A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction

Hadi Santoso - Universitas Mercu Buana , Jakarta, 11650, Indonesia
Ilham Hanif - Universitas Mercu Buana , Jakarta, 11650, Indonesia
Hilyah Magdalena - ISB Atma Luhur, Pangkalpinang, 33127, Indonesia
Afiyati Afiyati - Universitas Mercu Buana , Jakarta, 11650, Indonesia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.2.1943

Abstract


The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.

Keywords


waste classification; feature extraction; EfficientNet; transfer learning; PCA; dimensionality reduction

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References


Z. Feng, J. Yang, L. Chen, Z. Chen, and L. Li, ‘An Intelligent Waste-Sorting and Recycling Device Based on Improved EfficientNet,’ Int J Environ Res Public Health, vol. 19, no. 23, 2022, doi: 10.3390/ijerph192315987.

Indonesian Government, Law of the Republic of Indonesia no. 18 of 2008 Waste Management. Indonesia, 2008.

C. M. Annur, ‘Indonesia Generates 19 Million Tons of Waste in 2022, the Majority of which is Food Waste’. Accessed: Jun. 22, 2023. [Online]. Available: https://databoks.katadata.co.id/datapublish/2023/03/09/ri-hasilkan-19-juta-ton-timbulan-sampah-pada-2022-mayoritas-sisa-makanan

A. M. Kumar, M. T. Aafrid, and N. A. Kumar, ‘Garbage Waste Classification Using Supervised Deep Learning Techniques’, 2020. [Online]. Available: https://ssrn.com/abstract=3563564

N. Ferronato and V. Torretta, ‘Waste mismanagement in developing countries: A review of global issues’, International Journal of Environmental Research and Public Health, vol. 16, no. 6. MDPI AG, Mar. 02, 2019. doi: 10.3390/ijerph16061060.

Y. Chen, W. Han, J. Jin, H. Wang, Q. Xing, and Y. Zhang, ‘Clean Our City: An Automatic Urban Garbage Classification Algorithm Using Computer Vision and Transfer Learning Technologies’, in Journal of Physics: Conference Series, IOP Publishing Ltd, Aug. 2021. doi: 10.1088/1742-6596/1994/1/012022.

D. Surender Dhiman, K. Srivatsan, and A. Jain, ‘Waste Classification using Transfer Learning with Convolutional Neural Networks’, IOP Conf Ser Earth Environ Sci, vol. 775, no. 1, 2021, doi: 10.1088/1755-1315/775/1/012010.

L. Yong, L. Ma, D. Sun, and L. Du, ‘Application of MobileNetV2 to waste classification’, PLoS One, vol. 18, no. 3, p. e0282336, Mar. 2023, doi: 10.1371/journal.pone.0282336.

M. Diqi and U. R. Yogyakarta, ‘Waste Classification using CNN Algorithm’, pp. 4–9, 2022.

D. Mohan Rai and S. Gupta, ‘Waste Classification using Deep Learning CNN’, International Journal of Advances in Engineering and Management (IJAEM, vol. 2, no. 11, p. 185, 2020, doi: 10.35629/5252-0211185188.

M. H. Zayd, M. W. Oktavian, D. G. T. Meranggi, J. A. Figo, and N. Yudistira, ‘Improvement of garbage classification using pretrained Convolutional Neural Network’, Teknologi, vol. 12, no. 1, pp. 1–8, 2022, doi: 10.26594/teknologi.v12i1.2403.

A. Masand, S. Chauhan, M. Jangid, R. Kumar, and S. Roy, ‘ScrapNet: An Efficient Approach to Trash Classification’, IEEE Access, vol. 9, pp. 130947–130958, 2021, doi: 10.1109/ACCESS.2021.3111230.

Y. Wang, W. J. Zhao, J. Xu, and R. Hong, ‘Recyclable Waste Identification Using CNN Image Recognition and Gaussian Clustering’, vol. 4, 2020, [Online]. Available: http://arxiv.org/abs/2011.01353

M. Chhabra, B. Sharan, K. Gupta, and R. Astya, ‘Waste Classification Using Improved CNN Architecture’, no. Aece, pp. 354–360, 2022.

H. Shanmugasundaram and R. Prasath, ‘Image Classification using Convolutional Neural Networks’, no. January, 2022.

Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu, ‘Waste image classification based on transfer learning and convolutional neural network’, Waste Management, vol. 135, no. May, pp. 150–157, 2021, doi: 10.1016/j.wasman.2021.08.038.

B. Petrovska, E. Zdravevski, P. Lameski, R. Corizzo, I. Štajduhar, and J. Lerga, ‘Deep Learning for Feature Extraction in Remote Sensing: A Case-Study of Aerial Scene Classification’, Sensors, vol. 20, no. 14, p. 3906, Jul. 2020, doi: 10.3390/s20143906.

S. Majchrowska et al., ‘Deep learning-based waste detection in natural and urban environments’, Waste Management, vol. 138, pp. 274–284, Feb. 2022, doi: 10.1016/j.wasman.2021.12.001.

W. Mulim, M. F. Revikasha, Rivandi, and N. Hanafiah, ‘Waste Classification Using EfficientNet-B0’, in 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI), IEEE, Oct. 2021, pp. 253–257. doi: 10.1109/ICCSAI53272.2021.9609756.

Ü. Atila, M. Uçar, K. Akyol, and E. Uçar, ‘Plant leaf disease classification using EfficientNet deep learning model’, Ecol Inform, vol. 61, p. 101182, 2021, doi: 10.1016/j.ecoinf.2020.101182.

M. Malik et al., ‘Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models’, Sustainability (Switzerland), vol. 14, no. 12, Jun. 2022, doi: 10.3390/su14127222.

T. Handhayani and J. Hendryli, ‘Leboh: An Android Mobile Application for Waste Classification Using TensorFlow Lite’, 2023, pp. 53–67. doi: 10.1007/978-3-031-16075-2_4.

X. Xu, X. Qi, and X. Diao, ‘Reach On Waste Classification and Identification by Transfer Learning and Lightweight Neural Network’, 2020. [Online]. Available: www.preprints.org

J. Ma and Y. Yuan, ‘Dimension reduction of image deep feature using PCA’, J Vis Commun Image Represent, vol. 63, 2019, doi: 10.1016/j.jvcir.2019.102578.

R. Sheikh, M. Patel, and A. Sinhal, ‘Recognizing MNIST Handwritten Data Set Using PCA and LDA’, no. October, pp. 169–177, 2020, doi: 10.1007/978-981-15-1059-5_20.

D. Otero Gómez, M. Toro Bermúdez, and W. H. Morales, ‘Solid Domestic Waste classification using Image Processing and Machine Learning’, 2021.

A. P. Puspaningrum et al., ‘Waste Classification Using Support Vector Machine with SIFT-PCA Feature Extraction’, in ICICoS 2020 - Proceeding: 4th International Conference on Informatics and Computational Sciences, Institute of Electrical and Electronics Engineers Inc., Nov. 2020. doi: 10.1109/ICICoS51170.2020.9298982.

C. Shi, C. Tan, T. Wang, and L. Wang, ‘A waste classification method based on a multilayer hybrid convolution neural network’, Applied Sciences (Switzerland), vol. 11, no. 18, Sep. 2021, doi: 10.3390/app11188572.

W. Xuemei, ‘Garbage classification method based on deep learning’, in Proceedings of the 6th EAI International Conference on IoT in Urban Space, Urb-IoT 2021, 20-21 December 2021, Shenzhen, People’s Republic of China, EAI, 2022. doi: 10.4108/eai.20-12-2021.2315017.

K. Khadijah, S. N. Endah, R. Kusumaningrum, R. Rismiyati, P. S. Sasongko, and I. Z. Nisa, ‘Solid waste classification using pyramid scene parsing network segmentation and combined features’, TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 19, no. 6, p. 1902, Dec. 2021, doi: 10.12928/telkomnika.v19i6.18402.

S. Jin, Z. Yang, G. Królczykg, X. Liu, P. Gardoni, and Z. Li, ‘Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling’, Waste Management, vol. 162, pp. 123–130, May 2023, doi: 10.1016/j.wasman.2023.02.014.

J. A. P, S. M, M. C, and K. S, ‘Application of Deep Learning for Solid Waste Trash Classification using Deep CNN’. [Online]. Available: www.kaggle.com/dimonisochecolo/trash-dataset

H. Gupta, ‘Trash Image Classification System using Machine Learning and Deep Learning Algorithms’, 2020.

J. P. T. Yusiong, ‘AN ENSEMBLE OF CNN-ELM MODELS FOR TRASH CLASSIFICATION’, ICIC Express Letters, vol. 16, no. 9, pp. 943–950, Sep. 2022, doi: 10.24507/icicel.16.09.943.