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Entropy 2014, 16(11), 6133-6151; doi:10.3390/e16116133

Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture

1
Research Center for Experimental Marine Biology and Biotechnology—Plentziako Itsas Estazioa (PIE), University of the Basque Country UPV/EHU, Plentzia 48620, Spain
2
Department of Systems Engineering and Automatics, University of the Basque Country UPV/EHU, Donostia 20018 Spain
3
IKERBASQUE Basque Foundation for Science, Bilbao 48013, Spain
4
Norwegian College of Fishery Science, Faculty of Biosciences, Fisheries and Economics, University of Tromsø, Tromsø N-9037, Norway
*
Author to whom correspondence should be addressed.
Received: 6 October 2014 / Revised: 13 November 2014 / Accepted: 17 November 2014 / Published: 19 November 2014
(This article belongs to the Special Issue Entropy in Bioinspired Intelligence)
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Abstract

The objective of the work was to develop a non-invasive methodology for image acquisition, processing and nonlinear trajectory analysis of the collective fish response to a stochastic event. Object detection and motion estimation were performed by an optical flow algorithm in order to detect moving fish and simultaneously eliminate background, noise and artifacts. The Entropy and the Fractal Dimension (FD) of the trajectory followed by the centroids of the groups of fish were calculated using Shannon and permutation Entropy and the Katz, Higuchi and Katz-Castiglioni’s FD algorithms respectively. The methodology was tested on three case groups of European sea bass (Dicentrarchus labrax), two of which were similar (C1 control and C2 tagged fish) and very different from the third (C3, tagged fish submerged in methylmercury contaminated water). The results indicate that Shannon entropy and Katz-Castiglioni were the most sensitive algorithms and proved to be promising tools for the non-invasive identification and quantification of differences in fish responses. In conclusion, we believe that this methodology has the potential to be embedded in online/real time architecture for contaminant monitoring programs in the aquaculture industry. View Full-Text
Keywords: entropy; fractal dimension; nonlinear analysis; image analysis; clustering; optical flow; pattern recognition; seafood safety; fish welfare; intelligent methods; environmental monitoring; aquaculture entropy; fractal dimension; nonlinear analysis; image analysis; clustering; optical flow; pattern recognition; seafood safety; fish welfare; intelligent methods; environmental monitoring; aquaculture
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Eguiraun, H.; López-de-Ipiña, K.; Martinez, I. Application of Entropy and Fractal Dimension Analyses to the Pattern Recognition of Contaminated Fish Responses in Aquaculture. Entropy 2014, 16, 6133-6151.

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