A Review Paper on: "Automated Waste Classification with Image Processing and Deep Learning for a Circular Economy"
Year: 2025
Type: article
Abstract: With the growing population and urbanization, solid waste generation is rapidly increasing, creating serious environmental and health concerns. Traditional manual waste segregation is slow, labor-intensive, and often inaccurate, leading to lower recycling rates and more pollution. To address this, we propose a Smart Waste Segregation System that uses image processing and Convolutional Neural Networks (CNNs) to automatically classify waste into recyclable, organic, and non-recyclable categories. This automated system improves accuracy, efficiency, and speed while reducing labor costs and human error. By integrating sensors and machine learning, the system enables real-time waste sorting at the source, promoting safer working conditions, better recycling, and a cleaner environment. Our results show high performance based on key evaluation metrics, supporting sustainable waste management and contributing to a circular economy.
Authors: Dr Pusparani M K, Bhramara Katagi, Nithya Jain G S, Sudeep P M, Varun S
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4413985770