Review: The use of computer vision technologies in aquaculture - A review

Computer vision technology is a sophisticated inspection technology that is in common use in various industries. However, it is not as widely used in aquaculture. Application of computer vision technologies in aquaculture, the scope of the present review, is very challenging. The inspected subjects are sensitive, easily stressed and free to move in an environment in which lighting, visibility and stability are generally not controllable, and the sensors must operate underwater or in a wet environment. The review describes the state of the art and the evolution of computer vision in aquaculture, at all stages of production, from hatcheries to harvest. The review is organized according to inspection tasks that are common to almost all production systems: counting, size measurement and mass estimation, gender detection and quality inspection, species and stock identification, and monitoring of welfare and behavior. The objective of the review is to highlight areas of research and development in the field of computer vision which have made some progress, but have not matured into a useful tool. There are many potential applications for this technology in aquaculture which could be useful for improving product quality or production efficiency. There have been quite a few initiatives in this direction, and a tight collaboration between engineers, fish physiologists and ethologists could contribute to the search for, and development of solutions for the benefit of aquaculture.

[1]  Emanuele Trucco,et al.  A trainable system for grading fish from images , 2001, Appl. Artif. Intell..

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

[3]  Boaz Zion,et al.  In-vivo fish sorting by computer vision , 2000 .

[4]  Joseph Wilder,et al.  Fish detection and classification system , 2001, J. Electronic Imaging.

[5]  Norval J. C. Strachan,et al.  Length measurement of fish by computer vision , 1993 .

[6]  C. R. Savage,et al.  Underwater fish-video images: Image quality and edge detection techniques , 1994 .

[7]  Laurence T. Kell,et al.  A potential method for the differentiation between haddock fish stocks by computer vision using canonical discriminant analysis , 1995 .

[8]  P. R. Witthames,et al.  An automated method for counting and sizing fish eggs , 1987 .

[9]  Michele Scardi,et al.  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. , 2009 .

[10]  Lindsay G. Ross,et al.  Predicting biomass of Atlantic salmon from morphometric lateral measurements , 1996 .

[11]  M. G. Poxton,et al.  The Remote Estimation of Weight and Growth in Turbot Using Image Analysis , 1987 .

[12]  Joseph E. Merz,et al.  MORPHOLOGICAL FEATURES USED TO IDENTIFY CHINOOK SALMON SEX DURING FISH PASSAGE , 2004 .

[13]  Michel Larinier,et al.  Identification and counting of live fish by image analysis , 1994, Electronic Imaging.

[14]  L. F. Pau,et al.  PDL-HM: morphological and syntactic shape classification algorithm , 2005, Machine Vision and Applications.

[15]  Francisco Arreguín Sánchez,et al.  Length-weight relationship of demersal fish from the eastern coast of the mouth of the Gulf of California , 2010 .

[16]  Paolo Menesatti,et al.  A Novel Morphometry-Based Protocol of Automated Video-Image Analysis for Species Recognition and Activity Rhythms Monitoring in Deep-Sea Fauna , 2009, Sensors.

[17]  Kevin D. Friedland,et al.  Automated egg counting and sizing from scanned images : Rapid sample processing and large data volumes for fecundity estimates , 2005 .

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

[19]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[20]  Rabab K. Ward,et al.  Detection and counting of uneaten food pellets in a sea cage using image analysis , 1995 .

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

[22]  Frode Oppedal,et al.  A video analysis procedure for assessing vertical fish distribution in aquaculture tanks , 2007 .

[23]  Bahar Gümüş,et al.  Prediction of the weight of Alaskan pollock using image analysis. , 2010, Journal of food science.

[24]  Tsutomu Takagi,et al.  A digital stereo-video camera system for three-dimensional monitoring of free-swimming Pacific bluefin tuna, Thunnus orientalis, cultured in a net cage , 2011 .

[25]  Nigel J. B. McFarlane,et al.  Estimating Dimensions of Free-Swimming Fish Using 3D Point Distribution Models , 2000, Comput. Vis. Image Underst..

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

[27]  Jo Arve Alfredsen,et al.  Automatic measurement of rotifer Brachionus plicatilis densities in first feeding tanks , 2007 .

[28]  M. Balaban,et al.  Using image analysis to predict the weight of Alaskan salmon of different species. , 2010, Journal of food science.

[29]  John Reidar Mathiassen,et al.  Trends in application of imaging technologies to inspection of fish and fish products , 2011 .

[30]  N.J.C. Strachan.  Sea trials of a computer vision based fish species sorting and size grading machine , 1994 .

[31]  E. Cren,et al.  THE LENGTH-WEIGHT RELATIONSHIP AND SEASONAL CYCLE IN GONAD WEIGHT AND CONDITION IN THE PERCH , 2022 .

[32]  P. Hufschmied,et al.  Automatic stress‐free sorting of sturgeons inside culture tanks using image processing , 2011 .

[33]  Murat O. Balaban,et al.  Analysis of Skin Color Development in Live Goldfish Using a Color Machine Vision System , 2002 .

[34]  Victor Alchanatis,et al.  Classification of guppies’ (Poecilia reticulata) gender by computer vision , 2008 .

[35]  Raymond Williams,et al.  A Computer Vision System to Analyse the Swimming Behaviour of Farmed Fish in Commercial Aquaculture Facilities: a Case Study using Cage-held Atlantic Salmon , 2011 .

[36]  Sadami Yada,et al.  Weighing Type Counting System for Seedling Fry. , 1997 .

[37]  Eitan Kimmel,et al.  Behavioral response of carp (Cyprinus carpio) to ammonia stress , 1998 .

[38]  T. Petochi,et al.  Sex and reproductive stage identification of sturgeon hybrids (Acipenser naccarii × Acipenser baerii) using different tools: ultrasounds, histology and sex steroids , 2011 .

[39]  Fred S. Conte,et al.  Stress and the welfare of cultured fish , 2004 .

[40]  Steven X. Cadrin,et al.  Advances in morphometric identification of fishery stocks , 2000, Reviews in Fish Biology and Fisheries.

[41]  Eitan Kimmel,et al.  Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision , 1996 .

[42]  Joan Oca,et al.  Measurement of sole activity by digital image analysis , 2009 .

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

[44]  François Michaud,et al.  Intelligent system for automated fish sorting and counting , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

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

[46]  G. Hulata,et al.  Color variability in normal and gynogenetic progenies of ornamental (Koi) common carp (Cyprinus carpio L.) , 1995 .

[47]  Boaz Zion,et al.  Sorting fish by computer vision , 1999 .

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

[49]  Kit Rawson,et al.  Technical Notes: Accuracy and Precision of Counting Eyed Eggs with an Electronic Fish Counter , 1988 .

[50]  Herbert Spencer,et al.  The Principles of Biology , 1863, The British and Foreign Medico-Chirurgical Review.

[51]  Frank Storbeck,et al.  Fish species recognition using computer vision and a neural network , 2001 .

[52]  Murat O. Balaban,et al.  Prediction of the Weight of Aquacultured Rainbow Trout (Oncorhynchus mykiss) by Image Analysis , 2010 .

[53]  Paul F. Newbury,et al.  Automatic fish population counting by artificial neural network , 1995 .

[54]  Boaz Zion,et al.  Guidance of single guppies (Poecilia reticulata) to allow sorting by computer vision , 2003 .

[55]  J. Huxley.  Constant Differential Growth-Ratios and their Significance , 1924, Nature.

[56]  Victor Alchanatis,et al.  Guidance of groups of guppies (Poecilia reticulata) to allow sorting by computer vision , 2005 .

[57]  Royann J. Petrell,et al.  Accuracy of a machine-vision pellet detection system , 2003 .

[58]  Norval J. C. Strachan,et al.  Automated measurement of species and length of fish by computer vision , 2006 .

[59]  Kevin D. Friedland,et al.  The utility of image processing techniques for morphometric analysis and stock identification , 1999 .

[60]  F. Oppedal,et al.  Artificial light and season affects vertical distribution and swimming behaviour of post-smolt Atlantic salmon in sea cages , 2001 .

[61]  Juan R. Rabuñal,et al.  Optical Fish Trajectory Measurement in Fishways through Computer Vision and Artificial Neural Networks , 2011, J. Comput. Civ. Eng..