Abstract
This work proposes a novel approach based on deep learning to address the classification of shrimp (Pennaeus vannamei) into two classes, according to their level of pigmentation accepted by shrimp commerce. The main goal of this actual study is to support the shrimp industry in terms of price and process. An efficient CNN architecture is proposed to perform image classification through a program that could be set other in mobile devices or in fixed support in the shrimp supply chain. The proposed approach is a lightweight model that uses HSV color space shrimp images. A simple pipeline shows the most important stages performed to determine a pattern that identifies the class to which they belong based on their pigmentation. For the experiments, a database acquired with mobile devices of various brands and models has been used to capture images of shrimp. The results obtained with the images in the RGB and HSV color space allow for testing the effectiveness of the proposed model.
Keywords
- Pigmentation
- Color space
- Light weight network
This is a preview of subscription content, access via your institution.
Buying options
References
Carbajal, J., Sánchez, L.: Classification based on fuzzy inference systems for artificial habitat quality in shrimp farming. In: 2008 Seventh Mexican International Conference on Artificial Intelligence, pp. 388–392. IEEE (2008)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hu, W.C., Wu, H.T., Zhang, Y.F., Zhang, S.H., Lo, C.H.: Shrimp recognition using ShrimpNet based on convolutional neural network. J. Ambient Intell. Human. Comput. 1–8 (2020)
Liu, Z.: Soft-shell shrimp recognition based on an improved AlexNet for quality evaluations. J. Food Eng. 266, 109698 (2020)
Liu, Z., Jia, X., Xu, X.: Study of shrimp recognition methods using smart networks. Comput. Electron. Agric. 165, 104926 (2019)
Ma, P., et al.: Integrated portable shrimp-freshness prediction platform based on ice-templated metal-organic framework colorimetric combinatorics and deep convolutional neural networks. ACS Sustain. Chem. Eng. 9(50), 16926–16936 (2021)
Martinel, N., Foresti, G.L., Micheloni, C.: Wide-slice residual networks for food recognition. In: 2018 IEEE Winter conference on applications of computer vision (WACV), pp. 567–576. IEEE (2018)
Noor, A., Evi, J., Safitri, A.D., Mustari, M., Tiandho, Y., et al.: Melastoma malabathricum l. Extracts-based indicator for monitoring shrimp freshness integrated with classification technology using nearest neighbours algorithm. SINERGI 25(1), 69–74 (2021)
Qu, J.H., Cheng, J.H., Sun, D.W., Pu, H., Wang, Q.J., Ma, J.: Discrimination of shelled shrimp (metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique. LWT-Food Sci. Technol. 62(1), 202–209 (2015)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Yu, X., Tang, L., Wu, X., Lu, H.: Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm. Food Anal. Methods 11(3), 768–780 (2018)
Yu, X., Wang, J., Wen, S., Yang, J., Zhang, F.: A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in pacific white shrimp (Litopenaeus vannamei). Biosys. Eng. 178, 244–255 (2019)
Zhang, Y., Wei, C., Zhong, Y., Wang, H., Luo, H., Weng, Z.: Deep learning detection of shrimp freshness via smartphone pictures. J. Food Meas. Characterization 1–9 (2022)
Acknowledgements
This work has been partially supported by the ESPOL Polytechnic University; and the “CERCA Programme/Generalitat de Catalunya”. The authors gratefully acknowledge the NVIDIA Corporation for the donation of a Titan Xp GPU used for this research.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Suárez, P.L., Sappa, A., Carpio, D., Velesaca, H., Burgos, F., Urdiales, P. (2022). Deep Learning Based Shrimp Classification. In: , et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-20713-6_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20712-9
Online ISBN: 978-3-031-20713-6
eBook Packages: Computer ScienceComputer Science (R0)