Sustainable Collection and Classification of E-Waste: A Proposed Computer Vision Technology-Based Framework

Abstract

With enhanced technological advancements and globalization, there has been a significant increase in the amount of electronic waste (e-waste), posing a global environmental threat. Thus, it is imperative for nations today, to employ effective e-waste handling and disposal mechanisms. In fact, this is all the more applicable to developing nations, where e-waste has been a plaguing problem due to improper e-waste collection and classification procedures. Often, useful and valuable parts and/or components, and at times the whole electronic product itself are either destroyed and/or landfilled. This practice jeopardizes the environment, as unclassified e-waste might contain hazardous chemicals/materials, which in turn, could risk public health. To address the aforementioned issues, this study implements grey prediction model to map and forecast e-waste generation in India. Secondly, it proposes a sustainable collection and classification framework by integrating computer vision technology to manage the plaguing challenge across developing nations, based upon the result of prediction. Finally, this study highlights pertinent sustainable advantages of the proposed framework by drawing upon the Triple Bottom Line theory.

Keywords: automated e-waste classification, Computer vision, e-waste processing, forecasting, grey modeling, sustainable practices.