Abstract
In this paper, a deep learning based hyperspectral image analysis for detecting contaminated shrimp is proposed. The ability of distinguishing shrimps into two classes: clean and contaminated shrimps is visualized by t-distributed Stochastic Neighbor Embedding (t-SNE) using spectral feature data. Using only some small data set of hyperspectral images of shrimps, a simple processing technique is applied to generate enough data for training a deep neural network (DNN) with high reliability. Our results attain the accuracy of 98% and F1-score over 94%. This works confirms that with only few data samples, Hyperspectral Imaging processing technique together with DNN can be used to classify abnormality in agricultural productions like shrimp.
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References
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)
Schelkanova, I., Pandya, A., Muhaseen, A., Saiko, G., Douplik, A.: 13 - early optical diagnosis of pressure ulcers. In: Igor, M. (ed.) Biophotonics for Medical Applications, pp. 347–375. Woodhead Publishing (2015)
Vasefi, F., MacKinnon, N., Farkas, D.L.: Chapter 16 - hyperspectral and multispectral imaging in dermatology. In: Hamblin, M.R., Avci, P., Gupta, G.K. (eds.) Imaging in Dermatology, pp. 187–201. Academic Press, Boston (2016)
Yu, X., Tang, L., Wu, X., Lu, H.: Nondestructive freshness discriminating of shrimp using visible/near-infrared hyperspectral imaging technique and deep learning algorithm. J. Food Anal. Methods 11, 768–780 (2018)
Al-Sarayreh, M., Reis, M., Yan, W., Klette, R.: Detection of red-meat adulteration by deep spectral-spatial features in hyperspectral images. J. Imaging 4, 63 (2018)
Li, X., Li, R., Wang, M., Liu, Y., Zhang, B., Zhou, J.C.: Hyperspectral imaging and their applications in the nondestructive quality assessment of fruits and vegetables (2017). https://doi.org/10.5772/intechopen.72250
Specim: Specim FX10 - user guide 1.0. Specim imaging Oy Ltd
Li, Y., Zhang, H., Xue, X., Jiang, Y., Shen, Q.: Deep learning for remote sensing image classification: a survey. In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. e1264 (May 2018). https://doi.org/10.1002/widm.1264
Wang, W., et al.: Medical image classification using deep learning. In: Chen, Y.-W., Jain, L.C. (eds.) Deep Learning in Healthcare. ISRL, vol. 171, pp. 33–51. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-32606-7_3
Lu, Y.: Food image recognition by using convolutional neural networks (CNNs) (December 2016)
Thanasarn, N., Chaiprapat, S., Waiyakan, K., Thongkaew, K.: Automated discrimination of deveined shrimps based on grayscale image parameters. J. Food Process Eng. 42, e13041 (2019). https://doi.org/10.1111/jfpe.13041
Sural, S., Qian, G., Pramanik, S.: Segmentation and histogram generation using the HSV color space for image retrieval. In: Proceedings of International Conference on Image Processing, vol. 2, pp. II-589 (February 2002). https://doi.org/10.1109/ICIP.2002.1040019
Russell, B., Torralba, A., Murphy, K., Freeman, W.: LabelMe: a database and web-based tool for image annotation. Int. J. Comput. Vis. 77, 157–173 (2008). https://doi.org/10.1007/s11263-007-0090-8
Hahnloser, R., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947–951 (2000)
Han, J., Moraga, C.: The influence of the sigmoid function parameters on the speed of backpropagation learning. In: Mira, J., Sandoval, F. (eds.) IWANN 1995. LNCS, vol. 930, pp. 195–201. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-59497-3_175
Kingma, P., Lei Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980v9 (2014)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
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We thank Minh Phu seafood corporation for providing hyperspectral imaging data and inspiring us to realize this work.
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Nguyen, MH., Nguyen-Thi, XH., Pham, CN., Lê, N.C., Han, HD. (2020). Deep Learning Based Hyperspectral Images Analysis for Shrimp Contaminated Detection. In: Vo, NS., Hoang, VP. (eds) Industrial Networks and Intelligent Systems. INISCOM 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-030-63083-6_15
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DOI: https://doi.org/10.1007/978-3-030-63083-6_15
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