Revolutionizing Waste Management: A Cutting-edge pyTorch Model for Waste Classification and Prediction, Coupled with a User-friendly Recycling Recommendation Application Built with Tkinter

Waste management application with tkinter and deep learning

  • Biplov Paneru Dept.of Electronics & communication, Nepal Engineering College
  • Ramhari Poudyal Dept. of Electrical Engineering, Tribhuvan University Nepal
  • Bishwash Paneru Dept. of chemical Science Engineering, institute of Engineering Nepal
  • Krishna Bikram Shah Department of Computer Science and Engineering, Nepal Engineering College Pokhara University, Nepal
  • Khem Narayan Poudyal Department of Computer Science and Engineering, Nepal Engineering College Pokhara University, Nepal
Keywords: Tkinter, waste classification, pyTorch, recycle

Abstract

A major environmental concern is waste management, and encouraging recycling programs depends heavily on the accurate categorization and forecasting of waste kinds. We present an enhanced pytorch model in this work for waste prediction and classification. To promote sustainable waste disposal practices, we also present a Recycling Recommendation Application with an intuitive Tkinter interface. The goal of combining cutting-edge machine learning methods with user-centered design is to make waste management systems more effective. The model gained accuracy of 99% on training and approximately 96% on validation, and was successfully added in a tkinter app for making prediction on type of waste image, plus recommending of solution to such waste management is done by the application we develop.

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References

Adedeji, O., & Wang, Z. (2019). Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network. Procedia Manufacturing, 35, 607-612. https://doi.org/10.1016/j.promfg.2019.05.086

Yang, Z., Liu, W., Ouyang, H., Liu, Q., Cai, S., Wang, C., Xie, J., & Hu, W. (2022). Image Recognition for Garbage Classification Based on Transfer Learning and Model Fusion. Mathematical Problems in Engineering, 2022, 4793555. https://doi.org/10.1155/2022/4793555

Kaya, V. (2023). Classification of waste materials with a smart garbage system for sustainable development: a novel model. Front. Environ. Sci., 11, 1228732. https://doi.org/10.3389/fenvs.2023.1228732

Nowakowski, P., & Pamuła, T. (2020). Application of deep learning object classifier to improve e-waste collection planning. Waste Management, 109, 1-9. https://doi.org/10.1016/j.wasman.2020.04.041.

Malik, M., Sharma, S., Uddin, M., Chen, C., Wu, C., Soni, P., & Chaudhary, S. (2022). Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models. Sustainability, 14, 7222. https://doi.org/10.3390/su14127222

Chen, Z., Yang, J., Chen, L., Jiao, H., & Chen, L. (2022). Garbage classification system based on improved shufflenet v2. Resour. Conservation Recycl., 178, 106090–106111. https://doi.org/10.1016/j.resconrec.2021.106090

De Carolis, B., & Macchiarulo, F. N. (2020). Yolo trashnet: Garbage detection in video streams. 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), 1–7. https://doi.org/10.1109/EAIS48028.2020.9122693

Nair, V., & Hinton, G. E. (2010). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning.

Dong, Z. (2021). Intelligent garbage classification system based on computer vision. Int. Core J. Eng., 7 (4), 147–152. https://doi.org/10.6919/ICJE.202104_7(4).0021

Endah, S. N., & Shiddiq, I. N. (2020). Xception architecture transfer learning for garbage classification. 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), 1–4. https://doi.org/10.1109/ICICoS51170.2020.9299017

Fu, B., Li, S., Wei, J., Li, Q., Wang, Q., Wei, J., et al. (2021). A novel intelligent garbage classification system based on deep learning and an embedded Linux system. IEEE Access, 9, 131134–131146. https://doi.org/10.1109/ACCESS.2021.3114496

Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., et al. (2017). Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint, 1-9. https://arxiv.org/abs/1704.04861

Kang, Z., Yang, J., Li, G., & Zhang, Z. (2020). An automatic garbage classification system based on deep learning. IEEE Access, 8, 140019–140029. https://doi.org/10.1109/ACCESS.2020.3010496

Kuang, Y., & Lin, B. (2021). Public participation and city sustainability: Evidence from urban garbage classification in China. Sustain. Cities Soc., 67, 102741–102811. https://doi.org/10.1016/j.scs.2021.102741

Liu, W., Ouyang, H., Liu, Q., Cai, S., Wang, C., Liu, Q., et al. (2022). Image recognition for garbage classification based on transfer learning and model fusion. Math. Problems Eng., 2022, 1–12. https://doi.org/10.1155/2022/4793555

Luo, Q., Lin, Z., Yang, G., Zhao, X., & Yang, G. (2021). Dec: A deep-learning based edge-cloud orchestrated system for recyclable garbage detection. Concurrency Comput. Pract. Exp., 35, 1–8. https://doi.org/10.1002/cpe.6661

Ma, W., & Zhu, Z. (2021). Internet use and willingness to participate in garbage classification: An investigation of Chinese residents. Appl. Econ. Lett., 28 (9), 788–793. https://doi.org/10.1080

Bhattacharya, S., Sai, K. B., H. S., Puvirajan, H., Peera, H., & Jyothi, G. (2023). Automated Garbage Classification using Deep Learning. 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 404-410. https://doi.org/10.1109/ICAAIC56838.2023.10141483

Hoornweg, D., & Bhada-Tata, P. (2012). A Global Review of Solid Waste Management. 1-116.

Johansson, N., & Corvellec, H. (2018). Waste policies gone soft: An analysis of European and Swedish waste prevention plans. Waste Manag., 77, 322-332.

Singh, J., Laurenti, R., Sinha, R., & Frostell, B. (2014). Progress and challenges to the global waste management system. Waste Management & Research, 32, 800-812. https://doi.org/10.1177/0734242X14537868

O’Reilly, R.C., Wyatte, D., Herd, S., Mingus, B., & Jilk, D.J. (2013). Recurrent processing during object recognition. Front. Psychol., 4, 1-12.

Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Iclr, 1-14.

Khanal, A., Giri, S., & Mainali, P. (2023). The Practices of At-Source Segregation of Household Solid Waste by the Youths in Nepal. Journal of Environmental and Public Health, 2023, 5044295. https://doi.org/10.1155/2023/5044295

Zhang, S., & Forssberg, E. (1999). Intelligent Liberation and classification of electronic scrap. Powder Technology, 295-301.

Liu, C., Sharan, L., Adelson, E.H., & Rosenholtz, R. (2010). Exploring features in a Bayesian framework for material recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 239-246.

Singh, J., Laurenti, R., Sinha, R., & Frostell, B. (2014). Progress and challenges to the global waste management system. Waste Management & Research, 32(9), 800-812. https://doi.org/10.1177/0734242X14537868

Mittal, G., Yagnik, K.B., Garg, M., & Krishnan, N.C. (2016). SpotGarbage: Smartphone app to detect garbage using deep learning. UbiComp 2016 - Proc. 2016 ACM Int. Jt. Conf. Pervasive Ubiquitous Comput.

Thung, G., & Yang, M. (2016). Classification of Trash for Recyclability Status, 940-945.

LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proc. IEEE, 86, 2278-2288.

Leung, M.K.K., Xiong, H.Y., Lee, L.J., & Frey, B.J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30, 10-15.

Seay, J.R. (2022). The global plastic waste challenge and how we can address it. Clean Techn Environ Policy, 24, 729–730. https://doi.org/10.1007/s10098-021-02271-0

Published
2024-02-06
How to Cite
[1]
B. Paneru, R. Poudyal, B. Paneru, K. B. Shah, and K. N. Poudyal, “Revolutionizing Waste Management: A Cutting-edge pyTorch Model for Waste Classification and Prediction, Coupled with a User-friendly Recycling Recommendation Application Built with Tkinter: Waste management application with tkinter and deep learning”, Indones.J.electronic.electromed.med.inf, vol. 6, no. 1, pp. 43-51, Feb. 2024.
Section
Research Article