Transfer Learning with Xception Architecture for Snakefruit Quality Classification

Authors

  • Rismiyati Rismiyati Universitas Diponegoro
  • Ardytha Luthfiarta Universitas Dian Nuswantoro

DOI:

https://doi.org/10.33633/jais.v7i2.6797

Abstract

Machine learning has been greatly used in the field of image classification. Several machine learning techniques perform very well in this task. The development of machine learning technique in recent years are in the direction of deep learning. One of the main challenge of deep learning is that it requires the number of the samples to be extremely large for the model to perform well. This is because the number of feature that trainable parameter are huge. One of the solution to overcome this is by introducing transfer learning. One of the architecture that is currently introduced is Xception architecture. This architecture is claimed to outperform VGG16, ResNet50, and inception in terms of model accuracy and model size. This research aims to classify snakefruit quality by using transfer learning with Xception architecture. This is to explore possibility to achieve better result as Xception architecture generally perform better than other available architecture in transfer learning. The snakefruit quality is classified into two classes. Hyperparameter value is optimized by several scenario to determine the best model. The best performance is achieved by using learning rate of 0.0005, momentum 0.9 and dropout value of 0 or 0.25. The accuracy achieved is 94.44%.

References

P. U. Patil, S. B. Lande, V. J. Nagalkar, S. B. Nikam, and G. C. Wakchaure, “Grading and sorting technique of dragon fruits using machine learning algorithms,” J. Agric. Food Res., vol. 4, no. December 2020, p. 100118, 2021.

H. Azarmdel, A. Jahanbakhshi, S. S. Mohtasebi, and A. R. Muñoz, “Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM),” Postharvest Biol. Technol., vol. 166, no. December 2019, p. 111201, 2020.

Ian Goodfellow, Yoshua Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

A. Nasiri, A. Taheri-Garavand, and Y. D. Zhang, “Image-based deep learning automated sorting of date fruit,” Postharvest Biol. Technol., vol. 153, no. April, pp. 133–141, 2019.

A. Z. da Costa, H. E. H. Figueroa, and J. A. Fracarolli, “Computer vision based detection of external defects on tomatoes using deep learning,” Biosyst. Eng., vol. 190, pp. 131–144, 2020.

M. M. Raikar, S. M. Meena, C. Kuchanur, S. Girraddi, and P. Benagi, “Classification and Grading of Okra-ladies finger using Deep Learning,” Procedia Comput. Sci., vol. 171, no. 2019, pp. 2380–2389, 2020.

F. Zhuang et al., “A Comprehensive Survey on Transfer Learning,” Proc. IEEE, vol. 109, no. 1, pp. 43–76, 2021.

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-January, pp. 1800–1807, 2017.

S. K. Behera, A. K. Rath, and P. K. Sethy, “Maturity status classification of papaya fruits based on machine learning and transfer learning approach,” Inf. Process. Agric., vol. 8, no. 2, pp. 244–250, 2021.

N. Begum and M. K. Hazarika, “Maturity detection of tomatoes using transfer learning,” Meas. Food, vol. 7, no. December 2021, p. 100038, 2022.

R. Rismiyati and A. Luthfiarta, “VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification,” Telematika, vol. 18, no. 1, p. 37, 2021.

Rismiyati and H. A. Wibawa, “Snake Fruit Classification by Using Histogram of Oriented Gradient Feature and Extreme Learning Machine,” ICICOS 2019 - 3rd Int. Conf. Informatics Comput. Sci. Accel. Informatics Comput. Res. Smarter Soc. Era Ind. 4.0, Proc., pp. 2–6, 2019.

Rismiyati and S. N. Azhari, “Convolutional Neural Network implementation for image-based Salak sortation,” Proc. - 2016 2nd Int. Conf. Sci. Technol. ICST 2016, pp. 77–82, 2017.

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Published

2022-09-07