Design and Implementation of an Integrated Sensor Network for Monitoring Abiotic Parameters During Composting

Abdulqader Ghaleb Naser, Nazmi Mat Nawi (), Mohd Rafein Zakaria, Muhamad Saufi Mohd Kassim, Azimov Abdugani Mutalovich and Muhammad Adib Mohd Nasir
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Abdulqader Ghaleb Naser: Department of Agricultural Machinery and Equipment, Faculty of Agriculture, Tikrit University, Tikrit 34001, Iraq
Nazmi Mat Nawi: Department of Biological & Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Mohd Rafein Zakaria: Institute of Plantation Studies, Universiti Putra Malaysia, Serdang 43400, Malaysia
Muhamad Saufi Mohd Kassim: Department of Biological & Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
Azimov Abdugani Mutalovich: Research Laboratory Innovative Water Treatment Systems, M. Auezov South Kazakhstan University, Taukehan Street 5, Shymkent 16000, Kazakhstan
Muhammad Adib Mohd Nasir: Department of Biological & Agricultural Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia

Sustainability, 2025, vol. 17, issue 21, 1-27

Abstract: Efficient composting is essential for sustainable organic waste management, yet conventional monitoring approaches are limited by single-parameter measurements and delayed response. This study presents an integrated sensor–AI framework designed to capture the interaction between thermal, chemical, and environmental factors governing composting. A distributed in-pile sensor network continuously measured temperature, moisture, and pH, while ambient parameters and gaseous emissions (O 2 , CO 2 , CH 4 ) were recorded to validate process dynamics. Statistical analyses, including correlation and regression modeling, were applied to quantify parameter interdependencies and the influence of external conditions. Results showed strong positive associations between temperature, moisture, and CO 2 , and an inverse relationship with O 2 , indicating active microbial respiration and accelerated decomposition. The validated sensors maintained high accuracy (±0.5 °C, ±3%, ±0.1 pH units) and supported real-time feedback control, leading to improved nutrient enrichment (notably N, P, and K) in the final compost. The framework demonstrates a transition from static measurement to intelligent, feedback-driven management, providing a scalable and reliable platform for optimizing compost quality and advancing sustainable waste-to-resource applications.

Keywords: composting; sensor technologies; machine learning models; real-time monitoring; nutrient enhancement (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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