Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)

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 author...

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發表在:Waste and Biomass Valorization
主要作者: 2-s2.0-85178914032
格式: Article
語言:English
出版: Springer Science and Business Media B.V. 2024
在線閱讀:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178914032&doi=10.1007%2fs12649-023-02341-y&partnerID=40&md5=cba26af8c8d425769c73fb2f69453164
id Hong T.B.; Saruchi S.A.; Mustapha A.A.; Seng J.L.L.; Razap A.N.A.A.; Halisno N.; Solihin M.I.; Izni N.A.
spelling Hong T.B.; Saruchi S.A.; Mustapha A.A.; Seng J.L.L.; Razap A.N.A.A.; Halisno N.; Solihin M.I.; Izni N.A.
2-s2.0-85178914032
Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
2024
Waste and Biomass Valorization
15
5
10.1007/s12649-023-02341-y
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178914032&doi=10.1007%2fs12649-023-02341-y&partnerID=40&md5=cba26af8c8d425769c73fb2f69453164
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 classified 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 predefined 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 offers an economical yet high-efficiency solution. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
Springer Science and Business Media B.V.
18772641
English
Article
All Open Access; Green Open Access
author 2-s2.0-85178914032
spellingShingle 2-s2.0-85178914032
Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
author_facet 2-s2.0-85178914032
author_sort 2-s2.0-85178914032
title Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
title_short Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
title_full Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
title_fullStr Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
title_full_unstemmed Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
title_sort Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT)
publishDate 2024
container_title Waste and Biomass Valorization
container_volume 15
container_issue 5
doi_str_mv 10.1007/s12649-023-02341-y
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178914032&doi=10.1007%2fs12649-023-02341-y&partnerID=40&md5=cba26af8c8d425769c73fb2f69453164
description 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 classified 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 predefined 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 offers an economical yet high-efficiency solution. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
publisher Springer Science and Business Media B.V.
issn 18772641
language English
format Article
accesstype All Open Access; Green Open Access
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