A dual camera system for counting and sizing northern bluefin tuna (Thunnus thynnus; Linnaeus, 1758) stock, during transfer to aquaculture cages, with a semi automatic Artificial Neural Network tool.

A dual underwater camera system connected, synchronized and powered via cable to a waterproof transportable computer, was developed. This device was used for acquiring tuna images during the transfer from fishing net to a floating cage, thus validating the potential advantages of the use of underwater video monitoring. Two images per second were recorded during tuna transfer and stored for biometric analyses, which were carried out by means of a software tool based on Artificial Neural Networks (ANNs). The ANN was trained to convert distance between pixels in each pair of images into distance between points in the real objects, automatically correcting the estimates for distance from the cameras and aspect. The ANN-based software tool allowed analysing tuna transfer images, providing biometric information of about 1000 tunas. The results were compared to conventional assessment methods.

[1]  Peter Doherty,et al.  Innovative new methods for measuring the natural dynamics of some structurally dominant tropical sponges and other sessile fauna , 1999 .

[2]  Neil M. White,et al.  Sensors and their Applications VIII , 1997 .

[3]  Timothy Masters.  Signal and Image Processing with Neural Networks: A C++ Sourcebook , 1994 .

[4]  Michele Scardi,et al.  Preliminary observations on bluefin tuna (Thunnus thynnus, Linnaeus 1758) behaviour in captivity , 2010 .

[5]  Michele Scardi,et al.  Assessing the Uncertainties of Model Estimates of Primary Productivity in the Tropical Pacific Ocean Revised , 2008 .

[6]  Aníbal Ollero,et al.  Computer vision and robotics techniques in fish farms , 2003, Robotica.

[7]  M. Scardi,et al.  Application of the Self-Organizing Map to the study of skeletal anomalies in aquaculture: The case of dusky grouper (Epinephelus marginatus Lowe, 1834) juveniles reared under different rearing conditions , 2011 .

[8]  Euan S. Harvey,et al.  Calibration stability of an underwater stereo-video system : Implications for measurement accuracy and precision , 1998 .

[9]  M. Scardi,et al.  Fisheries yield and primary productivity in large marine ecosystems , 2010 .

[10]  R. L. Dunbrack.  In situ measurement of fish body length using perspective-based remote stereo-video , 2006 .

[11]  Michele Scardi,et al.  Challenges of modeling depth‐integrated marine primary productivity over multiple decades: A case study at BATS and HOT , 2010 .

[12]  Hilko van der Voet,et al.  Statistical aspects of environmental risk assessment of GM plants for effects on non-target organisms. , 2009, Environmental biosafety research.

[13]  J. A. Marchant,et al.  Fish sizing and monitoring using a stereo image analysis system applied to fish farming , 1995 .

[14]  Wang JawKai.  Techniques for modern aquaculture. , 1993 .

[15]  Royann J. Petrell,et al.  Swimming speed and morphological features of mixed populations of early maturing and non-maturing fish , 1999 .

[16]  Michele Scardi,et al.  Progress in modeling quality in aquaculture: an application of the Self-Organizing Map to the study of skeletal anomalies and meristic counts in gilthead seabream (Sparus aurata, L. 1758). , 2010 .

[17]  Peter J. B. Hancock,et al.  Using truss networks to estimate the biomass of Oreochromis niloticus, and to investigate shape characteristics. , 2000 .

[18]  A. Penna,et al.  Monitoring toxic microalgae Ostreopsis (dinoflagellate) species in coastal waters of the Mediterranean Sea using molecular PCR-based assay combined with light microscopy. , 2010, Marine pollution bulletin.

[19]  Euan S. Harvey,et al.  A comparison of the precision and accuracy of estimates of reef-fish lengths determined visually by divers with estimates produced by a stereo-video system. , 2001 .

[20]  D. L. Davis,et al.  Measurements with underwater video: camera flield width calibration and structured lighting , 1992 .

[21]  Michele Scardi,et al.  Extracting fish size using dual underwater cameras , 2006 .

[22]  M. New,et al.  Capture-based aquaculture: the fattening of eels, groupers, tunas and yellowtails. , 2004 .

[23]  A. Mangano.  BLUEFIN TUNA (THUNNUS THYNNUS L.) SIZE COMPOSITION IN CAGES FROM THE TYRRHENIAN SEA AND THE STRAIT OF SICILY IN 2004 , 2006 .

[24]  Walker O. Smith,et al.  An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across the globe , 2010 .

[25]  Michele Scardi,et al.  Using image analysis on the ventral colour pattern in Salamandrina perspicillata (Amphibia: Salamandridae) to discriminate among populations , 2008 .

[26]  M. Shortis,et al.  Improving the statistical power of length estimates of reef fish: a comparison of estimates determined visually by divers with estimates produced by a stereo-video system. , 2001 .

[27]  Victor Alchanatis,et al.  Real-time underwater sorting of edible fish species , 2007 .

[28]  Stuart Robson,et al.  The accuracy and precision of underwater measurements of length and maximum body depth of southern bluefin tuna (Thunnus maccoyii) with a stereo-video camera system , 2003 .

[29]  John J. Videler,et al.  A simple field method for stereo-photographic length measurement of free-swimming fish: Merits and constraints , 1996 .

[30]  John A. Marchant,et al.  Predicting salmon biomass remotely using a digital stereo-imaging technique , 1996 .

[31]  Michele Scardi,et al.  An expert system based on fish assemblages for evaluating the ecological quality of streams and rivers , 2008, Ecol. Informatics.

[32]  J. Lines,et al.  An automatic image-based system for estimating the mass of free-swimming fish , 2001 .

[33]  Rabab K. Ward,et al.  Determining fish size and swimming speed in cages and tanks using simple video techniques , 1997 .