Waste Classification Using NasNet-Mobile: A Multi-Stage Deep Learning Approach for Environmental Sustainability

Authors

Hui Ching Yoong
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
Siat Ling Jong
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
Kim Gaik Tay
Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
DOI: https://doi.org/10.52549/ijeei.v13i4.7198
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Published: 04-12-2025 • Issue: Vol. 13 No. 4: December 2025 • Section: Regular paper

Abstract

Improper waste management remains a significant global challenge, resulting in severe environmental and health impacts. Existing classification systems were designed and studied on large deep learning models, which are computationally expensive and not well-suited for embedded systems. To overcome this challenge, this study introduces a lightweight NasNet Mobile architecture that was trained using a three-stage learning framework. The framework employs transfer learning, fine-tuning, and hyperparameter optimisation to improve the model’s performance and generalisation capabilities progressively. To validate the proposed approach, experimental evaluations were conducted on TrashNet and Garbage Classification datasets. The model achieved an accuracy of 91.25% on the TrashNet dataset and 94.85% on the Garbage Classification dataset using the optimal hyperparameter set obtained through the random search technique. These results indicate that the proposed strategy effectively adapts to varying data distributions and outperforms popular Convolutional Neural Network (CNN) architectures, such as VGG-16, ResNet, AlexNet, etc. Therefore, the proposed model provides a reliable foundation for developing scalable and efficient waste classification systems for environmental applications. This study contributes to a practical deep learning approach that improves classification performance while maintaining low resource requirements for sustainable waste management.
Keywords: Waste classification system; NasNet-Mobile; Lightweight CNN; Transfer learning; Fine-tuning; Hyperparameter optimisation; Waste management

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Author Biographies

Hui Ching Yoong
Master’s Degree Student in Electrical Engineering
Siat Ling Jong
Senior Lecturer of the Department of Electronic Engineering
Kim Gaik Tay
Associate Professor of the Department of Electronic Engineering

How to Cite

[1]
H. C. Yoong, S. L. Jong, and K. G. Tay, “Waste Classification Using NasNet-Mobile: A Multi-Stage Deep Learning Approach for Environmental Sustainability”, IJEEI, vol. 13, no. 4, pp. 952–967, Dec. 2025, doi: 10.52549/ijeei.v13i4.7198.