WASTE CLASSIFICATION USING TRANSFER LEARNING WITH MOBILENETV2 TO SUPPORT ENVIRONMENTAL MANAGEMENT
DOI:
https://doi.org/10.33480/jitk.v12i1.7757Keywords:
Deep Learning, MobileNetV2, Transfer Learning, Waste Classification, Image ClassificationAbstract
Efficient waste management requires an accurate automatic classification system for organic and recyclable categories. This study aims to develop a deep learning-based waste classification model using the MobileNetV2 architecture with a transfer learning approach. The main contribution of this study lies in the modification of the architecture through fine-tuning techniques and the integration of a 0.5 dropout layer specifically designed to address the problems of overfitting and class imbalance in waste image data. Test results show that the optimized MobileNetV2 model significantly improves classification performance, achieving an accuracy of 93%. This proposed model is proven to be more adaptive and substantially superior compared to standard architectures and comparable architectures such as ResNet.
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[1] A. Maalouf and P. Agamuthu, “Waste management evolution in the last five decades in developing countries – A review,” Waste Manag. Res., vol. 41, no. 9, pp. 1420–1434, 2023, doi: 10.1177/0734242X231160099.
[2] M. G. Kibria, N. I. Masuk, R. Safayet, H. Q. Nguyen, and M. Mourshed, Plastic Waste: Challenges and Opportunities to Mitigate Pollution and Effective Management, vol. 17, no. 1. Springer International Publishing, 2023. doi: 10.1007/s41742-023-00507-z.
[3] D. C. Wilson, “Learning from the past to plan for the future: An historical review of the evolution of waste and resource management 1970–2020 and reflections on priorities 2020–2030 – The perspective of an involved witness,” Waste Manag. Res., vol. 41, no. 12, pp. 1754–1813, 2023, doi: 10.1177/0734242X231178025.
[4] M. I. B. Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustain., vol. 15, no. 14, pp. 1–16, 2023, doi: 10.3390/su151411138.
[5] B. Fang et al., Artificial intelligence for waste management in smart cities: a review, vol. 21, no. 4. Springer International Publishing, 2023. doi: 10.1007/s10311-023-01604-3.
[6] F. S. Alsubaei, F. N. Al-Wesabi, and A. M. Hilal, “Deep Learning-Based Small Object Detection and Classification Model for Garbage Waste Management in Smart Cities and IoT Environment,” Appl. Sci., vol. 12, no. 5, 2022, doi: 10.3390/app12052281.
[7] J. C. Arbeláez-Estrada et al., "A Systematic Literature Review of Waste Identification in Automatic Separation Systems," Recycling, vol. 8, no. 6, Art. no. 86, pp. 1–25, Nov. 2023, doi: 10.3390/recycling8060086.
[8] R. Prochazka et al., “Collection of Plastic Packaging of Various Types: Sorting of Fractions of Plastic Waste Using Both Automated and Manual Modes,” IEEE Access, vol. 12, no. February, pp. 44244–44261, 2024, doi: 10.1109/ACCESS.2024.3376230.
[9] F. R. Sayem et al., “Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection,” Neural Comput. Appl., vol. 37, no. 6, pp. 4567–4583, 2024, doi: 10.1007/s00521-024-10855-2.
[10] S. Rianmora, P. Punsawat, C. Yutisayanuwat, and Y. Tongtan, “Design for an Intelligent Waste Classifying System: A Case Study of Plastic Bottles,” IEEE Access, vol. 11, no. March, pp. 47619–47645, 2023, doi: 10.1109/ACCESS.2023.3274862.
[11] M. M. M. Alghamdi, M. Y. H. Dahab, and N. H. A. Alazwary, “Enhancing deep learning techniques for the diagnosis of the novel coronavirus (COVID-19) using X-ray images,” Cogent Eng., vol. 10, no. 1, 2023, doi: 10.1080/23311916.2023.2181917.
[12] A. A. Akinyelu and P. Blignaut, “COVID-19 diagnosis using deep learning neural networks applied to CT images,” Front. Artif. Intell., vol. 5, 2022, doi: 10.3389/frai.2022.919672.
[13] M. Ragab et al., “COVID-19 Identification System Using Transfer Learning Technique With Mobile-NetV2 and Chest X-Ray Images,” Front. Public Heal., vol. 10, no. March, pp. 1–15, 2022, doi: 10.3389/fpubh.2022.819156.
[14] Y. H. Bhosale and K. S. Patnaik, “PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates,” Biomed. Signal Process. Control, vol. 81, no. April 2022, p. 104445, 2023, doi: 10.1016/j.bspc.2022.104445.
[15] D. R. Beddiar, M. Oussalah, U. Muhammad, and T. Seppänen, “A Deep learning based data augmentation method to improve COVID-19 detection from medical imaging,” Knowledge-Based Syst., vol. 280, no. August, 2023, doi: 10.1016/j.knosys.2023.110985.
[16] M. Neshat, M. Ahmed, H. Askari, M. Thilakaratne, and S. Mirjalili, “Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs,” Procedia Comput. Sci., vol. 235, pp. 1841–1850, 2024, doi: 10.1016/j.procs.2024.04.175.
[17] W. F. Akbar, N. C. Wibowo, S. Kom, M. Kom, and D. S. Yudha, “Implementasi Transfer Learning Model DenseNet169 Untuk Klasifikasi Citra Jenis Sampah,” J. Tek. Inform. dan Sist. Inf., vol. 11, no. 4, pp. 495–506, 2025.
[18] A. Bustamin et al., “Sistem Multi Klasifikasi Sampah Anorganik Dengan Menggunakan Transfer Learning Inorganic Waste Multi Classification System Using Transfer Learning,” vol. 8, no. 1, pp. 1–5, 2023.
[19] P. T. Huong, L. T. Hien, N. M. Son, H. C. Tuan, and T. Q. Nguyen, “Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques,” J. Algorithms Comput. Technol., vol. 19, 2025, doi: 10.1177/17483026241309070.
[20] J. Lee, S. Member, J. Park, Y. Lee, and S. Member, “Toward Efficient Cancer Detection on Mobile Devices,” IEEE Access, vol. 13, no. February, pp. 34613–34626, 2025, doi: 10.1109/ACCESS.2025.3543838.
[21] J. Sharma et al., “Deep learning based ensemble model for accurate tomato leaf disease classification by leveraging ResNet50 and MobileNetV2 architectures,” Sci. Rep., vol. 15, no. 1, 2025, doi: 10.1038/s41598-025-98015-x.
[22] A. Tripathi, T. Singh, R. R. Nair, and P. Duraisamy, “Improving Early Detection and Classification of Lung Diseases With Innovative MobileNetV2 Framework,” IEEE Access, vol. 12, no. June, pp. 116202–116217, 2024, doi: 10.1109/ACCESS.2024.3440577.
[23] Q. Zhu, H. Zhuang, M. Zhao, S. Xu, and R. Meng, “A study on expression recognition based on improved mobilenetV2 network,” Sci. Rep., vol. 14, no. 1, pp. 1–11, 2024, doi: 10.1038/s41598-024-58736-x.
[24] H. Tesfai et al., “Lightweight Shufflenet Based CNN for Arrhythmia Classification,” IEEE Access, vol. 10, no. October, pp. 111842–111854, 2022, doi: 10.1109/ACCESS.2022.3215665.
[25] I. Rodriguez-Conde, C. Campos, and F. Fdez-Riverola, Optimized convolutional neural network architectures for efficient on-device vision-based object detection, vol. 34, no. 13. Springer London, 2022. doi: 10.1007/s00521-021-06830-w.
[26] M. M. H. Shuvo, S. K. Islam, J. Cheng, and B. I. Morshed, “Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review,” Proc. IEEE, vol. 111, no. 1, pp. 42–91, 2023, doi: 10.1109/JPROC.2022.3226481.
[27] M. Z. Sajid et al., “Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture,” Diagnostics, vol. 13, no. 8, 2023, doi: 10.3390/diagnostics13081439.
[28] M. Z. Asghar et al., “Facial Mask Detection Using Depthwise Separable Convolutional Neural Network Model During COVID-19 Pandemic,” Front. Public Heal., vol. 10, no. March, pp. 1–14, 2022, doi: 10.3389/fpubh.2022.855254.
[29] Y. Lin, Y. Zhang, and X. Yang, “A Low Memory Requirement MobileNets Accelerator Based on FPGA for Auxiliary Medical Tasks,” Bioengineering, vol. 10, no. 1, pp. 1–15, 2023, doi: 10.3390/bioengineering10010028.
[30] M. Naseri, E. De Poorter, I. Moerman, H. V. Poor, and A. Shahid, “High-Throughput Blind Co-Channel Interference Cancellation for Edge Devices Using Depthwise Separable Convolutions, Quantization, and Pruning,” IEEE Open J. Commun. Soc., vol. 6, no. December 2024, 2024, doi: 10.1109/OJCOMS.2024.3523797.
[31] H. Chen, G. Zhou, W. He, X. Duan, and H. Jiang, “Classification and identification of agricultural products based on improved MobileNetV2,” Sci. Rep., vol. 14, no. 1, pp. 1–14, 2024, doi: 10.1038/s41598-024-53349-w.
[32] A. Elhanashi, P. Dini, S. Saponara, and Q. Zheng, “Advancements in TinyML: Applications, Limitations, and Impact on IoT Devices,” Electron., vol. 13, no. 17, 2024, doi: 10.3390/electronics13173562.
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Copyright (c) 2026 Muhammad Trihardyansyah, M Safii, Sundari Retno Andani
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