Published Paper Details:

: Plastic waste scattered in public spaces such as streets, parks, and industrial areas poses a major environmental concern. Traditional waste collection methods are labor-intensive, time-consuming, and often ineffective in covering large areas. To address this issue, an intelligent robotic system is proposed for the detection and collection of plastic bottle waste. The system utilizes a Raspberry Pi to control the robot and process input data. For object detection, a deep learning model, YOLOv8, based on Convolutional Neural Networks , is employed to identify and classify plastic bottles in real-time camera footage. The model is trained on a curated dataset of plastic waste images to ensure reliable detection. Once a plastic bottle is identified, the robot uses a mechanical arm to pick it up and place it into a collection bin. This system enhances the efficiency and safety of waste collection, particularly in hard-to-reach or hazardous areas. By reducing human involvement and automating the process, the proposed solution contributes to a cleaner, smarter, and more sustainable environment.