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Volume: 32 | Article ID: art00002
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Fish Freshness Estimation though analysis of Multispectral Images with Convolutional Neural Networks
  DOI :  10.2352/ISSN.2470-1173.2020.12.FAIS-171  Published OnlineJanuary 2020
Abstract

Quantification of food quality is a critical process for ensuring public health. Fish correspond to a particularly challenging case due to its high perishable nature as food. Existing approaches require laboratory testing, a laborious and timeconsuming process. In this paper, we propose a novel approach for evaluating fish freshness by exploiting the information encoded in the spectral profile acquired by a snapshot spectral camera. To extract the relevant information, we employ state-ofthe- art Convolutional Neural Networks and treat the problem as an instance of multi-class classification, where each class corresponds to a two-day period since harvesting. Experimental evaluation on individuals from the Sparidae (Boops sp.) family demonstrates that the proposed approach constitutes a valid methodology, offering both accuracy as well as effortless application.

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G. Tsagkatakis, S. Nikolidakis, E. Petra, A. Kapantagakis, K. Grigorakis, G. Katselis, N. Vlahos, P. Tsakalides, "Fish Freshness Estimation though analysis of Multispectral Images with Convolutional Neural Networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Food and Agricultural Imaging Systems,  2020,  pp 171-1 - 171-5,  https://doi.org/10.2352/ISSN.2470-1173.2020.12.FAIS-171

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