Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64851
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of CSE(AI&ML)
CMR Technical Campus
Department of CSE(AI&ML)
CMR Technical Campus
Department of CSE(AI&ML)
CMR Technical Campus
The significant contribution of the pollution and health risks on the population is improper waste segregation. It is tedious, unsanitary, and inaccurate as people do not understand the process that should be used in sorting waste manually. The current paper is devoted to a Smart Garbage Segregation Mobile Application that facilitates Artificial Intelligence and image classification methods to identify and classify garbage automatically. People can scan or upload pictures of waste products and the system identifies them as biodegradable, recyclable, hazardous or general waste. Clear instructions on disposal and bin suggestions are given in the application, which promotes proper disposal of waste. Points and badges are some of the Gamification features present to attract more user engagement and foster sustainable behavior. The suggested system will be an effective, scalable, and user-friendly solution to enhance waste separation and waste sustainability by integrating machine learning, cloud services, and mobile technology.
KEYWORDS
Ultimately, the Smart Garbage Waste Segregation App is a viable and convenient product to enhance the day-to-day waste management behaviors. The system can effectively eliminate the need to manually input the type of waste by enabling people to take a picture of the waste materials or upload them and the system will be capable of telling them how and where to dispose it. This eliminates the misunderstanding of the segregation of wastes and assists the users to make right choices without the need to be technically or environmentally knowledgeable. The application will not be aimed at substituting the current waste management systems and sanitation procedures, but helping people to learn and apply correct waste segregation in their origin. The system can be useful in various users because of such features as AI-based image classification, real-time feedback, secure data handling, and user-centric interface. Moreover, the educational material and gamification raise the long-term interaction and responsible behaviour. Generally, the project shows that modern technologies can be properly exploited to simplify, increase the reliability and actionability of waste segregations, which helps to make the environment less polluted and more sustainable.
1. Kaza, S., Yao, L., Bhada-Tata, P., & Van Woerden, F., What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, World Bank Publications, 2018. 2. LeCun, Y., Bengio, Y., & Hinton, G., “Deep Learning,” Nature, Vol. 521, No. 7553, pp. 436–444, 2015. 3. Krizhevsky, A., Sutskever, I., & Hinton, G. E., “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems (NIPS), pp. 1097–1105, 2012. 4. He, K., Zhang, X., Ren, S., & Sun, J., “Deep Residual Learning for Image Recognition,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016. 5. Redmon, J., & Farhadi, A., “YOLO: You Only Look Once – Unified, Real-Time Object Detection,” CVPR, 2016. 6. World Health Organization (WHO), Health-Care Waste Management Guidelines, WHO Publications, 2020. 7. United Nations Environment Programme (UNEP), Solid Waste Management and Recycling, UNEP Reports, 2019. 8. Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning, MIT Press, 2016. 9. Simonyan, K., & Zisserman, A., “Very Deep Convolutional Networks for Large-Scale Image Recognition,” International Conference on Learning Representations (ICLR), 2015. 10. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A., “Going Deeper with Convolutions,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–9, 2015.
Publication Date
6/12/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
4
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...