Moisture content monitoring in industrial-scale composting systems using low-cost sensor-based machine learning techniques

Moisture is a key aspect for proper composting, allowing greater efficiency and lower environmental impact. Low-cost real-time moisture determination methods are still a challenge in industrial composting processes. The aim of this study was to design a model of hardware and software that would allow self-adjustment of a low-cost capacitive moisture sensor. Samples of organic composts with distinct waste composition and from different composting stages were used. Machine learning techniques were applied for self-adjustment of the sensor. To validate the model, results obtained in a laboratory by the gravimetric method were used. The proposed model proved to be efficient and reliable in measuring moisture in compost, reaching a correlation coefficient of 0.9939 between the moisture content verified by gravimetric analysis and the prediction obtained by the Sensor Node.

Automated summary

This study designed a hardware and software model to enable self-adjustment of a low-cost capacitive moisture sensor and improve the measurement accuracy using machine learning techniques. The results demonstrate that the proposed model is efficient and reliable in measuring moisture in compost.