Swin Transformer V2 for the classification of loja coffee
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Abstract
This study presents a binary classification model for green coffee beans of the Arabica variety from the Loja region in Ecuador, based on the Swin Transformer V2 architecture. Two datasets were used, the public USK-COFFEE dataset of Indonesian origin and a proprietary dataset captured under controlled conditions. Two training strategies were evaluated: sequential transfer learning and unified training, with the latter achieving a validation accuracy of 98.30%. After hyperparameter optimization, the model reached 100% accuracy on a test set of 150 images and 93% accuracy on an external generalization set of 400 images with varying lighting conditions and backgrounds. Model interpretability was validated using Grad-CAM, demonstrating that the network focuses on actual defective regions rather than background information. An ablation analysis revealed that performance degradation in unconstrained scenarios is mainly due to sensitivity to noise and extreme lighting conditions. The main contributions of this work include the creation of a specialized dataset for Arabica green coffee from Loja and the development of an efficient model for its automatic classification.
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References
[1] ICP. (2025) I-CIP retreats on news of looser supply, relieving some of the upward pressure. Coffee market report. International Coffee Organization. [Online]. Available: https://upsalesiana.ec/ing35ar10r1
[2] Agricultura. (2025) 6425 hectáreas de café son renovadas en la provincia de Loja. Ministerio de Agricultura, Ganadería y Pesca. [Online]. Available: https://upsalesiana.ec/ing35ar10r2
[3] M. Faisal, J.-S. Leu, and J. T. Darmawan, “Model selection of hybrid feature fusion for coffee leaf disease classification,” IEEE Access, vol. 11, pp. 62 281–62 291, 2023. [Online]. Available: https://doi.org/10.1109/ACCESS.2023.3286935
[4] E. Hassan, “Enhancing coffee bean classification: a comparative analysis of pretrained deep learning models,” Neural Computing and Applications, vol. 36, no. 16, pp. 9023–9052, Apr. 2024. [Online]. Available: https://doi.org/10.1007/s00521-024-09623-z
[5] C.-H. Hsia, Y.-H. Lee, and C.-F. Lai, “An explainable and lightweight deep convolutional neural network for quality detection of green coffee beans,” Applied Sciences, vol. 12, no. 21, p. 10966, Oct. 2022. [Online]. Available: https://doi.org/10.3390/app122110966
[6] S.-J. Chang and C.-Y. Huang, “Deep learning model for the inspection of coffee bean defects,” Applied Sciences, vol. 11, no. 17, p. 8226, Sep. 2021. [Online]. Available: https://doi.org/10.3390/app11178226
[7] A. Chavarro, D. Renza, and E. Moya-Albor, “Convnext as a basis for interpretability in coffee leaf rust classification,” Mathematics, vol. 12, no. 17, p. 2668, Aug. 2024. [Online]. Available: https://doi.org/10.3390/math12172668
[8] Y. A. Auliya, I. Fadah, Y. Baihaqi, and I. N. Awwaliyah, “Green bean classification: Fully convolutional neural network with Adam optimization,” Mathematical Modelling of Engineering Problems, vol. 11, no. 6, pp. 1641–1648, Jun. 2024. [Online]. Available: https://doi.org/10.18280/mmep.110626
[9] J. Maurício, I. Domingues, and J. Bernardino, “Comparing vision transformers and convolutional neural networks for image classification: A literature review,” Applied Sciences, vol. 13, no. 9, p. 5521, Apr. 2023. [Online]. Available: https://doi.org/10.3390/app13095521
[10] J. Wei, J. Chen, Y. Wang, H. Luo, and W. Li, “Improved deep learning image classification algorithm based on Swin Transformer V2,” PeerJ Computer Science, vol. 9, p. e1665, Oct. 2023. [Online]. Available: https://doi.org/10.7717/peerj-cs.1665
[11] S. Arwatchananukul, D. Xu, P. Charoenkwan, S. Aung Moon, and R. Saengrayap, “Implementing a deep learning model for defect classification in Thai Arabica green coffee beans,” Smart Agricultural Technology, vol. 9, p. 100680, Dec. 2024. [Online]. Available: https://doi.org/10.1016/j.atech.2024.100680
[12] W. Pinheiro Claro Gomes, L. Gonçalves, C. Barboza da Silva, and W. R. Melchert, “Application of multispectral imaging combined with machine learning models to discriminate special and traditional green coffee,” Computers and Electronics in Agriculture, vol. 198, p. 107097, Jul. 2022. [Online]. Available: https://doi.org/10.1016/j.compag.2022.107097
[13] M. N. Izza and G. P. Kusuma, “Image classification of Green Arabica Coffee using transformer-based architecture,” International Journal of Engineering Trends and Technology, vol. 72, no. 6, pp. 304–314, Jun. 2024. [Online]. Available: https://doi.org/10.14445/22315381/IJETT-V72I6P128
[14] H. F. Alhasson and S. S. Alharbi, “Classification of saudi coffee beans using a mobile application leveraging squeeze vision transformer technology,” Neural Computing and Applications, vol. 37, no. 14, pp. 8629–8649, Feb. 2025. [Online]. Available: https://doi.org/10.1007/s00521-025-11024-9
[15] Y. Jiao, Y. Zhao, A. Jia, T. Wang, J. Li, K. Xiang, H. Deng, M. He, R. Jiang, and Y. Zhang, “Swin-HSSAM: a green coffee bean grading method by swin transformer,” PLOS One, vol. 20, no. 5, p. e0322198, May 2025. [Online]. Available: https: //doi.org/10.1371/JOURNAL.PONE.0322198
[16] J. H. L. Goh, E. Ang, S. Srinivasan, X. Lei, J. Loh, T. C. Quek, C. Xue, X. Xu, Y. Liu, C.-Y. Cheng, J. C. Rajapakse, and Y.-C. Tham, “Comparative analysis of vision transformers and conventional convolutional neural networks in detecting referable diabetic retinopathy,” Ophthalmology Science, vol. 4, no. 6, p. 100552, Nov. 2024. [Online]. Available: https: //doi.org/10.1016/j.xops.2024.100552
[17] Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, F. Wei, and B. Guo, “Swin Transformer V2: scaling up capacity and resolution,” arXiv, 2021. [Online]. Available: https://doi.org/10.48550/arXiv.2111.09883
[18] S. Studer, T. B. Bui, C. Drescher, A. Hanuschkin, L. Winkler, S. Peters, and K.-R. Müller, “Towards CRISP-ML(Q): a machine learning process model with quality assurance methodology,” Machine Learning and Knowledge Extraction, vol. 3, no. 2, pp. 392–413, Apr. 2021. [Online]. Available: https://doi.org/10.3390/make3020020
[19] A. Febriana, K. Muchtar, R. Dawood, and C.-Y. Lin, “USK-Coffee dataset: A multi-class green arabica coffee bean dataset for deep learning,” in 2022 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom). IEEE, Jun. 2022, pp. 469–473. [Online]. Available: ttps://doi.org/10.1109/CyberneticsCom55287.2022.9865489
[20] Patricio Bolívar Betancourt Ludeña, “Lojano Arabica coffee,” Zenodo, 2025. [Online]. Available: https://doi.org/10.34740/kaggle/dsv/13947455
[21] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: visual explanations from deep networks via gradient-based localization,” International Journal of Computer Vision, vol. 128, no. 2, pp. 336–359, Oct. 2019. [Online]. Available: http://dx.doi.org/10.1007/s11263-019-01228-7
[22] H. L. Gope and H. Fukai, “Peaberry and normal coffee bean classification using CNN, SVM, and KNN: their implementation in and the limitations of Raspberry Pi 3,” AIMS Agriculture and Food, vol. 7, no. 1, pp. 149–167, 2022. [Online]. Available: https://doi.org/10.3934/agrfood.2022010