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Technical solutions for waste classification and management: A mini-review
The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about se...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108328/ https://www.ncbi.nlm.nih.gov/pubmed/36377610 http://dx.doi.org/10.1177/0734242X221135262 |
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author | M, Shreya V, Nimal Yughan Katyal, Jyotika R, Ramesh |
author_facet | M, Shreya V, Nimal Yughan Katyal, Jyotika R, Ramesh |
author_sort | M, Shreya |
collection | PubMed |
description | The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission. |
format | Online Article Text |
id | pubmed-10108328 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-101083282023-04-18 Technical solutions for waste classification and management: A mini-review M, Shreya V, Nimal Yughan Katyal, Jyotika R, Ramesh Waste Manag Res Mini-Review Articles The massive growth of population coupled with urbanization over the years has created a significant challenge of increase in waste generation. India has achieved massive developmental growth in economic and social areas but still lacks a proper waste management system. The lack of knowledge about segregation of waste into different categories and proper disposal techniques in a country like India with an accelerated population growth is a critical issue. Since trash has different disposal techniques, according to its type, segregating waste through an automated process at the point of collection will streamline the process and result in effective waste management and utilization. The mini-review article evaluates the recent literature on technologies used for municipal waste segregation and management, with the motive of providing critical information for advancement in current research. This article reviews the use of various convolutional neural network architectures for waste classification and describes in detail as to why image processing methods are preferred over sensors for segregation into respective categories. It is also important to have an efficient waste monitoring and management system for proper disposal. A comprehensive mini-review was undertaken to understand internet of things-based models proposing efficient waste handling, from the perspective of reduced costs, collection time and optimized routes. The proposed systems were compared and evaluated based on the sensors used monitoring, microcontrollers and communication protocols such as Long Range, Global System for Mobile Communication, Zigbee and Wi-Fi, which are employed for the secure and efficient data transmission. SAGE Publications 2022-11-15 2023-04 /pmc/articles/PMC10108328/ /pubmed/36377610 http://dx.doi.org/10.1177/0734242X221135262 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Mini-Review Articles M, Shreya V, Nimal Yughan Katyal, Jyotika R, Ramesh Technical solutions for waste classification and management: A mini-review |
title | Technical solutions for waste classification and management: A
mini-review |
title_full | Technical solutions for waste classification and management: A
mini-review |
title_fullStr | Technical solutions for waste classification and management: A
mini-review |
title_full_unstemmed | Technical solutions for waste classification and management: A
mini-review |
title_short | Technical solutions for waste classification and management: A
mini-review |
title_sort | technical solutions for waste classification and management: a
mini-review |
topic | Mini-Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10108328/ https://www.ncbi.nlm.nih.gov/pubmed/36377610 http://dx.doi.org/10.1177/0734242X221135262 |
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