Review on Development of E-Waste Management System

Abstract

Today, the advancement of technology has the power to change everyone's life. Although this innovation is beneficial, it creates serious effects on human health and environmental health. One of the main reasons for this is "e-waste" resulting from electronic products. With the use of electronic products all over the world, the amount of "e-waste" or e-waste has also increased and this has now become a serious problem. Improper disposal of e-waste has become an environmental and public health problem as it now accounts for the largest portion of water litter in the world's cities. Therefore, correct classification and management of e-waste requires the recovery of important information about waste. These growing wastes are hard in nature and rich in metals such as neodymium, indium, palladium, tantalum, platinum, gold, silver, lead and copper, which can be recovered from discards and brought back to the earth. Production cycle and daily use. In this project, a deep learning model is used to identify e-waste and general waste using image processing. The design model, on the other hand, selects the waste with good accuracy and takes less time. Wastes are divided into two groups according to the amount or value in the waste. By using this model effectively, we can solve e-waste management problems, improve recycling and contribute to environmental sustainability.