Guidance of single guppies (Poecilia reticulata) to allow sorting by computer vision

Aquacultural Engineering

Volumn 27, Issue 3, 2003, Pages 177-190

  Karplus, I ^a   Gottdiener, M ^a   Zion, B ^a  

^a VOLCANI CENTER   (Israel)

Author keywords

Computer vision; Fish guidance; Fish sorting; Poecilia reticulata

Indexed keywords

COMPUTER VISION; DECISION MAKING; FISHERIES;

SORTING APPARATUS;

AQUACULTURE;

FISH CULTURE;

POECILIA RETICULATA;

EID: 0037370863     PISSN: 01448609     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0144-8609(02)00085-7     Document Type: Article

References (20)

1. Abbott, R.R., 1972. Induced aggregation of pond-reared rainbow trout (Salmo gairdneri) through acoustic conditioning. Trans. Am. Fish. Soc. 101, 35-43.
2. Arnarson, H., Pau, L.F., 1994. PDH-HM: morphological and syntactic shape classification algorithm. Real-time application to fish species classification. Machine Vision Applications 7, 59-68.
3. Bjornsson, B., 1999. Fjord-ranching of wild cod in an Icelandic fjord: effects of feeding on nutritional condition, growth rate and behaviour. In: Howell, B.R., Moksness, E., Svasand, T. (eds.), Stock enhancement and sea ranching, pp. 257-276, Fishing News Books, Blackwell Science, 606 pp.
4. Crow, R.T., Liley, N.R., 1979. A sexual pheromone in the guppy, Poecilia reticulata (Peters). Can. J. Zool. 57, 184-188.
5. Gandolfi, G., 1969. A chemical sex attractant in the guppy Poecilia reticulata Peters (Pisces, Poeciliidae). Monit. Zool. Ital. 3, 89-98.
6. Jain, V.K., Sahai, S., 1994. Phototactic response of some fishes. Environ. Ecol. 12, 123-127.
7. Kuwada, H., Masuda, R., Shiozawa, S., Kogane, T., Imaizumi, K., Tsukamoto, K., 2000. Effect of fish size, handling stress and training procedure on the swimming behavior of hatchery-reared striped jack: implications for stock enhancement. Aquaculture 185, 245-256.
8. Levin, L.E., Levin, A.F., 1994. Conditioning as an aid to fish harvest. Aquacul. Eng. 13, 201-210.
9. Masuda, R., Tsukamoto, K., 1998. Stock enhancement in Japan: review and perspective. Bull. Mar. Sci. 62, 337-358.
10. Pau, L.F., Olafsson, R., 1991. Fish Quality Control by Computer Vision. M. Dekker, New York, p. 296.
11. Savage, C.R., Petrell, R.J., Neufeld, T.P., 1994. Underwater fish-video images: image quality and edge detection techniques. Can. Agric. Eng. 36, 175-183.
12. Strachan, N.J.C., 1993. Length measurement of fish by computer vision. Comput. Electron. Agric. 8, 93-104.
13. Strachan, N.J.C., 1994. Sea trials of a computer based fish species sorting and size grading machine. Mechantronics 4, 773-783.
14. Strachan, N.J.C., Kell, L., 1995. A potential method for the differentiation between haddock fish stocks by computer vision using canonical discriminant analysis. ICES J. Marine Sci. 52, 145-149.
15. Tayama, M., Shimadate, N., Kubota, N., Nomure, Y., 1982. Application for optical sensor to fish sorting. Reito (Tokyo), Refrigeration 57, 1146-1150.
16. Ungson, R.J., Matsuda, Y., Hirata, H., 1995. Ranching the red sea bream in Japan. World Aquacul. 26, 6-12.
17. Wagner, H., Schmidt, U., Rudek, J.H., 1987. Distinction between species of sea fish. Lebesmittelindustrie 34, 20-23.
18. Zion, B., Shklyar, A., Karplus, I., 1999. Sorting fish by computer vision. Comput. Elect. Agric. 23, 175-187.
19. Zion, B., Shklyar, A., Karplus, I., 2000a. In-vivo fish sorting by computer vision. Aquacul. Eng. 22, 165-179.
20. Zion, B., Shklyar, A., Shalaev, S., Alchanatis, V., Hezroni, A., Karplus, I., 2000b. Sex determination and quality sorting of guppy fish by computer vision. The XIV Memorial CIGR World Congress, Tsukuba, Japan.