Title: Artificial Intelligence for Intelligent Waste Sorting: An Efficient and Scalable System
Authors: Nesreen S. Aljerjawi and Samy S. Abu-Naser
Volume: 9
Issue: 8
Pages: 114-117
Publication Date: 2025/08/28
Abstract:
The escalating global waste crisis and the limitations of traditional manual sorting methods necessitate a transformative approach to recycling. This paper proposes a conceptual and technical framework for an intelligent waste sorting system leveraging artificial intelligence (AI) and computer vision. The core of the system is a deep learning model, specifically a Convolutional Neural Network (CNN), designed to classify diverse waste materials with high accuracy and speed. Unlike conventional rule-based expert systems, this data-driven methodology is uniquely suited to handle the inherent imprecision and variability of real-world waste streams. The system integrates this AI core with a high-speed camera, a conveyor belt, and robotic sorting arms to achieve real-time, automated sorting. We present a detailed methodology, discuss the significant advantages in sorting accuracy, operational speed, and scalability, and contrast this approach with traditional expert systems. This research concludes that an AI-powered waste sorting system offers a highly effective and scalable solution to enhance recycling efficiency and promote global sustainability.