Towards Sustainable E-Waste Management: A Computer Vision and Machine Learning Approach for Identification and Valuation

Electronic garbage (e-waste) from electronic devices is a major environmental and resource management concern. According to this research, E-waste identification and evaluation should be done using computer vision and machine learning. The purpose of this study is to create a system to identify and value e-waste. The robust feature extraction and comparison of the YOLOv8 object recognition method is used. The system extracts and identifies features by photographing e-waste components on a platform. This project needs a complete e-waste dataset. YOLOv8 learns to identify and classify electrical components from this dataset. Improving the model’s performance enhances e-waste differentiation. This approach values e-waste after identification. Get component pricing info from databases or APIs. Brand, condition, and functionality may help users evaluate their e-waste assets’ economic potential. The campaign emphasizes e-waste management principles. The project must follow data privacy rules and appropriately dispose of garbage to achieve social and environmental objectives. A simple interface allows people to upload e-waste photos for identification and appraisal. This program helps consumers and companies value e-waste for sustainable device lifetime management. This effort makes detecting and valuing e-waste practical. Environmentalism and cutting-edge technology create a more sustainable and resource-efficient future.