Smart Waste Segregation System

Abstract

As urbanization accelerates, the volume of solid waste generated has dramatically increased, presenting considerable hurdles for efficient and sustainable waste management. Traditional methods of manual waste segregation are often laborious, inconsistent, and can pose risks to personnel. To overcome these challenges, this article introduces a Smart Garbage Segregation System that integrates the Internet of Things, deep learning, and image processing technologies to automate waste classification and sorting. The system employs a high-resolution camera to capture images of waste materials, which are then categorized into e-waste, non-recyclable, and biodegradable groups using a Convolutional Neural Network model. Subsequently, a microcontroller-driven servo mechanism guides each item into its appropriate container. Cloud connectivity, facilitated by Firebase and MQTT, allows for real-time data analysis and continuous monitoring. This proposed solution aims to minimize human involvement, achieves an accuracy rate exceeding 85%, and promotes more sustainable waste management practices within smart cities.