Leveraging CNN and IoT for Effective E-Waste Management
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908436298465280 |
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| author | Nadar, Ajesh Thangaraj Raj, Gabriel Nixon Chandane, Soham Bhat, Sushant |
| author_facet | Nadar, Ajesh Thangaraj Raj, Gabriel Nixon Chandane, Soham Bhat, Sushant |
| contents | The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_16647 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging CNN and IoT for Effective E-Waste Management Nadar, Ajesh Thangaraj Raj, Gabriel Nixon Chandane, Soham Bhat, Sushant Computer Vision and Pattern Recognition 68T05 (Primary), 68T01 (Secondary) I.2.10; C.3; J.2 The increasing proliferation of electronic devices in the modern era has led to a significant surge in electronic waste (e-waste). Improper disposal and insufficient recycling of e-waste pose serious environmental and health risks. This paper proposes an IoT-enabled system combined with a lightweight CNN-based classification pipeline to enhance the identification, categorization, and routing of e-waste materials. By integrating a camera system and a digital weighing scale, the framework automates the classification of electronic items based on visual and weight-based attributes. The system demonstrates how real-time detection of e-waste components such as circuit boards, sensors, and wires can facilitate smart recycling workflows and improve overall waste processing efficiency. |
| title | Leveraging CNN and IoT for Effective E-Waste Management |
| topic | Computer Vision and Pattern Recognition 68T05 (Primary), 68T01 (Secondary) I.2.10; C.3; J.2 |
| url | https://arxiv.org/abs/2506.16647 |