Deep Learning in Sorting and Classification for Automated E-Waste Recycling

The application of deep learning in recycling facilities has gained traction, offering promising solutions for automated e-waste sorting and classification. With computer vision advancements, neural networks now enable accurate detection and categorization of electronic components, helping enhance recycling efficiency. This paper explores various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and their hybrid variants, for classifying e-waste components. By training these models on labeled e-waste datasets, we achieved high classification accuracy, demonstrating the potential of deep learning to reduce manual labor in recycling facilities. This study further discusses hardware and software implementations essential for deploying these models in realworld recycling environments, as well as the results obtained through empirical evaluation of each architecture. E-waste demands urgent attention to devise effective and sustainable waste management solutions. In response to this challenge, the integration of deep learning classifiers emerges as a promising avenue to optimize the planning of e-waste collection. By harnessing the capabilities of deep learning, these classifiers yield enriched navigational insights, facilitating more informed asset allocation and strategic interventions. This synthesis of technology-driven planning holds the potential to address the intricate intricacies of e-waste in a sustainable environmental landscape. Furthermore, the identification of important parts in e-waste helps in improving recycling processes, adding to a more maintainable and harmless ecosystem. The matter presented in this article is the review of group of papers and is the summary of those papers which was submitted in the form of an assignment / as an alternate assessment tool / case study.