Back to articles
Articles
Volume: 32 | Article ID: art00002
Image
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.

Subject Areas :
Views 49
Downloads 6
 articleview.views 49
 articleview.downloads 6
  Cite this article 

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

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2020
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA