International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 2 (March-April 2026) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 2
March-April 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Computer Vision Approaches for Waste Segregation: A Survey of Methods, Datasets, and Challenges

Author(s) Mr. A M Sanjeev
Country India
Abstract Municipal solid waste management has emerged as a critical environmental challenge due to rapid urbanization and increasing waste generation. Manual waste segregation is often inefficient, labour-intensive, and prone to inconsistency, leading to growing interest in automated approaches. In recent years, computer vision and machine learning techniques have been increasingly explored for image-based waste segregation, enabling the classification of waste materials using visual information.
This paper presents a survey of existing computer vision-based approaches for waste segregation reported in the literature. The review covers the evolution of image-based waste classification methods, including early feature-based techniques, classical machine learning models, and recent deep learning-based approaches. Publicly used datasets and commonly adopted evaluation practices in academic studies are also discussed at a high level.
In addition to summarizing existing methods, this survey highlights key challenges identified across prior studies, such as dataset limitations, environmental variability, class imbalance, and generalization issues in real-world scenarios. Based on the reviewed literature, open research gaps and future directions are outlined to guide researchers entering this domain. This survey focuses on synthesizing existing literature and identifying open challenges in image-based waste segregation.
Keywords Waste Segregation, Computer Vision, Machine Learning, Deep Learning, Municipal Solid Waste, Image Based Classification
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-21
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.64204
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2Cover Page Vol 8 Isu 1Cover Page Vol 7 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals