The rapid increase in electronic waste has created a need for efficient and safe segregation methods to reduce environmental impact and improve recycling processes. This project proposes an automated e-waste segregation system that utilizes artificial intelligence (AI)– based image processing integrated with an embedded control system to classify and sort electronic components such as resistors, inductors, integrated circuits, and transistors. The system employs a camera to capture images of electronic components, and the AI model analyses these images to identify the component type based on visual features. The classification result is then transmitted to a microcontroller, which controls a relaydriven DC motor to rotate a bin toward the appropriate compartment. A predefined delay ensures accurate positioning of the bin for proper alignment. Once the bin reaches the desired position, a servo motor mechanism opens a gate to allow the component to drop into the correct section and then closes it after disposal. To ensure complete automation and reliability, a limit switch is used to detect the home position of the rotating bin. After each sorting cycle, the system automatically returns to its initial position. Additionally, an LCD display provides real-time feedback on the detected component and system status, enhancing user interaction. The proposed system offers a cost-effective, efficient, and intelligent solution for e-waste segregation by combining AI, automation, and embedded systems, thereby reducing manual effort and improving sorting accuracy.
S.Kavitha et al. (Thu,) studied this question.