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Automatic recognition of coffee bean varieties based on pre-trained architectures

Year 2024, Volume: 66 Issue: 2, 162 - 175
https://doi.org/10.33769/aupse.1411294

Abstract

Coffee is an agricultural commodity of fundamental and considerable economic importance on the global market. In this study, the coffee bean varieties were examined from images via artificial intelligence due to their quality and value on the market. This study aims to create an automated system that can efficiently identify coffee beans without requiring a significant amount of time. In this study, five pre-trained Convolutional Neural Network (CNN) architectures were performed to detect four varieties of coffee beans through images. Extracting features from images is a challenging and specialized task. However, CNN possesses the ability to extract features automatically. Therefore, these architectures were employed as both deep feature extractors and classifiers. Primarily, 1600 coffee beans' images were split into 75:25 training and testing sets. Next, 5-fold cross-validation was applied during the training process. This study presented both validation and testing results. Eventually, ShuffleNet achieved the best classification performance with 99.33% and 99.75% accuracy rates in identifying types of coffee beans for the training and testing sets, respectively. As a result, this study has demonstrated that deep learning technologies can automatically recognize the different types of coffee beans.

References

  • De Oliveira, E. M., Leme, D. S., Barbosa, B. H. G., Rodarte, M. P., Pereira, R. G. F. A., A computer vision system for coffee beans classification based on computational intelligence techniques, J. Food Eng., 171 (2016), 22-27.
  • Gope, H. L., Fukai, H., Normal and pea berry coffee beans classification from green coffee bean images using convolutional neural networks and support vector machine, Int. J. Comput. Inf. Eng., 14 (6) (2020), 189-196.
  • Adiwijaya, N. O., Romadhon, H. I., Putra, J. A., Kuswanto, D. P., The quality of coffee bean classification system based on color by using k-nearest neighbor method, J. Phys.: Conference Series, 2157 (2022).
  • Vogt, M. A. B., Developing stronger association between market value of coffee and functional biodiversity, J. Environ. Manage., 269 (2020).
  • Buhrmester, V., Münch, D., Arens, M., Analysis of explainers of black box deep neural networks for computer vision: A survey, Mach. Learn. Knowl. Extr., 3 (4) (2021), 966-989.
  • Unal, Y., Taspinar, Y. S., Cinar, I., Kursun, R., Koklu, M., Application of pre-trained deep convolutional neural networks for coffee beans species detection, J. Food Anal. Method, 15 (12) (2022), 3232-3243.
  • Jumarlis, M., Mirfan, M., Manga, A. R., Classification of coffee bean defects using graylevel co-occurrence matrix and k-nearest neighbor, ILKOM J. Ilmiah, 14 (1) (2022), 1-9.
  • Arboleda, E. R., Comparing performances of data mining algorithms for classification of green coffee beans, J Int. J. Eng. Adv. Technol, 8 (5) (2019), 1563-1567.
  • Fukai, H., Furukawa, J., Katsuragawa, H., Pinto, C., Afonso, C., Classification of green coffee beans by convolutional neural network and its implementation on raspberry Pi and Camera Module, Timor. Acad. J. Sci., 1 (2018), 1-10.
  • Huang, N. F., Chou, D. L., Lee, C. A., Wu, F. P., Chuang, A. C., Chen, Y. H., Tsai, Y. C., Smart agriculture: real‐time classification of green coffee beans by using a convolutional neural network, JIET Smart Cities, 2 (4) (2020), 167-172.
  • Santos, F. F. L. d., Rosas, J. T. F., Martins, R. N., Araújo, G. d. M., Viana, L. d. A., Gonçalves, J. d. P., Quality assessment of coffee beans through computer vision and machine learning algorithms, Coff. Sci., (2020).
  • Tsai, J.-J., Chang, C.-C., Huang, D.-Y., Lin, T.-S., Chen, Y.-C., Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy, Food Chem., (2023), 426, https://doi.org/10.1016/j.foodchem. 2023.136610.
  • Arboleda, E., Classification of immature and mature coffee beans using texture Features and medium K nearest neighbor, J. Artif. Intell. Technol, 3 (3) (2022), 114-118, https://doi.org/10.37965/jait.2023.0203.
  • Raveena, S., Surendran, R., ResNet50-based classification of coffee cherry maturity using deep-CNN, 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, (2023), 1275-1281, https://doi.org/10.1109/ICSSIT55814.2023.10061006.
  • Kim, Y., Lee, J., Kim, S., Study of active food processing technology using computer vision and AI in coffee roasting, Food Sci. Biotechnol., (2024), 1-8, https://doi.org/10.1007/s10068-023-01507-7.
  • Chang, S.-J., Liu, K.-H., Multiscale defect extraction neural network for green coffee bean defects detection, IEEE Access, 12 (2024), 15856-15866, https://doi.org/10.1109/ACCESS.2024.3356596.
  • Ontoum, S., Khemanantakul, T., Sroison, P., Triyason, T., Watanapa, B., Coffee roast intelligence, arXiv: 2206.01841, (2022).
  • Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M. S., Deep learning for visual understanding: A review, Neurocomputing, 187 (2016), 27-48.
  • Ozaltin, O., Yeniay, O., A novel proposed CNN–SVM architecture for ECG scalograms classification, Soft Comput., 27 (8) (2023), 4639-4658.
  • Krizhevsky, A., Sutskever, I., Hinton, G. E., Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25 (2012).
  • Zaidi, S. S. A., Ansari, M. S., Aslam, A., Kanwal, N., Asghar, M., Lee, B., A survey of modern deep learning-based object detection models, Dig. Signal Process., 126, (2022).
  • Zhang, X., Zhou, X., Lin, M., Sun, J. Shufflenet: An extremely efficient convolutional neural network for mobile devices, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018).
  • Mathworks, 2023, https://www.mathworks.com/help/deeplearning/ug/pretrainedconvolutional-neural-networks.html.
  • Yadav, S., Shukla, S., Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification, 2016 IEEE 6th International Conference on Advanced Computing (IACC), (2016).
  • Gorunescu, F., Gorunescu, F., Classification performance evaluation, Data Mining: Concepts, Models Techniques, (2011), 319-330.
Year 2024, Volume: 66 Issue: 2, 162 - 175
https://doi.org/10.33769/aupse.1411294

Abstract

References

  • De Oliveira, E. M., Leme, D. S., Barbosa, B. H. G., Rodarte, M. P., Pereira, R. G. F. A., A computer vision system for coffee beans classification based on computational intelligence techniques, J. Food Eng., 171 (2016), 22-27.
  • Gope, H. L., Fukai, H., Normal and pea berry coffee beans classification from green coffee bean images using convolutional neural networks and support vector machine, Int. J. Comput. Inf. Eng., 14 (6) (2020), 189-196.
  • Adiwijaya, N. O., Romadhon, H. I., Putra, J. A., Kuswanto, D. P., The quality of coffee bean classification system based on color by using k-nearest neighbor method, J. Phys.: Conference Series, 2157 (2022).
  • Vogt, M. A. B., Developing stronger association between market value of coffee and functional biodiversity, J. Environ. Manage., 269 (2020).
  • Buhrmester, V., Münch, D., Arens, M., Analysis of explainers of black box deep neural networks for computer vision: A survey, Mach. Learn. Knowl. Extr., 3 (4) (2021), 966-989.
  • Unal, Y., Taspinar, Y. S., Cinar, I., Kursun, R., Koklu, M., Application of pre-trained deep convolutional neural networks for coffee beans species detection, J. Food Anal. Method, 15 (12) (2022), 3232-3243.
  • Jumarlis, M., Mirfan, M., Manga, A. R., Classification of coffee bean defects using graylevel co-occurrence matrix and k-nearest neighbor, ILKOM J. Ilmiah, 14 (1) (2022), 1-9.
  • Arboleda, E. R., Comparing performances of data mining algorithms for classification of green coffee beans, J Int. J. Eng. Adv. Technol, 8 (5) (2019), 1563-1567.
  • Fukai, H., Furukawa, J., Katsuragawa, H., Pinto, C., Afonso, C., Classification of green coffee beans by convolutional neural network and its implementation on raspberry Pi and Camera Module, Timor. Acad. J. Sci., 1 (2018), 1-10.
  • Huang, N. F., Chou, D. L., Lee, C. A., Wu, F. P., Chuang, A. C., Chen, Y. H., Tsai, Y. C., Smart agriculture: real‐time classification of green coffee beans by using a convolutional neural network, JIET Smart Cities, 2 (4) (2020), 167-172.
  • Santos, F. F. L. d., Rosas, J. T. F., Martins, R. N., Araújo, G. d. M., Viana, L. d. A., Gonçalves, J. d. P., Quality assessment of coffee beans through computer vision and machine learning algorithms, Coff. Sci., (2020).
  • Tsai, J.-J., Chang, C.-C., Huang, D.-Y., Lin, T.-S., Chen, Y.-C., Analysis and classification of coffee beans using single coffee bean mass spectrometry with machine learning strategy, Food Chem., (2023), 426, https://doi.org/10.1016/j.foodchem. 2023.136610.
  • Arboleda, E., Classification of immature and mature coffee beans using texture Features and medium K nearest neighbor, J. Artif. Intell. Technol, 3 (3) (2022), 114-118, https://doi.org/10.37965/jait.2023.0203.
  • Raveena, S., Surendran, R., ResNet50-based classification of coffee cherry maturity using deep-CNN, 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, (2023), 1275-1281, https://doi.org/10.1109/ICSSIT55814.2023.10061006.
  • Kim, Y., Lee, J., Kim, S., Study of active food processing technology using computer vision and AI in coffee roasting, Food Sci. Biotechnol., (2024), 1-8, https://doi.org/10.1007/s10068-023-01507-7.
  • Chang, S.-J., Liu, K.-H., Multiscale defect extraction neural network for green coffee bean defects detection, IEEE Access, 12 (2024), 15856-15866, https://doi.org/10.1109/ACCESS.2024.3356596.
  • Ontoum, S., Khemanantakul, T., Sroison, P., Triyason, T., Watanapa, B., Coffee roast intelligence, arXiv: 2206.01841, (2022).
  • Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., Lew, M. S., Deep learning for visual understanding: A review, Neurocomputing, 187 (2016), 27-48.
  • Ozaltin, O., Yeniay, O., A novel proposed CNN–SVM architecture for ECG scalograms classification, Soft Comput., 27 (8) (2023), 4639-4658.
  • Krizhevsky, A., Sutskever, I., Hinton, G. E., Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst., 25 (2012).
  • Zaidi, S. S. A., Ansari, M. S., Aslam, A., Kanwal, N., Asghar, M., Lee, B., A survey of modern deep learning-based object detection models, Dig. Signal Process., 126, (2022).
  • Zhang, X., Zhou, X., Lin, M., Sun, J. Shufflenet: An extremely efficient convolutional neural network for mobile devices, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2018).
  • Mathworks, 2023, https://www.mathworks.com/help/deeplearning/ug/pretrainedconvolutional-neural-networks.html.
  • Yadav, S., Shukla, S., Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification, 2016 IEEE 6th International Conference on Advanced Computing (IACC), (2016).
  • Gorunescu, F., Gorunescu, F., Classification performance evaluation, Data Mining: Concepts, Models Techniques, (2011), 319-330.
There are 25 citations in total.

Details

Primary Language English
Subjects Information Systems (Other)
Journal Section Research Articles
Authors

Aynur Yonar 0000-0003-1681-9398

Öznur Özaltın 0000-0001-9841-1702

Publication Date
Submission Date December 28, 2023
Acceptance Date April 3, 2024
Published in Issue Year 2024 Volume: 66 Issue: 2

Cite

APA Yonar, A., & Özaltın, Ö. (n.d.). Automatic recognition of coffee bean varieties based on pre-trained architectures. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, 66(2), 162-175. https://doi.org/10.33769/aupse.1411294
AMA Yonar A, Özaltın Ö. Automatic recognition of coffee bean varieties based on pre-trained architectures. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 66(2):162-175. doi:10.33769/aupse.1411294
Chicago Yonar, Aynur, and Öznur Özaltın. “Automatic Recognition of Coffee Bean Varieties Based on Pre-Trained Architectures”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 66, no. 2 n.d.: 162-75. https://doi.org/10.33769/aupse.1411294.
EndNote Yonar A, Özaltın Ö Automatic recognition of coffee bean varieties based on pre-trained architectures. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 66 2 162–175.
IEEE A. Yonar and Ö. Özaltın, “Automatic recognition of coffee bean varieties based on pre-trained architectures”, Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng., vol. 66, no. 2, pp. 162–175, doi: 10.33769/aupse.1411294.
ISNAD Yonar, Aynur - Özaltın, Öznur. “Automatic Recognition of Coffee Bean Varieties Based on Pre-Trained Architectures”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 66/2 (n.d.), 162-175. https://doi.org/10.33769/aupse.1411294.
JAMA Yonar A, Özaltın Ö. Automatic recognition of coffee bean varieties based on pre-trained architectures. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng.;66:162–175.
MLA Yonar, Aynur and Öznur Özaltın. “Automatic Recognition of Coffee Bean Varieties Based on Pre-Trained Architectures”. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, vol. 66, no. 2, pp. 162-75, doi:10.33769/aupse.1411294.
Vancouver Yonar A, Özaltın Ö. Automatic recognition of coffee bean varieties based on pre-trained architectures. Commun.Fac.Sci.Univ.Ank.Series A2-A3: Phys.Sci. and Eng. 66(2):162-75.

Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering

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