Page 211 - 《水产学报》2025年第5期

张士薇，等 水产学报, 2025, 49(5): 059117

Development and application of a deep learning algorithm-based growth
phenotypes measurement system of the Pacific white shrimp
(Litopenaeus vannamei)

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ZHANG Shiwei 1,2,3 , DAI Ping , GAO Guangchun , MENG Xianhong , LUO Kun ,
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SUI Juan , TAN Jian , FU Qiang , CAO Jiawang , CHEN Baolong , LI Xupeng ,
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QIANG Guangfeng , XING Qun , QI Yunhui , KONG Jie , LUAN Sheng 1,2*
1. State Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute,
Chinese Academy of Fishery Sciences, Qingdao 266071, China;
2. Laboratory for Marine Fisheries Science and Food Production Processes,
Qingdao Marine Science and Technology Center, Qingdao 266237, China;
3. College of Fisheries and Life Sciences, Dalian Ocean University, Dalian 116023, China;
4. Hangzhou Feirui Technology Co., Ltd., Hangzhou 311100, China;
5. School of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, China;
6. BLUP aquabreed Co., Ltd., Weifang 261312, China
Abstract: To address the low efficiency and high error rates associated with manual measurement of growth phenotypes in the
Pacific white shrimp (Litopenaeus vannamei), this study developed a dedicated image acquisition box capable of capturing
standardized, high-quality side-view images of the shrimp. Utilizing this system, a High-Resolution Network (HRNet) model
was employed to identify nine key feature points of the shrimp, enabling the measurement of traits such as body length. Addi-
tionally, a Mask Region Convolutional Neural Network (Mask R-CNN) model was utilized for shrimp contour segmentation to
calculate body surface area. Regression models incorporating body length and body surface area were subsequently developed
to predict body weight. An integrated image processing and data management software was also developed to establish a pre-
cise measurement system for the growth phenotypes of L. vannamei. The study found that the HRNet model achieved recogni-
tion rates exceeding 98% for all nine feature points, with rates exceeding 99% for seven points. The true values of body length
and abdominal segment length were measured using two methods: manual measurement with a ruler and measurement from
manually tagged feature points in the images. The predictive accuracy of body length and abdominal segment length was calcu-
lated to be 0.91-0.97 and 0.91-0.93, respectively, with average relative errors of 1.39%-4.63% and 2.46%-4.59%. Evaluation
against manually segmented shrimp body contours showed that the Mask R-CNN model predicted body surface area with an
accuracy of 0.98 and an average relative error of 1.73%. Regression models incorporating variables such as body length, body
surface area, and gender were developed to predict body weight, achieving accuracies above 0.94, with the model incorporating
both body length and body surface area achieving the highest prediction accuracy (0.97). These results demonstrate that com-
puter vision technology combined with deep learning algorithms can accurately measure growth phenotypes, such as body
length and body surface area, and predict body weight L. vannamei. This study provides an efficient tool for the accurate and
rapid measurement of growth phenotypes in L. vannamei.
Key words: Litopenaeus vannamei; growth phenotypes; deep learning; computer vision; measurement system

Corresponding authors: KONG Jie. E-mail: kongjie@ysfri.ac.cn;
LUAN Sheng. E-mail: luansheng@ysfri.ac.cn
Funding projects: National Key Research and Development Program of China (2022YFD2400202); China Agriculture
Research System of MOF and MARA (CARS-48); Central Public-interest Scientific Institution Basal Research Fund, CAFS
(2020TD26); Taishan Scholars Program; Open Competition Program of Top Ten Critical Priorities of Agricultural Science and
Technology Innovation for the 14th Five-Year Plan of Guangdong Province (2022SDZG01); Shandong Province Science and
Technology-oriented Small and Medium-sized Enterprise Innovation Capacity Enhancement Project (2023TSGC0744)

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