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Aquacultural Engineering
Zion, B., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Barki, A., Institute of Animal Science, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Karplus, I., Institute of Animal Science, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Labor costs of guppy growers and breeders are largely those of manual sorting (by strain, quality and gender) and counting fish. In most farms, female and male fish are grown together and sold either separately or together. Sorting fish according to gender is important for marketing as well as for breeding programs, so that a device for sorting and counting fish can potentially reduce production costs and improve quality. A project aiming to develop sorting and counting technologies for ornamental fish growers included development and testing of image-processing algorithms for sorting guppy fish (Poecilia reticulata) by gender. The algorithms are derived from shape and color differences between female and male guppies. An algorithm for the determination of landmarks on fish contours was developed and found to be accurate in accordance with human judgment, enabling extraction of specific shape and color features of the tail and the body. The algorithms were applied to three sets of images of guppies of the "Red-Blond" strain. Gender identification accuracy was approximately 90% using shape features, approximately 96% using color features and was slightly improved when both color and shape features were used. Some of the components used are essential for future development of a computer vision based system for sorting and grading ornamental fish by strain and quality. © 2008 Elsevier B.V. All rights reserved.
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Classification of guppies' (Poecilia reticulata) gender by computer vision
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Zion, B., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Ostrovsky, V., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Barki, A., Institute of Animal Science, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Karplus, I., Institute of Animal Science, Agricultural Research Organization, Volcani Center, P.O. Box 6, Bet-Dagan, 50250, Israel
Classification of guppies' (Poecilia reticulata) gender by computer vision
Labor costs of guppy growers and breeders are largely those of manual sorting (by strain, quality and gender) and counting fish. In most farms, female and male fish are grown together and sold either separately or together. Sorting fish according to gender is important for marketing as well as for breeding programs, so that a device for sorting and counting fish can potentially reduce production costs and improve quality. A project aiming to develop sorting and counting technologies for ornamental fish growers included development and testing of image-processing algorithms for sorting guppy fish (Poecilia reticulata) by gender. The algorithms are derived from shape and color differences between female and male guppies. An algorithm for the determination of landmarks on fish contours was developed and found to be accurate in accordance with human judgment, enabling extraction of specific shape and color features of the tail and the body. The algorithms were applied to three sets of images of guppies of the "Red-Blond" strain. Gender identification accuracy was approximately 90% using shape features, approximately 96% using color features and was slightly improved when both color and shape features were used. Some of the components used are essential for future development of a computer vision based system for sorting and grading ornamental fish by strain and quality. © 2008 Elsevier B.V. All rights reserved.
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