Abstract
This paper describes an intelligent system that classifies citrus brand considering physical features. The system consists of new original equipment to measure the citrus physical features and the convolutional neural network (CNN) to classify the citrus into any brand. When a needle probes citrus flesh, the load, and the sound are measured. From measured signals, the computer generates an image consisting of the sound spectrogram and the color bar expressing the load change. The image is inputted to CNN in order to infer the brand of citrus. In the experiment, CNN is validated.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kato, S., Ito, R., Shiozaki, T., Kitano, F., Wada, N., Kagawa, T., Nobuhara, H., Hino, T., Sato, Y.: Apple brand classification using CNN aiming at automatic apple texture estimation. Lect. Notes Netw. Syst. 96, 811–820 (2019)
LeCun, T., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Tabilo-Munizaga, G., Barbosa-Cánovas, G.V.: Rheology for the food industry. J. Food Eng. 67(1–2), 147–156 (2005)
Akimoto, H., Sakurai, N., Blahovec, J.: A swing arm device for the acoustic measurement of food texture. J. Texture Stud. 50(2), 104–113 (2019)
Kato, S., Wada, N.: Estimation system of food texture using neural network and fuzzy logic. Int. J. Space-Based Situated Comput. 8(2), 96–104 (2018)
Kato, S., Ito, R., Wada, N., Kagawa, T., Yamamoto, M.: Snack food texture estimation by neural network. In: Proceedings of 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent System, pp. 548–553 (2018)
Hayakawa, F., et al.: Classification of Japanese texture terms. J. Texture Stud. 44(2), 140–159 (2013)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS 2012), pp. 1097–1105 (2012)
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
MathWorks: Transfer Learning Using AlexNet. https://www.mathworks.com/help/deeplearning/examples/transfer-learning-using-alexnet.html. Accessed 20 Dec 2019
Sharma, G., Umapathy, K., Krishnan, S.: Trends in audio signal feature extraction methods. Appl. Acoust. 158(15), 1–21 (2020)
Badshah, A.M., Rahim, N., Ullah, N., Ahmad, J., Muhammad, K., Lee, M.Y., Kwon, S., Baik, S.W.: Deep features-based speech emotion recognition for smart affective services. Multimed. Tools Appl. 78(5), 5571–5589 (2019)
Xie, J., Zhu, M.: Handcrafted features and late fusion with deep learning for bird sound classification. Ecol. Inform. 52, 74–81 (2019)
Bardou, D., Zhang, K., Ahmad, S.M.: Lung sounds classification using convolutional neural networks. Artif. Intell. Med. 88, 58–69 (2018)
Chen, Y., Guo, Q., Liang, X., Wang, J., Qian, Y.: Environmental sound classification with dilated convolutions. Appl. Acoust. 148, 123–132 (2019)
Shen, Y., Cao, J., Wang, J., Yang, Z.: Urban acoustic classification based on deep feature transfer learning. J. Frankl. Inst. 357(1), 667–686 (2020)
Costa, Y.M.G., Oliveira, L.S., Silla Jr., C.N.: An evaluation of Convolutional Neural Networks for music classification using spectrograms. Appl. Soft Comput. 52, 28–38 (2017)
Priddy, K.L., Keller, P.E.: Artificial neural networks - an introduction, chap 11. In: Dealing with Limited Amounts of Data, Bellingham, WA, USA. SPIE Press, pp. 101–102 (2005)
Wong, T.-T.: Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 48(9), 2839–2846 (2015)
Nishimoto, S., Muranaka, A., Nishi, K., Kadota, A., Sugahara, T.: Immunomodulatory effects of citrus fruit auraptene in vitro and in vivo. J. Funct. Foods 4(4), 883–890 (2012)
Hara, Y., Shiraishi, A., Sakane, Y., Takezawa, Y., Kamao, T., Ohashi, Y., Yasunaga, S., Sugahara, T.: Effect of Mandarin Orange Yogurt on allergic conjunctivitis induced by conjunctival allergen challenge. Clin. Epidemiol. Res. 58(7), 2922–2929 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kato, S., Kagawa, T., Wada, N., Hino, T., Nobuhara, H. (2020). Citrus Brand Classification by CNN Considering Load and Sound. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_113
Download citation
DOI: https://doi.org/10.1007/978-3-030-44038-1_113
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44037-4
Online ISBN: 978-3-030-44038-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)