Page 217 - 《水产学报》2023年第1期

王禹莎，等 水产学报, 2023, 47(1): 019516

Application of computer vision in morphological and body weight
measurements of large yellow croaker (Larimichthys crocea)

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WANG Yusha , WANG Jiaying , XIN Rui , KE Qiaozhen ,
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JIANG Pengxin , ZHOU Tao , XU Peng 1,3*
(1. Fujian Province Key Laboratory of Marine Biological Genetic Breeding,
College of Oceanic and Earth, Xiamen University, Xiamen 361102, China;
2. School of Aerospace Engineering, Xiamen University, Xiamen 361005, China;
3. State Key Laboratory of Larimichthys crocea Breeding, Ningde 352103, China)
Abstract: Phenotypic traits such as body weight and body length of fish are very important economic traits in
aquaculture and genetic breeding. In order to avoid the uncertainty, error randomness and low efficiency of manual
measurement, this paper develops an automated, non-invasive device based on Mask Region Convolutional Neural
Network (Mask R-CNN) for fish image segmentation and phenotypic traits measurement. The device consists of
two parts: an image acquisition device able to measure fish of different sizes (body length 1-40 cm) and control
software. The control software based on Mask R-CNN can train and predict the target traits of images, and realize
the measurement, storage and management of target data. The experimental results show that the average relative
error in body length and body height of Larimichthys crocea measured by the device is less than 4%. The body
weight was fitted with multiple regression models based on body length, body height and body surface area. The
correlation coefficient between measured values and the real body weight was 0.99, the average relative error was
4%, and the average processing time for each image was 3 seconds, which was 8 times as fast as manual measure-
ment. The data measurement device based on machine vision and image capture developed in this study can auto-
matically, efficiently and accurately obtain morphological and weight data of L. crocea, which provides a more
convenient and efficient phenotype evaluation tool for the evaluation of L. crocea germplasm resources, breeding
of improved varieties and germplasm innovation.
Key words: Larimichthys crocea; image analysis; morphological traits; mass estimation
Corresponding author: XU Peng. E-mail: xupeng77@xmu.edu.cn

Funding projects: National Key Research and Development Program (2022YFD2401001); Special Foundation
for Major Research Program of Fujian Province (2020NZ08003); Seed Industry Innovation and Industrialization
Project of Fujian Province (2021FJSCZY01); National Science Fund for Distinguished Young Scholars
(32225049)

https://www.china-fishery.cn 中国水产学会主办 sponsored by China Society of Fisheries
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