Performance Analysis of Low-Computational Computer Vision Techniques for Recyclable Waste Identification.

Waste management on the low-resource setting is also a serious issue because of the shortage of infrastructure, and the lack of access to modern technologies, and the use of manual sorting of waste leads to environmental pollution and ineffective recycling. Although deep learning-based image classification has demonstrated the potential of automating waste, the majority of studies emphasize high-computation models, which cannot be used in resource-constrained environments. The research gap is a significant lack of systematic comparisons on both classification and computational efficiency of low-computational convolutional neural network (CNN) models. This study aim to examine the appropriateness of lightweight CNN models in the identification of recyclable wastes with a tradeoff between accuracy and resource consumption. Three low-computational CNN models, namely EfficientNet-Lite, MobileNetV2, and SqueezeNet, were trained and tested using publicly available datasets, such as TrashNet. Accuracy and F1-score were used to measure the classification performance, and latency, memory usage, and model size were used to measure computational efficiency. Findings show that EfficientNet-Lite had the best accuracy (92.4) and F1-score (92.0) but used more memory and inference time whereas MobileNetV2 provided a reasonable compromise between performance and efficiency. SqueezeNet was the lightest and the fastest model but with a lesser classification reliability. These results give practical recommendations on the design of AI-powered waste management in low-resource settings. This study helps to advance sustainable recycling efforts because it emphasizes the existence of the accuracy-latency-memory trade-offs of lightweight CNNs and informs practitioners and researchers on the need to use the right model whenever deploying lightweight CNNs to mobile, edge, or embedded applications in developing regions.

Recyclable Waste Identification, Low-Computational CNN, Computer Vision, Resource-Constrained Environments.

1. Ahmad, K., Khan, K., & Al-Fuqaha, A. (2020). Intelligent fusion of deep features for improved waste classification. IEEE Access, 8, 96495-96504. Retrieved from https://doi.org/10.1109/ACCESS.2020.3023092. [Google Scholar] [Crossref]

2. Bauravindah, A., & Fudholi, D. (2024). Lightweight steganalysis models: MobileNet, ShuffleNet, EfficientNet. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 8(6), 737-747. https://doi.org/10.29207/resti.v8i6.6091 [Google Scholar] [Crossref]

3. Chhabra, M., Sharan, B., Elbarachi, M., & Kumar, M. (2024). Intelligent Waste Classification Approach Based on Improved Multi-Layered Convolutional Neural Network. Multimedia Tools and Applications, 83, 84095–84120. https://doi.org/10.1007/s11042-024-18939-w [Google Scholar] [Crossref]

4. Ebikapade, A., & Baird, J. (2016). Understanding waste and waste management. Journal of Management and Sustainability, 6(4), 88. https://doi.org/10.5539/jms.v6n4p88 [Google Scholar] [Crossref]

5. Frost, A., Jean, G., & Ailyn, D. (2025). Lightweight CNNs for edge computing in resource-limited settings. Retrieved from https://www.researchgate.net/publication/395767938 [Google Scholar] [Crossref]

6. Hossain, M. A., Ferdous, N., & Ferdous, E. (2024). Crisis-driven Disruptions in Global Waste Management: Impacts, Challenges, and Policy Responses Amid COVID-19, Russia-Ukraine War, Climate Change, and Colossal Food Waste. Environmental Challenges, 14, 100807. Retrieved from https://doi.org/10.1016/j.envc.2023.100807. [Google Scholar] [Crossref]

7. Kartik, K., Ahmed, T., Ghosh, S., Gupta, R., & Tripathi, A. (2023). Transfer learning with CNNs on small datasets. In Proceedings of the International Conference on Machine Learning and Applications (pp. 1087-1099). CC BY 4.0 license. [Google Scholar] [Crossref]

8. Malik, M., Sharma, S., Uddin, M., Chen, C. L., Wu, C. M., Soni, P., & Chaudhary, S. (2022). Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models. Sustainability, 14(12), 7222. Retrieved from https://doi.org/10.3390/su14127222. [Google Scholar] [Crossref]

9. Mao, W., Chen, W., Wang, C., & Lin, Y. (2021). Optimized CNN for recycling waste classification. Resources, Conservation and Recycling, 164, 105132. https://doi.org/10.1016/j.resconrec.2020.105132 [Google Scholar] [Crossref]

10. Nahiduzzaman, M., Ahamed, M., Naznine, M., Karim, M., Kibria, H., Ayari, M., Khandakar, A., Ashraf, A., Ahsan, M., & Haider, J. (2025). Deep learning for automated waste classification. Knowledge-Based Systems, 310, 113028. https://doi.org/10.1016/j.knosys.2025.113028 [Google Scholar] [Crossref]

11. Nasir, I., & Al-Talib, G. (2023). AI techniques for waste classification: A literature review. Technium: Romanian Journal of Applied Sciences and Technology, 5, 49-59. https://doi.org/10.47577/technium.v5i.8345 [Google Scholar] [Crossref]

12. Patil, D., Rane, N., Desai, P., & Rane, J. (2024). Machine learning and deep learning: An overview. In Machine Learning and Deep Learning (pp. 11-30). ISBN: 978-81-981367-4-9. [Google Scholar] [Crossref]

13. Qu, D. (2022). AI in university waste classification management. In Advances in Artificial Intelligence and Applied Mathematics (pp. 287-296). Springer, Cham. https://doi.org/10.1007/978-3-030-97196-0_27 [Google Scholar] [Crossref]

14. Sallang, N. C. A., Islam, M. T., Islam, M. S., & Arshad, H. (2021). A CNN-based smart waste management system using TensorFlow lite and LoRa-GPS shield in Internet of Things environment. IEEE Access, 9, 153560-153574. Retrieved from https://doi.org/10.1109/ACCESS.2021.3055128. [Google Scholar] [Crossref]

15. Sayem, F. R., Islam, M. S. B., Naznine, M., Nashbat, M., Hasan-Zia, M., Kunju, A. K. A., ... & Chowdhury, M. E. (2024). Enhancing Waste Sorting and Recycling Efficiency: Robust Deep Learning-Based Approach for Classification and Detection. Neural Computing and Applications, 1–17. Retrieved from https://doi.org/10.1007/s00521-024-06994-8. [Google Scholar] [Crossref]

16. Shahab, S., Anjum, M., & Umar, M. (2022). Deep learning in solid waste management: A review. International Journal of Advanced Computer Science and Applications, 13. https://doi.org/10.14569/IJACSA.2022.0130347 [Google Scholar] [Crossref]

17. Sudha, S., Vidhyalakshmi, M., Pavithra, K., Sangeetha, K., & Swaathi, V. (2016). Deep learning for eco-friendly waste classification. In 2016 International Conference on Technological Innovations in Management, Engineering, and Sciences (TIARE) (pp. 65-70). IEEE. https://doi.org/10.1109/TIAR.2016.7801215 [Google Scholar] [Crossref]

18. Taglay, D., Rigor, J., Royo, R., & Fernandez, R. (2025). CNN-based image recognition for recyclables. International Journal of Latest Technology in Engineering Management & Applied Science, 14, 606-617. https://doi.org/10.51583/IJLTEMAS.2025.1409000073 [Google Scholar] [Crossref]

19. William, Lily & Ayodele, Harry & Blake, Harrison. (2024). Evaluating the Impact of Data Augmentation Techniques on Model Performance. https://www.researchgate.net/publication/389357090_Evaluating_the_Impact_of_Data_Augmentation_Techniques_on_Model_Performance/citation/download [Google Scholar] [Crossref]

20. Yang, Z., & Li, D. (2020). WasNet: A neural network-based garbage collection management system. IEEE Access, 8, 103984-103993. Retrieved from https://doi.org/10.1109/ACCESS.2020.3021510. [Google Scholar] [Crossref]

• Block-Based Programming for Education: A Comprehensive Analysis of Visual Programming Environments in K-12 Learning
• Management of Academic Libraries and Client Satisfaction Towards Digital Utilization: Basis for Monitoring Library Operations in SOCCSKSARGEN Region.
• Revenue Leakages in TPA Insurance Claims and Corporate Claims: An Institutional Overview of Aster Prime Hospital, Hyderabad
• Technology and Innovation in Hospitality and Tourism: A Management Perspective
• Geospatial Distribution of Tarok Sacred Grove of Langtang North and Langtang South Local Government Areas