Automated Waste Classification and Sorting System Using Deep Learning and Arduino

Abstract

Efficient waste management is crucial in reducing environmental pollution and promoting sustainability. This paper presents an automated waste classification and sorting system that integrates deep learning with Arduino-based hardware for real-time waste disposal. The system employs the YOLOv8 object detection model to classify waste into three categories: biodegradable, non-biodegradable, and hazardous. A laptop's integrated camera captures waste images, which are processed by a deep learning model to determine the appropriate bin for disposal. The hardware system consists of an Arduino board controlling servo motors that open designated pressable bins based on the classification. Experimental results demonstrate high classification accuracy and efficient real-time sorting, reducing human effort and enhancing waste management efficiency.