Journal
BIORESOURCE TECHNOLOGY
Volume 359, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2022.127456
Keywords
Artificial intelligence; Compost management; Remote sensing; IBK; MLP; Linear regression
Funding
- CAPES
- CNPq
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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.
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.
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