Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)

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AI-Powered Waste Segregation and Carbon Footprint Tracking with a Waste Trading Platform

Authors
V. Vanitha1, *, Shaheen Begum1, G. Sanjay1, G. Saran1
1Department of Electronics and Communication Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
*Corresponding author. Email: vanithav@srmist.edu.in
Corresponding Author
V. Vanitha
Available Online 30 June 2025.
DOI
10.2991/978-94-6463-754-0_77How to use a DOI?
Keywords
Waste segregation; Raspberry Pi; IoT; object detection; weight sensor; carbon footprint tracking; real-time monitoring; automation; waste trading
Abstract

Environmental sustainability and resource optimization depend on efficient waste management. This article offers a smart system that promotes a circular economy, enhances recycling efficiency, and automates waste categorization. The system uses an automated gripper arm, computer vision-based object detection, and weight-based material classification to combine these three components for precise sorting into designated bins. A Raspberry Pi-based control device that interprets sensor data and links to a cloud-based platform via ESP8266 Wi-Fi enables data transmission. A mobile app providing real-time bin status updates, carbon footprint analysis, and a garbage trading platform could help buyers and garbage collectors trade more easily. In keeping with Sustainable Development Goal (SDG) 9: Industry, Innovation, and Infrastructure, this approach uses IoT, cloud computing, and machine learning to assist technical advances in smart waste management. Based on testing findings demonstrating great classification accuracy, real-time tracking efficiency, and notable environmental advantages, the technology is positioned as a scalable and intelligent solution for future smart cities.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
Series
Atlantis Highlights in Engineering
Publication Date
30 June 2025
ISBN
978-94-6463-754-0
ISSN
2589-4943
DOI
10.2991/978-94-6463-754-0_77How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - V. Vanitha
AU  - Shaheen Begum
AU  - G. Sanjay
AU  - G. Saran
PY  - 2025
DA  - 2025/06/30
TI  - AI-Powered Waste Segregation and Carbon Footprint Tracking with a Waste Trading Platform
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 890
EP  - 900
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_77
DO  - 10.2991/978-94-6463-754-0_77
ID  - Vanitha2025
ER  -