Enhancing Sustainability through Automated Waste Classification: A Machine Intelligence Framework

Abstract

This study presents a novel framework integrating a deep learning image classifier into waste classification systems for enhancing sustainability. Leveraging diverse waste image datasets, our approach employs a convolutional neural network (CNN) architecture tailored for precise waste material identification and sorting from images. Through transfer learning and dataset augmentation techniques, the CNN model demonstrates robust performance in real-time waste categorization, surpassing conventional methods. Experimental validation using comprehensive waste image datasets showcases notable advancements in classification accuracy and operational efficiency. The results underscore the potential of deep learning image classifiers in optimizing waste sorting processes, contributing to more effective recycling strategies, and promoting environmental sustainability. This research emphasizes the practical implications of integrating deep learning techniques into waste management systems, offering actionable insights for stakeholders and waste management professionals seeking innovative solutions for sustainable waste handling.