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JRM Vol.30 No.2 pp. 231-237
doi: 10.20965/jrm.2018.p0231
(2018)

Paper:

Body Measurement of Reared Red Sea Bream Using Stereo Vision

Kazuyoshi Komeyama*1, Tatsuya Tanaka*2, Takeharu Yamaguchi*3, Shigeru Asaumi*3, Shinsuke Torisawa*4, and Tsutomu Takagi*1

*1Faculty of Fisheries Sciences, Hokkaido University
3-1-1 Minato-cho, Hakodate 041-8611, Japan

*2Graduate School of Fisheries Sciences, Hokkaido University
3-1-1 Minato-cho, Hakodate 041-8611, Japan

*3FURUNO Electric Co., Ltd.
9-52 Ashihara-cho, Nishinomiya 662-8580, Japan

*4Department of Agriculture, Kindai University
3327-204 Nakamachi, Nara 631-0052, Japan

Received:
September 20, 2017
Accepted:
February 1, 2018
Published:
April 20, 2018
Keywords:
stereo image measurement, aquaculture management, Pagrus major
Abstract

For aquaculture management, aquaculture farmers require a new, inexpensive device that can obtain the size of a fish without touching them, replacing the conventional spoon-net sampling method. Conventional sampling involves the risks of physical injury and mental stress to the fish, which may affect their growth rate and mortality. Therefore, we developed methods for monitoring the size of fish, considering red sea bream (RSB) aquaculture, using commercially available cameras. This study evaluates the sample size using the estimated mean fork length value in a cage, and its value is approximately 20 samples with a 2% error rate for a fork length of greater than 30 cm. We measured the fish fork length under water in the cage using both stereo vision and net-sampling methods simultaneously. The examination demonstrated that for RSB aquaculture, the estimated values of fork length from the two methods have no statistical difference. This result implies that our stereo vision system can be effectively applied to monitor RSB growth.

Reared <i>Pagrus major</i> in an aquaculture net-cage

Reared Pagrus major in an aquaculture net-cage

Cite this article as:
K. Komeyama, T. Tanaka, T. Yamaguchi, S. Asaumi, S. Torisawa, and T. Takagi, “Body Measurement of Reared Red Sea Bream Using Stereo Vision,” J. Robot. Mechatron., Vol.30 No.2, pp. 231-237, 2018.
Data files:
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