YOLO for Environmental Sustainability: An Examination of Deep Learning Methods for  Intelligent Sorting of Garbage

Author(s)	Neeta Mohite, Sushma Nandgaonkar
Country	India
Abstract	Recycling and ecologically friendly waste management depend on the classification of waste. Conventional manual procedures are expensive, labour-intensive, and prone to mistakes. This can be resolved by using computer vision and deep learning methods to automatically recognize and categorize garbage.In this study, we investigate waste photo categorization using YOLOv8 (You Only Look Once, version 8), a single deep learning system that can specify and categorize items in real-time with a very high degree of accuracy.By training on a diverse dataset with six categories (cardboard, glass, metal, paper, plastic, and trash), the proposed YOLOv8-based method demonstrates significant improvements over conventional machine learning and earlier deep learning models.This approach promotes sustainable urban environments by ensuring reliable and efficient rubbish sorting.
Keywords	Computer vision, recycling, YOLOv8, deep learning, waste category, object detection, and sustainable disposal of Garbage.
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-24
DOI	https://doi.org/10.63363/aijfr.2026.v07i02.5191

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