Design and Implementation of an Automatic Waste Sorting Machine for Efficient Waste Management

The rapid increase in waste generation due to urbanization and industrialization has led to severe environmental and operational challenges. Inefficient waste segregation contributes to landfill overflow, pollution, and reduced recycling efficiency. This paper presents the Automatic Waste Sorting Machine (AWSM), an efficient system for automated waste classification and sorting. The AWSM integrates capacitive, inductive, and ultrasonic sensors to detect different waste materials, including plastics, metals, and glass. An AI-based vision system enhances classification accuracy. A microcontroller processes sensor data in real-time, controlling a servo-driven gate and a rotating pipe system to direct waste into designated bins. The prototype was tested under controlled conditions, achieving an average classification accuracy of over 90%. The system offers a cost-effective, scalable, and adaptable solution for improving waste management in smart cities and industrial applications. The results indicate that AWSM can enhance recycling efficiency, reduce landfill dependency, and contribute to sustainable waste management practices.