Smart Recycling by Harnessing Machine Learning Techniques

Deep learning offers an innovative solution for automating waste classification, essential for effective recycling and waste management. Given the rise in trash production worldwide, advanced technologies are needed to improve the accuracy of the sort. Models like convolutional Neural Networks, particularly using the VGG16 architecture, classify waste into categories such as plastic, glass, metal, organic, and hazardous materials from image data. This reduces human error, cuts labor costs, and enhances sorting efficiency. Integrating deep learning with IoT devices enables real-time monitoring and data collection, providing insights into waste trends. These systems improve recycling rates, reduce landfill dependency, and support sustainable waste management. This paper explores deep learning architectures for waste classification, challenges in data acquisition, and their role in urban waste management, emphasizing AI’s potential in driving environmental sustainability and resource recovery.