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

Dhanvanth Kumar Gude, Harshavardan Bandari, Anjani Kumar Reddy Challa, Sabiha Tasneem, Zarin Tasneem, Shyama Barna Bhattacharjee, Mohit Lalit, Miguel Angel López Flores and Nitin Goyal ()
Additional contact information
Dhanvanth Kumar Gude: Apex Institute of Technology (AIT-CSE), Chandigarh University, Mohali 140413, Panjab, India
Harshavardan Bandari: Apex Institute of Technology (AIT-CSE), Chandigarh University, Mohali 140413, Panjab, India
Anjani Kumar Reddy Challa: Apex Institute of Technology (AIT-CSE), Chandigarh University, Mohali 140413, Panjab, India
Sabiha Tasneem: Department of Allied Sciences, Faculty of Science, Engineering and Technology (FSET), University of Science and Technology Chittagong (USTC), Chattogram 4220, Bangladesh
Zarin Tasneem: Department of Allied Sciences, Faculty of Science, Engineering and Technology (FSET), University of Science and Technology Chittagong (USTC), Chattogram 4220, Bangladesh
Shyama Barna Bhattacharjee: Department of Computer Science and Engineering, Faculty of Science, Engineering and Technology (FSET), University of Science and Technology Chittagong (USTC), Chattogram 4220, Bangladesh
Mohit Lalit: Apex Institute of Technology (AIT-CSE), Chandigarh University, Mohali 140413, Panjab, India
Miguel Angel López Flores: Engineering Research & Innovation Group, Universidad Europea del Atlántico, C/Isabel Torres 21, 39011 Santander, Spain
Nitin Goyal: Department of Computer Science and Engineering, School of Engineering and Technology, Central University of Haryana, Mahendergarh 123031, Haryana, India

Sustainability, 2024, vol. 16, issue 17, 1-21

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.

Keywords: Internet of Things; deep learning; smart city; LoRaWAN; sanitation; healthcare (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/17/7626/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/17/7626/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:17:p:7626-:d:1470250

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().