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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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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