E-waste Management Using Deep Learning

Abstract

E-waste disposal poses a global concern because to its environmental impact and health risks from harmful compounds such as lead, mercury, and cadmium.This project uses Convolutional Neural Networks (CNNs), a type of deep learning, to automatically recognize, classify, and valuee-waste, helps turning obsolete electronics into useful materials. Device photos uploaded by sellers are evaluated for functionality, condition, and recycling potential before being assigned coin-based values to encourage environmentally friendly disposal.Sellers profit from safe coin-to-cash conversion, and buyers may peruse and buy products via an easy-to-use interface. This platform advances sustainability and environmental conservation by encouraging the reuse of electronic resources and reducing the accumulation of hazardous waste through recycling and the development of a circular economy.

Introduction

1. Introduction

With the rapid rise in electronic device usage, electronic waste (e-waste) is growing at an alarming rate. E-waste contains hazardous materials like lead, mercury, and cadmium, which pose serious health and environmental risks if not properly managed. Traditional manual sorting methods are labor-intensive, unsafe, and inefficient.

2. Proposed Solution

This project proposes an automated e-waste management system using Convolutional Neural Networks (CNNs) for:

• Image-based classification of e-waste

• Detection of item condition (damaged or usable)

• Automated sorting, reducing human error and labor

The system also integrates a user interface:

• Sellers upload images of their e-waste and get matched with buyers

• A coin-based incentive system motivates proper disposal

• Buyers can browse and purchase listed items securely

3. Literature Review Summary

Prior research has explored various AI-driven waste management approaches:

• Deep learning (Inception V3, EfficientNet, R-CNN, etc.) for accurate waste classification

• IoT integration for real-time monitoring and routing

• Smart systems using sensors (IR, ultrasonic), LoRa communication, and image recognition

• Benchmarked datasets and surveys show promising accuracy (over 90%) using CNNs and hybrid models (e.g., CNN + SVM)

4. System Architecture and Methodology

A. System Design

• Integrates seller and buyer modules

• Uses CNN for e-waste image classification

• Stores user and product data in a backend database

B. Methodology Steps

1. Requirement Analysis: Identify goals like automation, user incentives, and secure transactions

2. System Design: Create architecture, flow diagrams, and data schemas

3. Development: Build core modules and train CNN models for classification

4. Testing: Conduct functional, integration, and performance testing for accuracy and speed

5. Results

The system successfully allows:

• User registration and login

• Sellers to upload e-waste images

• Buyers to view and choose items

• OTP-based secure transactions

Screenshots show user interface steps, from registration to transaction.

Conclusion

The e-waste management system highlights how technology can contribute to sustainability by providing an automated, safe, and user-friendly platform for recycling electronic waste. It uses machine learning to ensure accurate item classification, provides recycling values, and incorporates features such as a coin-based incentive system, OTP-based verification, and data visualization to encourage users to adopt environmentally beneficial practices. Future enhancements may include enhancing the machine learning model\'s accuracy, developing a mobile app, integrating blockchain for transparency, and including gamification, multilingual support, and AI-powered recommendations to boost inclusivity and engagement.Partnerships with recycling firms, new reward options, and real-time analytics may all help to enhance logistics and optimize the platform. Incorporating sustainability metrics, two-factor authentication, and scalable cloud deployment would help to prepare the system for wider acceptability, changing it into an effective solution that promotes environmental change and broad sustainable e-waste management practices.

References

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Copyright

Copyright © 2025 Purushotham P, Rishitha P, Shravya N, Harshitha G. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.