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

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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 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.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to plagiarism possibility but are available from the corresponding author on reasonable request.

References

  1. Kaza, S., Yao, L.C., Bhada-Tata, P., Van Woerden, F.: What a waste 2.0: a global snapshot of solid waste management to 2050. Urban Development; © Washington, DC: World Bank. http://hdl.handle.net/10986/30317 License: CC BY 3.0 IGO (2018)

  2. Bernama: Food waste during Ramadan increasing annually, laments deputy minister. New Straits Times. https://www.nst.com.my/news/nation/2023/04/898118/food-waste-during-ramadan-increasing-annually-laments-deputy-minister (2023). Accessed 15 Oct 2023

  3. Bernama: SWCorp: Food waste drops during MCO, rises again soon after. New Straits Times. https://www.nst.com.my/news/nation/2020/10/633738/swcorp-food-waste-drops-during-mco-rises-again-soon-after (2020). Accessed 20 Oct 2022

  4. Secretariat, GMI: U.S. Government Global Methane Initiative (GMI) Accomplishments: 2017 Annual Report. United States Environmental Protection Agency (EPA). https://www.epa.gov/gmi/us-government-gmi-accomplishments-2017-annual-report (2022). Accessed 18 Feb 2022.

  5. Phooi, C.L., Azman, E.A., Ismail, R., Arif Shah, J., Koay, E.S.R.: Food waste behaviour and awareness of Malaysian. Scientifica. 2022, 6729248 (2022). https://doi.org/10.1155/2022/6729248

    Article  Google Scholar 

  6. Nakanishi, H., Shibata, H.: Kitakyuhsu City, Japan. In: Roberts B.H., Lindfield M., Steinberg F. (eds.) Partnerships for the sustainable development of cities on the APEC region, APEC Policy Support Unit, pp. 183–201. Asia-Pacific Economic Cooperation (2017)

  7. MAEKO: The food waste specialists. https://www.maeko.com.my/aboutus.php (2022). Accessed 1 Dec 2022

  8. Li, R., Chen, S., Li, X., Lar, J.S., He, Y., Zhu, B.: Anaerobic codigestion of kitchen waste with cattle manure for biogas production. Energy Fuels 23(4), 2225–2228 (2009). https://doi.org/10.1021/ef8008772

    Article  Google Scholar 

  9. Yao, G., Lei, T., Zhong, J.: A review of convolutional-neural-network -based action recognition. Pattern Recogn. Lett. 118, 14–22 (2019). https://doi.org/10.1016/j.patrec.2018.05.018

    Article  Google Scholar 

  10. Novianti, P.W., Tweel, I.V.D., Long, V.L., Roes, K.C.B., Eijkemans, M.J.C.: An application of sequential meta-analysis to gene expression studies. Cancer Inform. 14(Suppl 5), 1–10 (2015)

    Google Scholar 

  11. Ayilara, M.S., Olanrewaju, O.S., Bababola, O.O., Odeyemi, O.: Waste management through composting: challenges and potentials. Sustainability 12(11), 4456 (2020). https://doi.org/10.3390/su12114456

    Article  Google Scholar 

  12. Svendsen, J.H., Diederichsen, S.Z., Hojberg, S., Krieger, D.W., Graff, C., et al.: Implantable loop recorder detection of atrial fibrillation to prevent stroke (The LOOP Study): a randomised controlled trial. Lancet 398(10310), 1507–1516 (2021). https://doi.org/10.1016/S0140-6736(21)01698-6

    Article  Google Scholar 

  13. Mirani, A.A., Velasco-Hernandez, G., Awasthi, A., Walsh, J.: Key challenges and emerging technologies in industrial IoT architectures: a review. Sensors. 22(15), 5836 (2022). https://doi.org/10.3390/s22155836

    Article  Google Scholar 

  14. Bharany, S., Sharma, S., Khalaf, O.I., Abdulsahib, G.M., Al Humaimeedy, A.S., Aldhyani, T.H.H., Maashi, M., Alkahtani, H.: A systematic survey on energy-efficient techniques in sustainable cloud computing. Sustainability. 14(10), 6256 (2022). https://doi.org/10.3390/su14106256

    Article  Google Scholar 

  15. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Futur. Gener. Comput. Syst. 29(7), 1645–1660 (2013). https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  16. Kumar, P., Ashok, G.: Design and fabrication of smart seed sowing robot. Mater. Today 39(Part 1), 354–358 (2021). https://doi.org/10.1016/j.matpr.2020.07.432

    Article  Google Scholar 

  17. Hanumante, V., Roy, S., Maity, S.: Low cost obstacle avoidance robot. Int. J. Soft Comput. Eng. 3(4), 52–55 (2013). https://doi.org/10.6084/M9.FIGSHARE.1327873

    Article  Google Scholar 

  18. Punetha, D., Kumar, N., Mehta, V.: Development and applications of line following robot based health care management system. Int. J. Adv. Res. Comput. Eng. Technol. 2(8), 2446–2450 (2013)

    Google Scholar 

  19. Adabara, I., Hiriji, N., Emmanuel, O., Alkasim, S.M., Zaina, K., Mustafa, M.M.: Intelligent embedded agricultural robotic system. Int. J. Eng. Inf. Syst. 3(1), 14–24 (2019)

    Google Scholar 

  20. Abraham, A., Ananthakrishnan, G.S., Verghese, B.B., Sivakumar, S., Rakesh, S.: SARGoT: smart autonomous robotic goods transporter. Mater. Today 24(Part 3), 2030–2035 (2020). https://doi.org/10.1016/j.matpr.2020.03.633

    Article  Google Scholar 

  21. Sandler, M. Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018). arXiv:1801.04381

  22. Alzubaidi, L., Zhang, J., Humaidi, A.J., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 53 (2021). https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  23. Mozilla: Django introduction. Mdn web docs. https://developer.mozilla.org/en-US/docs/Learn/Server-side/Django/Introduction. Accessed 27 July 2021

Download references

Acknowledgements

Highly appreciation to the research team members from UCSI Universiti, Universiti Malaysia Pahang Al-Sultan Abdullah, and Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, for their dedication and contribution throughout this study.

Funding

This project was funded by UCSI University, Research Excellence & Innovation Grant REIG-FETBE-2022/026 and UMPSA Internal Grant RDU230327. The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Department of Mechanical and Mechatronics, Faculty of Engineering, Built and Technology, UCSI University, Cheras, 56000, Kuala Lumpur, Malaysia

    Teh Boon Hong, Ain Atiqa Mustapha, Jonathan Lam Lit Seng, Ahmad Nor Alifa A. Razap, Nico Halisno & Mahmud Iwan Solihin

  2. Emmecom (Asia), Pte. Ltd., Singapore, 628942, Singapore

    Teh Boon Hong

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Sarah Atifah Saruchi & Ain Atiqa Mustapha

  4. Centre of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 43800, Dengkil, Selangor, Malaysia

    Nor Aziyatul Izni

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  1. Teh Boon Hong
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  2. Sarah Atifah Saruchi
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Correspondence to Sarah Atifah Saruchi.

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Hong, T.B., Saruchi, S.A., Mustapha, A.A. et al. Intelligent Kitchen Waste Composting System via Deep Learning and Internet-of-Things (IoT). Waste Biomass Valor 15, 3133–3146 (2024). https://doi.org/10.1007/s12649-023-02341-y

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