Automated Resource Recovery Processes for Electronic Waste Recycling

Electronic waste (e-waste) is a heterogeneous mix of hazardous, valuable and environmentally sensitive materials that demand careful handling to recover resources and prevent pollution. Relying on landfills not only squanders reusable components but also poses serious health and environmental risks. Although material recovery facilities (MRFs) exist, manual sorting remains slow, error-prone and unsafe, creating a clear need for automation. To address this, we propose a computer-vision system that identifies and classifies e-waste items in cluttered nature, paving the way for robotic arms to pick, sort and redirect materials for recycling or reuse. By training models to recognize shapes and textures specific to common e-waste categories, our approach improves sorting speed, accuracy and safety. This solution can be integrated into existing MRFs across industries, boosting resource recovery rates and supporting more sustainable waste management practices.