Citation

Mior Mohammad Hairol, Yang Fatin Syahira and Mahmud, Azwan and Md Jiziat, Noor Lindawaty and Abd Aziz, Azlan and Yaacob, Syamsuri (2025) Energy-Efficient Smart Composting IoT System for Sustainable Food Waste Management. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

This paper presents the development of a smart composting machine that integrates IoT, sensor-based automation, and machine learning to enhance the decomposition process of food waste. The system features realtime monitoring of temperature, gas emissions (methane and air quality), and pH levels, along with automated environmental controls to maintain optimal composting conditions. An actuator assists in mixing the compost, while a heater maintains the temperature using an energy-efficient battery. The sensor data is transmitted to a mobile Blynk dashboard via ESP8266, enabling remote monitoring and user alerts. We proposed development of a fully automated smart composting system that integrates automated pH adjustment through acid/alkaline dosing, and a linear regression-based machine learning model that predicts the maturity stage of compost. The system was tested using five categories of food waste—fruits, vegetables, meat, dairy, and bread—demonstrating stable operation and accurate predictions. The system shows that a combined approach significantly optimizes decomposition conditions and reduces the composting cycle to under 12 hours, a reduction by a third in comparison to commercial composting machine

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET) Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 07:31
Last Modified:	18 Mar 2026 07:31
URII:	http://shdl.mmu.edu.my/id/eprint/15559

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