International Research Journal of Education and Technology

Urban waste management faces significant challenges due to increasing population density and inadequate segregation practices at the source. Mixed household waste contains recyclable materials like plastic and glass that often end up in landfills, contributing to environmental degradation. This project proposes an intelligent automated waste segregation system that uses computer vision, IoT sensors, and mechanical actuation to separate plastic and glass waste from mixed household waste streams. The system employs a conveyor belt mechanism integrated with a camera module for real-time waste identification using image processing and machine learning algorithms. A servo motor-controlled rotating funnel mechanism redirects identified recyclable waste into dedicated collection bins while general waste continues to the main bin. This automated approach improves recycling rates, reduces manual sorting labor, minimizes contamination in waste streams, and supports smart city initiatives for sustainable waste management.

Keywords: Waste segregation, IoT, Smart cities, Computer vision, Machine learning, Automated sorting, Plastic recycling, Glass recycling, Sustainable waste management