Year: 2025
Type: article
Abstract: Garbage disposal is one of the challenging issues today in urban society, where incorrect segregation of garbage results in pollution and ineffective recycling processes. This project proposes an AI-Based Solid Waste Management System employing Sortbot to segregate waste automatically with high precision and efficiency. The suggested system takes advantage of Deep Learning using the AlexNet50 convolutional neural network (CNN) to classify the waste into various categories including biodegradable, non-biodegradable, and metallic waste. The system comes with multiple sensors such as ultrasonic sensors used for detecting objects and metal detectors used to detect metallic waste. The waste image is taken using a camera module, processed through the deep learning algorithm to identify the type of waste. Based on the classification outcome, a servo motor-driven sorting mechanism sends the waste to the right bin. The system also has GSM communication to offer real-time alerts on bin status. The proposed model is efficient in segregating waste, minimizes human involvement, and improves the recycling process, all in an effort to provide a sustainable environment. Experimental results prove the accuracy and performance of the Sortbot system in waste classification, making it an effective solution for smart waste management applications.
Authors: Sivajothi Kavitha S, Dr. V. Jamuna, Usha D, Dhanalakshmi T, Divya Lakshmi S
Institution: Jerusalem College of Engineering
Language: English
Subfield: Waste Management and Disposal
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410100007