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Sustainable environmental management depends on effective waste segregation. Conventional techniques frequently involve manual sorting, which is laborious and prone to mistakes. This study suggests a machine learning-based method that automatically classifies waste materials into categories like organic and recyclable using static images. It does this by utilizing YOLOv8, a cutting-edge object detection model. The suggested method works with previously taken or user-uploaded images, doing away with the requirement for live camera feeds. A custom dataset of labeled waste images is used to train and evaluate the model. According to experimental results, YOLOv8 is a feasible option for clever and economical waste segregation systems because of its high accuracy and quick processing. Without the hassle of real-time video processing, this model can be combined with automated trash cans or sorting equipment to improve urban waste management
Keywords:
YOLOv8, waste segregation, object detection, smart bins, deep learning, sanitation
Cite Article:
"Automated Waste Management Using CNN Model", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.d17-d24, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504303.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator