APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR THE CLASSIFICATION OF ORGANIC AND INORGANIC WASTE IMAGES
Keywords:
Convolutional Neural Network; Waste Classification; Deep Learning; TensorFlowAbstract
Waste management has become one of the most pressing environmental challenges due to the increasing volume of waste and the limited public awareness of proper waste segregation. The inability to distinguish between organic and inorganic waste often reduces the effectiveness of recycling and sustainable waste management practices. This study aims to develop an automatic image classification system based on the Convolutional Neural Network (CNN) method to identify organic and inorganic waste from digital images. The dataset consists of 25,077 images collected from publicly available sources and self-collected data, which were divided into 90% training data and 10% testing data. Before model training, the images underwent preprocessing techniques, including resizing, normalization, and data augmentation, to improve model performance and generalization. The CNN model was developed using the TensorFlow framework and achieved a training accuracy of 93.08% at the ninth epoch. Furthermore, the trained model was integrated into a Flask-based web application, enabling users to upload waste images and instantly receive classification results. The findings demonstrate that the proposed CNN-based system effectively classifies organic and inorganic waste images and has the potential to support intelligent waste management systems by improving the efficiency and accuracy of waste sorting. This research also highlights the applicability of deep learning techniques in addressing environmental issues through automated image recognition technology.
Downloads
References
Fauzan, A. D., Al Hafizh, M. R., Rahayu, W. I., & Riza, N. (2024). Waste classification dashboard using TensorFlow and Convolutional Neural Network (CNN) for sustainable waste management. Journal of Dinda: Data Science, Information Technology, and Data Analytics, 6(1), 1–8. https://doi.org/10.20895/dinda.v6i1.9908
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/CVPR.2016.90
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint arXiv:1704.04861. https://arxiv.org/abs/1704.04861
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lilhore, U. K., Simaiya, S., Sharma, S. K., Ghosh, U., & Mahmoud, M. (2024). A smart waste classification model using hybrid CNN-LSTM with transfer learning for sustainable environments. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-16677-z
Oktayaessofa, E., Sari, C. A., Rachmawanto, E. H., & Yaacob, N. M. (2024). Classification of organic and non-organic waste with CNN-MobileNetV2. Jurnal Teknik Informatika (JUTIF), 5(4), 899–908. https://doi.org/10.52436/1.jutif.2024.5.4.2165
Rayhan, Y., & Rifai, A. P. (2024). Multi-class waste classification using convolutional neural networks. Applied Environmental Research, 46(2), 1–14. https://doi.org/10.35762/AER.2024021
Shreya, M., Yughan, V. N., Katyal, J., & Ramesh, R. (2023). Technical solutions for waste classification and management: A mini review. Waste Management & Research, 41(3), 356–370. https://doi.org/10.1177/0734242X221135262
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations (ICLR). https://arxiv.org/abs/1409.1556
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning (ICML), 6105–6114.
Wu, T. W., Zhang, H., Peng, W., Lü, F., & He, P. J. (2023). Applications of convolutional neural networks for intelligent waste identification and recycling: A review. Resources, Conservation and Recycling, 190, 106813. https://doi.org/10.1016/j.resconrec.2022.106813
Zhang, X., Wang, Y., Liu, H., & Li, J. (2024). Intelligent waste classification based on improved convolutional neural networks. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-18939-w
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
