Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing

Gude, Dhanvanth Kumar and Bandari, Harshavardan and Challa, Anjani Kumar Reddy and Tasneem, Sabiha and Tasneem, Zarin and Bhattacharjee, Shyama Barna and Lalit, Mohit and López Flores, Miguel Ángel and Goyal, Nitin UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, miguelangel.lopez@uneatlantico.es, UNSPECIFIED (2024) Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing. Sustainability, 16 (17). p. 7626. ISSN 2071-1050

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Abstract

The enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system.

Item Type:	Article
Uncontrolled Keywords:	Internet of Things; deep learning; smart city; LoRaWAN; sanitation; healthcare
Subjects:	Subjects > Engineering
Divisions:	Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	15 Dec 2025 09:36
Last Modified:	15 Dec 2025 09:36
URI:	http://repositorio.funiber.org/id/eprint/17896

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