A video analysis procedure for assessing vertical fish distribution in aquaculture tanks

This paper describes an economical and efficient video analysis procedure for registering vertical fish distribution in aquaculture tanks. The procedure can be summarized in three steps: (1) A digital underwater camera is used to film a section of a tank wall marked with one or more vertical black lines. (2) Automatic image analysis is used to identify those parts of the lines that are not obstructed by fish in the individual image-frames. (3) The visible parts of the lines are compared with the known extent of the lines and the percentage coverage is calculated. The procedure does not require uniform or stable lighting conditions, only that the black stripes are clearly visible. A case study demonstrates that the procedure can be used to document how fish in a classical conditioning learning procedure react to a sudden fright stressor in the first trial and how the fish gradually become habituated to the stressor and start to associate it with feeding in subsequent trials. Since the procedure offers a good description of vertical distribution over time, is cost effective and easy to implement, we believe that it is likely to become a standard for describing fish behaviour in aquaculture tanks.

[1]  J. Sumpter,et al.  Fish stress and health in aquaculture , 1998 .

[2]  Colin Hunter,et al.  A video-based movement analysis system to quantify behavioral stress responses of fish. , 2004, Water research.

[3]  G. Wedemeyer.  Physiology of Fish in Intensive Culture Systems , 1996, Springer US.

[4]  J. W. Tucker.  Marine Fish Culture , 1998 .

[5]  Gilson Luiz Volpato,et al.  Conditioning of stress in Nile tilapia , 2004 .

[6]  Edward R. Dougherty,et al.  Mathematical Morphology in Image Processing , 1992 .

[7]  Donald M. Broom,et al.  Animal welfare defined in terms of attempts to cope with the environment , 1996 .

[8]  K. Muramoto,et al.  Fast and slow recovery phases of goldfish behavior after transection of the optic nerve revealed by a computer image processing system , 1999, Neuroscience.

[9]  B. Spruijt,et al.  A concept of welfare based on reward evaluating mechanisms in the brain: anticipatory behaviour as an indicator for the state of reward systems. , 2001, Applied animal behaviour science.

[10]  Michael J. Brooks,et al.  Issues in Automated Visual Surveillance , 2003, DICTA.

[11]  Anders Fernö,et al.  Swimming behaviour as an indicator of low growth rate and impaired welfare in Atlantic halibut (Hippoglossus hippoglossus L.) reared at three stocking densities , 2004 .

[12]  P. Witten,et al.  Impact of high water carbon dioxide levels on Atlantic salmon smolts (Salmo salar L.) : effects on fish performance, vertebrae composition and structure , 2006 .

[13]  Frode Oppedal,et al.  Effect of environmental factors on swimming depth preferences of Atlantic salmon (Salmo salar L.) and temporal and spatial variations in oxygen levels in sea cages at a fjord site , 2006 .

[14]  Vågsholm,et al.  Risk factors for spinal deformities in Atlantic salmon, Salmo salar L. , 1998, Journal of fish diseases.

[15]  Richard D. Zakia,et al.  The Focal encyclopedia of photography , 1993 .

[16]  Ying Liu,et al.  Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved oxygen levels as monitored by computer vision , 2006 .

[17]  T. Ellis,et al.  The relationships between stocking density and welfare in farmed rainbow trout , 2002 .

[18]  R. S. McKinley,et al.  Can feeding status and stress level be assessed by analyzing patterns of muscle activity in free swimming rainbow trout (Oncorhynchus mykiss Walbaum) , 2004 .

[19]  Heinrich Niemann,et al.  Illumination Insensitive Template Matching with Hyperplanes , 2003, DAGM-Symposium.

[20]  Sun-Yuan Kung,et al.  Multimedia Image and Video Processing , 2000 .

[21]  Paul J. Ashley.  Fish welfare: Current issues in aquaculture , 2007 .

[22]  Y. Liu,et al.  Behavioral response of tilapia (Oreochromis niloticus) to acute ammonia stress monitored by computer vision. , 2005, Journal of Zhejiang University. Science. B.

[23]  L.-F F. Pau,et al.  Fish Quality Control by Computer Vision , 1991 .

[24]  Tetsu Nemoto,et al.  A computer image processing system for quantification of zebrafish behavior , 2004, Journal of Neuroscience Methods.

[25]  Tom G. Pottinger,et al.  Current issues in fish welfare , 2006 .

[26]  Milan Sonka,et al.  Image Processing, Analysis and Machine Vision , 1993, Springer US.

[27]  A. R. Frost,et al.  Selective attraction of salmon , 1997 .

[28]  Donat-Peter Häder.  Image analysis : methods and applications , 2001 .

[29]  M. Dawkins,et al.  Using behaviour to assess animal welfare , 2004, Animal Welfare.

[30]  C. Schreck,et al.  Conditioning improves performance of juvenile chinook salmon, Oncorhynchus tshawytscha, to transportation stress☆ , 1995 .

[31]  Tao Xie,et al.  Novel approach for determining the optimal axial preload of a simulating rotary table spindle system , 2007 .

[32]  D. Fraser.  Understanding animal welfare , 2008, Acta Veterinaria Scandinavica.

[33]  R. Moccia,et al.  An evaluation of current perspectives on consciousness and pain in fishes , 2004 .

[34]  E. R. Davies,et al.  Image Processing for the Food Industry , 2000, Series in Machine Perception and Artificial Intelligence.