Open Access

ARTICLE

DWaste: Greener AI for Waste Sorting Using Mobile and Edge Devices

Suman Kunwar^*

DWaste, Baltimore, MD 21218, USA

* Corresponding Author: Suman Kunwar. Email:

Journal on Artificial Intelligence 2026, 8, 39-49. https://doi.org/10.32604/jai.2026.076674

Received 24 November 2025; Accepted 30 December 2025; Issue published 22 January 2026

Abstract

The rise in convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy (≈96%) but suffered from high latency (≈0.22 s) and elevated carbon emissions. In contrast, lightweight object detection models delivered strong performance (up to 80% mAP) with ultra-fast inference (≈0.03 s) and significantly smaller model sizes (<7 MB), making them ideal for real-time, low-power use. Model quantization further maximized efficiency, substantially reducing model size and VRAM usage by up to 75%. Our work demonstrates the successful implementation of “Greener AI” models to support real-time, sustainable waste sorting on edge devices.

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Keywords

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