Automatic Waste Segregation System Based on Image and Audio Data

Abstract

This paper explores the creation of an Automated Waste Segregation System intended to make waste management more efficient through real-time waste material classification and disposal. The system applies machine learning models, executed using TensorFlow Lite, in audio and image-based classification of waste. The hardware architecture includes a Raspberry Pi 4 as the central processing unit, which communicates with sensors and actuators for automatic waste recognition and sorting. The hardware structure of the system is made of PVC pipes and PVC sheets so that the overall structure of the waste compartments remains lightweight, inexpensive, and long-lasting. The mechanized segregation system is driven by servo motors and an efficient motor system to route waste to corresponding bins. The analytical system combines sensor-based data acquisition, real-time processing, and AI-based decision-making logic to segregate waste into types like biodegradable, recyclable, and non-recyclable. The new system will provide higher precision and effectiveness of garbage disposal in both domestic and commercial areas, leading to enhanced sustainability and garbage handling techniques.