An Identification and Categorization of Plastic Material using Deep Learning Approach

Viswanadham, Yedlapalli Kasi; Rani, Ravipati Sandhya; Mai, Varanasi Madhu Sri Lakshmi Sudha; Krishna, Vaka Naga Vamsi; Vamsi, Yaragani Veera

doi:10.1109/icpcsn58827.2023.00054

2023 3rd International Conference on Pervasive Computing and Social Networking (ICPCSN) 2023

DOI: 10.1109/icpcsn58827.2023.00054

|View full text |Cite

An Identification and Categorization of Plastic Material using Deep Learning Approach

Yedlapalli Kasi Viswanadham,

Ravipati Sandhya Rani,

Varanasi Madhu Sri Lakshmi Sudha Mai

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review

Ramos,

Lopes,

Mendonça

2024

Processes

View full text Add to dashboard Cite

The intersection of artificial intelligence and environmental sustainability has become a relevant exploration domain in the contemporary era of rapid technological advancements and complex global challenges. This work reviews the application of machine learning (ML) models to address the pressing issue of plastic waste (PW) management. By systematically examining the state of the art with snowballing, this research aims to determine the efficiency and effectiveness of ML-based methods for PW detection and classification. Considering the increasing environmental concerns and information processing potential, this article hypothesised that ML models could contribute to more sustainable PW management practices. For this purpose, two scientific article repositories were examined from 2000 to 2023, and 188 articles were identified. After the systematic screening procedure, 28 were selected. Additionally, 28 more articles were included by snowballing. It was observed that accuracy in either detection or classification problems often exceeded the 80% detection accuracy benchmark, further improving when the model combination was employed. As a result, strong support was reached for the applicable potential of ML in PW. It was also concluded that models based on convolutional neural networks were the most commonly used.

show abstract

Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review

Ramos,

Lopes,

Mendonça

2024

Processes

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Identification and Categorization of Plastic Material using Deep Learning Approach

Cited by 1 publication

References 10 publications

Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review

Application of Machine Learning in Plastic Waste Detection and Classification: A Systematic Review

Contact Info

Product

Resources

About