Teh, Boon Hong and Sarah Atifah, Saruchi and Ain Atiqa, Mustapha and Lam, Jonathan Lit Seng and Ahmad Nor Alifa, A. Razap and Halisno, Nico and Mahmud Iwan, Solihin and Nor Aziyatul, Izni (2024) Intelligent kitchen waste composting system via deep learning and internet-of-things (IoT). Waste and Biomass Valorization, 15 (5). pp. 3133-3146. ISSN 1877-2641. (Published)
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Abstract
Kitchen waste is listed among the top global sustainability issue as it contributes to global warming and climate change. Composting is one of the solutions to tackle the issue of kitchen waste increment. However, a manual composting system has led to several problems for the waste management authorities to invest more in human labor, cost, and time to segregate and dispose of the kitchen waste and its composting soil. Therefore, this project proposes an intelligent kitchen waste composting system via deep learning and Internet-of-Things (IoT) that is fully automated to cater for that issue. Firstly, the proposed system utilized Convolutional Neural Network (CNN) to detect and segregate kitchen waste into compostable and non-compostable categories. Then, the classifed compostable waste went through composting stage inside an automated compost bin with the feature of IoT. The IoT compost bin requires less human labor as it used sensors, actuators, and Wi-Fi connection to monitor and control the composting process. Finally, the compost soil is transferred to the designated gardening area via smart compost soil transportation system. The system consists of a robot equipped with infrared sensors. The sensors control the robot’s movement by tracking the predefned black tape path. A prototype is built to investigate the performance of the proposed system. Results show that each sub-system managed to interact with one another, thus creating a large intelligent system that succeeded in completing the kitchen waste segregation, composting and ready compost delivering tasks automatically. In the future, it is expected that the proposed intelligent system has the potential to be commercialized to tackle the kitchen waste increment issue as it ofers an economical yet high-efciency solution.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Automation; Composting; Convolutional Neural Network; Internet-of-Things; Kitchen waste |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TS Manufactures |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs. Nurul Hamira Abd Razak |
| Date Deposited: | 15 Jul 2025 07:31 |
| Last Modified: | 15 Jul 2025 07:31 |
| URI: | http://umpir.ump.edu.my/id/eprint/44172 |
| Download Statistic: | View Download Statistics |
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