waste from a small-scale farm while supporting sustainability education. The prototype combined an aerated static-pile configuration with forced aeration, humidity-triggered irrigation, three vertically distributed temperature and relative-humidity sensors, oxygen and fill-level monitoring, a touchscreen interface, transparent viewing panels, multiple access points, and remote data logging. A 29-day trial was conducted in an unheated tunnel greenhouse using approximately 1 m³ of vegetative waste and wood chips mixed at a reported 1:1 mass ratio. Sensor data were recorded at 10-minute intervals and analyzed descriptively to examine temporal patterns, vertical variation, system performance, and data completeness. Temperatures showed recurrent diurnal fluctuations and clear vertical stratification, reaching maximum values of 54.39°C, 45.83°C, and 35.67°C in the upper, middle, and lower zones, respectively. Lower-zone measurements were incomplete because of a connection failure, while middle-zone relative humidity remained near sensor saturation, limiting interpretation. The observed temperature increases, absence of malodor, and visible material changes were consistent with ongoing aerobic decomposition, although compost maturity and educational outcomes were not directly assessed. These findings demonstrate the feasibility of integrating monitoring, user interaction, visual access, and real-time data communication in a smart composter. Future work should validate sensor placement, quantify actuator performance, assess compost quality, and evaluate learning outcomes aligned with the United Nations Sustainable Development Goals.]]>
