The use of computer vision technologies in aquaculture - A review

Computers and Electronics in Agriculture

Volumn 88, Issue , 2012, Pages 125-132

  Zion, Boaz ^a  

^a VOLCANI CENTER   (Israel)

Author keywords

Aquaculture; Biomass; Computer vision; Gender; Species; Welfare

Indexed keywords

COMPUTER VISION TECHNOLOGY; GENDER; GENDER DETECTION; INSPECTION TASKS; INSPECTION TECHNOLOGY; MASS ESTIMATION; POTENTIAL APPLICATIONS; PRODUCT QUALITY; PRODUCTION EFFICIENCY; PRODUCTION SYSTEM; QUALITY INSPECTION; RESEARCH AND DEVELOPMENT; SIZE MEASUREMENTS; SPECIES; STATE OF THE ART; STOCK IDENTIFICATION; WELFARE;

AQUACULTURE; BIOMASS; INSPECTION; TECHNOLOGY;

COMPUTER VISION;

ANIMAL WELFARE; AQUACULTURE METHOD; AQUACULTURE PRODUCTION; COMPUTER VISION; GENDER; HARVESTING; HATCHERY; LITERATURE REVIEW; PHYSIOLOGY; RESOURCE DEVELOPMENT; SENSOR; TECHNOLOGICAL DEVELOPMENT;

EID: 84865059368     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2012.07.010     Document Type: Review

References (70)

1. Aguzzi J., Costa C., Fujiwara Y., Iwase R., Ramirez-Llorda E., Menesatti P. A novel morphometry-based protocol of automated video-image analysis for species recognition and activity rhythms monitoring in deep-sea fauna. Sensors 2009, 9:8438-8455.
2. Alver M.O., Tennoy T., Alfredsen J.A., Oie G. Automatic measurement of rotifer Brachionus plicatilis densities in first feeding tanks. Aquacult. Eng. 2007, 36:115-121.
3. Arnarson H., Pau L.F. PDH-HM: morphological and syntactic shape classification algorithm. Real-time application to fish species classification. Mach. Vis. Appl. 1994, 7:59-68.
4. Balaban M.O., Chombeau M., Cirban D., Gumus B. Prediction of the weight of alaskan pollock using image analysis. J. Food Sci. 2010, 75:E552-E556.
5. Balaban M.O., Sengor G.F.U., Soriano M.G., Ruiz E.G. Using image analysis to predict the weight of alaskan salmon of different species. J. Food Sci. 2010, 75:E157-E162.
6. Beddow T.A., Ross L.G. Predicting biomass of Atlantic salmon from morphometric lateral measurements. J. Fish Biol. 1996, 49:469-482.
7. Beddow T.A., Ross L.G., Marchant J.A. Predicting salmon biomass remotely using a digital stereo-imaging technique. Aquaculture 1996, 146:189-203.
8. Bookstein, F.L., 1990. Introduction to methods for landmark data, In: Rohlf, F.J., Bookstein, F.L. (Eds.), Michigan Morphometrics Workshop. University of Michigan, Museum of Zoology, Michigan, USA, pp. 215-226.
9. Cadieux, S., Lalonde, F., Michaud, F., 2000. Intelligent system for automated fish sorting and counting. International Conference on Intelligent Robots and Systems (IROS). IEEE/RSJ, Takamatsu, Japan, pp. 1279-1284.
10. Cardin S., Friedland K.D. The utility of image processing techniques for morphometric analysis and stock identification. Fish. Res. 1999, 43:129-139.
11. Cardin S.X. Advances in morphometric identification of fishery stocks. Rev. Fish Biol. Fish. 2000, 10:91-112.
12. Castignolles N., Cattoen M., Larinier M. Identification and counting of live fish by image analysis. Image and Video Processing II 1994, 200-209. SPIE, San Jose, CA, USA. S.A. Rajala, R.L. Stevenson (Eds.).
13. Conte F.S. Stress and the welfare of cultured fish. Appl. Anim. Behav. Sci. 2004, 86:205-223.
14. Costa C., Loy A., Cataudella S., Davis D., Scardi M. Extracting fish size using dual underwater cameras. Aquacult. Eng. 2006, 35:218-227.
15. Costa C., Scardi M., Vitalini V., Cataudella S. 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. Aquaculture 2009, 291:161-167.
16. Di Marco P., Donadelli V., Longobardi A., Corsalini I., Bertotto D., Finoia M.G., Marino G. Sex and reproductive stage identification of sturgeon hybrids (acipenser naccariiaAaAcipenser baerii) using different tools: ultrasounds, histology and sex steroids. J. Appl. Ichthyol. 2011, 27:637-642.
17. Duarte S., Reig L., Oca J. Measurement of sole activity by digital image analysis. Aquacult. Eng. 2009, 41:22-27.
18. Foster M., Petrell R., Ito M.R., Ward R. Detection and counting of uneaten food pellets in a sea cage using image-analysis. Aquacult. Eng. 1995, 14:251-269.
19. Friedland K.D., Ama-Abasi D., Manning M., Clarke L., Kligys G., Chambers R.C. Automated egg counting and sizing from scanned images: rapid sample processing and large data volumes for fecundity estimates. J. Sea Res. 2005, 54:307-316.
20. Fulton T.W. The Rate of Growth of Fishes 1904, Fisheries Board of Scotland, Edinburgh, pp. 141-241.
21. Gomelski B., Chersas N.B., Hulata G., Ben-Dom N. Color variability in normal and gynogenetic progenies of ornamental (Koi) common carp (Cyprinus carpio L.). Aquaculture 1995, 137:102.
22. Gumus B., Balaban M.O. Prediction of the weight of aquacultured rainbow trout (oncorhynchus mykiss) by image analysis. J. Aquat. Food Prod. Technol. 2010, 19:227-237.
23. Hu M.K. Visual pattern recognition by moment invariants. IRE Trans. Inf. Theory 1962, 8:179-187.
24. Hufschmied P., Fankhauser T., Pugovkin D. Automatic stress-free sorting of sturgeons inside culture tanks using image processing. J. Appl. Ichthyol. 2011, 27:622-626.
25. Huxley J.S. Constant differential growth-ratios and their significance. Nature 1924, 114:895-896.
26. Israeli-Weinstein D., Kimmel E. Behavioral response of carp (Cyprinus carpio) to ammonia stress. Aquaculture 1998, 165:81-93.
27. Israeli D., Kimmel E. Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision. Aquacult. Eng. 1996, 15:423-440.
28. Joyce T.l., Rawson K. Accuracy and precision of counting eyed eggs with an electronic fish counter. Prog. Fish-Cult. 1988, 50:113-115.
29. Karplus I., Alchanatis V., Zion B. Guidance of groups of guppies (Poecilia reticulata) to allow sorting by computer vision. Aquacult. Eng. 2005, 32:509-520.
30. Karplus I., Gottdiener A., Zion B. Guidance of single guppies (Poecilia reticulata) to allow sorting by computer vision. Aquacult. Eng. 2003, 27:177-190.
31. Le Cren E.D. The length-weight relationship and seasonal cycle in gonad weight and condition in the perch (Perca fluviatilis). J. Anim. Ecol. 1951, 20:201-219.
32. Lines J.A., Tillett R.D., Ross L.G., Chan D., Hockaday S., McFarlane N.J.B. An automatic image-based system for estimating the mass of free-swimming fish. Comput. Elect. Agric. 2001, 31:151-168.
33. Love R.M. Stress and behaviour in the culture environment. Realism in Aquaculture: Achievements, Constraints, Perspectives 1981, 449-470. European Aquaculture Society, Venice, Italy. M. Bilio, H. Rosenthal, C.J. Sindermann (Eds.).
34. Martinez-de Dios J.R., Serna C., Ellero A. Computer vision and robotics techniques in fish farms. Robotica 2003, 21:233-243.
35. Mathiassen J.R., Misimi E., Bondo M., Veliyulin E., Ostvik S.O. Trends in application of imaging technologies to inspection of fish and fish products. Trends Food Sci. Technol. 2011, 22:257-275.
36. Merz J.E., Merz W.R. Morphological features used to identify chinook salmon sex during fish passage. Southwest. Nat. 2004, 49:197-202.
37. Newbury P.F., Culverhouse P.F., Pilgrim D.A. Automatic fish population counting by artificial neural network. Aquaculture 1995, 133:45-55.
38. Nieto-Navarro J.T., Zetina-Rejon M., Arreguin-Sanchez F., Arcos-Huitron N.E., Pena-Messina E. Length-weight relationship of demersal fish from the eastern coast of the mouth of the gulf of California. J. Fish. Aqua. Sci. 2010, 5:494-502.
39. Odone F., Trucco E., Verri A. A trainable system for grading fish from images. App. Art. Int. 2001, 15:735-745.
40. Oppedal F., Juell J.E., Taranger G.L., Hansen T. Artificial light and season affects vertical distribution and swimming behaviour of post-smolt Atlantic salmon in sea cages. J. Fish Biol. 2001, 58:1570-1584.
41. Parsonage K.D., Petrell R.J. Accuracy of a machine-vision pellet detection system. Aquacult. Eng. 2003, 29:109-123.
42. Pinkiewicz T.H., Purser G.J., Williams R.N. A computer vision system to analyse the swimming behaviour of farmed fish in commercial aquaculture facilities: a case study using cage-held Atlantic salmon. Aquacult. Eng. 2011, 45:20-27.
43. Poxton M.G., Goldsworthy G.T. The remote estimation of weight and growth in turbot using image analysis. Automation and Data Processing in Aquaculture 1987, 163-170. Pergamon Press, Oxford. J.G. Balchen (Ed.).
44. Rodriguez A., Bermudez M., Rabunal J.R., Puertas J., Dorado J., Pena L., Balairon L. Optical fish trajectory measurement in fishways through computer vision and artificial neural networks. J. Comput. Civ. Eng. 2011, 25:291-301.
45. Ruff B.P., Marchant J.A., Frost A.R. Fish sizing and monitoring using a stereo image-analysis system applied to fish farming. Aquacult. Eng. 1995, 14:155-173.
46. Savage C.R., Petrell R.J., Neufeld T.P. Underwater fish-video images - image quality and edge-detection techniques. Can. Agric. Eng. 1994, 36:175-183.
47. Spencer H. The Principles of Biology 1898, Williams & Norgate, London.
48. Stien L.H., Brafland S., Austevollb I., Oppedala F., Kristiansen T.S. A video analysis procedure for assessing vertical fish distribution in aquaculture tanks. Aquacult. Eng. 2007, 37:115-124.
49. Storbeck F., Daan B. Fish species recognition using computer vision and a neural network. Fish. Res. 2001, 51:11-15.
50. Strachan N.J.C. Length measurement of fish by computer vision. Comput. Elect. Agric. 1993, 8:93-104.
51. Strachan N.J.C. Sea trials of a computer vision based fish species sorting and size grading machine. Mechantronics 1994, 4:773-783.
52. Strachan N.J.C., Kell L. A potential method for the differentiation between haddock fish stocks by computer vision using canonical discriminant analysis. ICES J. Mar. Sci. 1995, 52:145-149.
53. Tayama I., Shimadate M., Kubuta N., Nomura Y. Application for optical sensor to fish sorting. Reito (Tokyo) Refrigeration 1982, 57:1146-1150.
54. Tidd R.A., Wilder J. Fish detection and classification system. J. Electron. Imag. 2001, 10:283-288.
55. Tillett R., McFarlane N., Lines J. Estimating dimensions of free-swimming fish using 3D point distribution models. Comput. Vis. Image Underst. 2000, 79:123-141.
56. Torisawa S., Kadota M., Komeyama K., Suzuki K., Takagi T. A digital stereo-video camera system for three-dimensional monitoring of free-swimming Pacific bluefin tuna, Thunnus orientalis, cultured in a net cage. Aquat. Living Resour. 2011, 24:107-112.
57. Wagner H., Schmidt U., Rudek J.H. Distinction between species of sea fish. Lebensmittelindustrie 1987, 34:20-23.
58. Wallat G.K., Luzuriaga D.A., Balaban M.O., Chapman F.A. Analysis of skin color development in live goldfish using a color machine vision system. N. Am. J. Aquacult. 2002, 64:79-84.
59. White D.J., Svellingen C., Strachan N.J.C. Automated measurement of species and length of fish by computer vision. Fish. Res. 2006, 80:203-210.
60. Winans G.A. Using morphometric and meristic characters for identifying stock of fish. Stock Identification Workshop 1985, 135-146. NOAA, Panam City Beach, FL, USA. H.E. Kumpf, R.N. Vaught, C.B. Grimes, A.G. Jhonason, E.L. Nakamura (Eds.).
61. Witthames P.R., Walker M.G. An automated-method for counting and sizing fish eggs. J. Fish Biol. 1987, 30:225-235.
62. Wohlfarth G.W., Rothbard S. Preliminary investigation on color inheritance in Japanese ornamental carp (Nishihiki goi). Isr. J. Aquacult./Bamidgeh 1991, 43:62-68.
63. Xu J.Y., Liu Y., Cui S.R., Miao X.W. Behavioral responses of tilapia (Oreochromis niloticus) to acute fluctuations in dissolved oxygen levels as monitored by computer vision. Aquacult. Eng. 2006, 35:207-217.
64. Yada S., Chen H. Weighing type counting system for seedling fry. Nippon Suisan Gakkaishi 1997, 63:178-183.
65. Zion B., Alchanatis V., Ostrovsky V., Bark i.A., Karplus I. Real-time underwater sorting of edible fish species. Comput. Elect. Agric. 2007, 56:34-45.
66. Zion B., Alchanatis V., Ostrovsky V., Barki A., Karplus I. Classification of guppies' (Poecilia reticulata) gender by computer vision. Aquacult. Eng. 2008, 38.
67. Zion B., Doitch N., Ostrovsky V., Alchanatis V., Segev R., Barki A., Karplus I. Ornamental Fish Fry Counting by Image Processing 2006, Agricultural Research Organization, Bet Dagan.
68. Zion B., Ostrovsky V., Karplus I., Lidor G., Barki A. Ornamental Fish Mass Estimation by Image Processing 2012, Agricultural Research Organization, Bet Dagan.
69. Zion B., Shklyar A., Karplus I. Sorting fish by computer vision. Comput. Elect. Agric. 1999, 23:175-187.
70. Zion B., Shklyar A., Karplus I. In-vivo fish sorting by computer vision. Aquacult. Eng. 2000, 22:165-179.