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Computer vision-based growth prediction and digestive tract assessment in pacific white shrimp (Litopenaeus vannamei)

文献类型：期刊论文

作者Zhao, Haocheng1,2,3,6; Liu, Mei5; Ren, Ziwen4; Jiang, Keyong1,3,6; Zhao, Xudong5; Xu, Kefeng5; Gao, Yan5; Wang, Baojie1,3,6; Wang, Lei1,2,3,6
刊名AQUACULTURE REPORTS
出版日期2025-12-30
卷号45页码:14
关键词Shrimp culture Growth prediction Digestive tract assessment Computer vision Deep learning
ISSN号2352-5134
DOI10.1016/j.aqrep.2025.103137
通讯作者Wang, Baojie(wangbaojie@qdio.ac.cn) ; Wang, Lei(leiwang@qdio.ac.cn)
英文摘要Litopenaeus vannamei is a key economic species in global aquaculture, and monitoring its growth is critical for optimizing feed management, and reducing costs. Traditional manual sampling methods are labor-intensive, error-prone, and can cause stress or injury to shrimp, negatively impacting growth. This study employed computer vision and machine learning to propose an innovative approach for growth prediction and digestive tract assessment in L. vannamei. An improved annotation method was developed to simplify the marking process and reduce weight prediction errors. Furthermore, A new length measurement approach, termed "visual total length", was introduced to overcome the limitations of traditional measurement techniques. In this study, images of shrimp on feeding trays were analyzed to simulate real aquaculture monitoring conditions, and a prediction system was constructed by combining image segmentation (You Only Look Once v8n-seg, YOLOv8n-seg), classification (YOLOv8n-cls), traditional fitting, and machine learning models (Light Gradient Boosting Machine, LightGBM). The results showed that the improved annotation method also significantly reduced weight prediction errors caused by tail fan area. The visual total length had a high linear correlation with traditional total length (r2 = 0.99), effectively allowing it to replace traditional measurements and enhancing applicability in real production environments. The final model achieved an accuracy of over 97 % in predicting length and weight when compared to manual measurements, and over 87 % accuracy in assessing digestive tract fullness. This study provides an efficient and precise method for growth monitoring, laying a solid foundation in future intelligent shrimp aquaculture.
WOS关键词EVACUATION
资助项目Key R & D Project of Shandong Province[2024TZXD047] ; Shandong Provincial Natural Science Foundation[ZR2023MC096]
WOS研究方向Fisheries
语种英语
WOS记录号WOS:001587414300001
出版者ELSEVIER
源URL[http://ir.qdio.ac.cn/handle/337002/203554]  
专题海洋研究所_实验海洋生物学重点实验室
通讯作者Wang, Baojie; Wang, Lei
作者单位1.Qingdao Marine Sci & Technol Ctr, Lab Marine Biol & Biotechnol, Qingdao 266000, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Oceanol, State Key Lab Breeding Biotechnol & Sustainable Aq, Qingdao 266000, Peoples R China
4.Shandong Yellow River Delta Marine Technol Co Ltd, Dongying, Peoples R China
5.Marine Sci Res Inst Shandong Prov, Municipal Engn Res Ctr Aquat Biol Qual Evaluat & A, Qingdao, Peoples R China
6.Chinese Acad Sci, Inst Oceanol, Lab Expt Marine Biol, Qingdao 266000, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Haocheng,Liu, Mei,Ren, Ziwen,et al. Computer vision-based growth prediction and digestive tract assessment in pacific white shrimp (Litopenaeus vannamei)[J]. AQUACULTURE REPORTS,2025,45:14.
APA Zhao, Haocheng.,Liu, Mei.,Ren, Ziwen.,Jiang, Keyong.,Zhao, Xudong.,...&Wang, Lei.(2025).Computer vision-based growth prediction and digestive tract assessment in pacific white shrimp (Litopenaeus vannamei).AQUACULTURE REPORTS,45,14.
MLA Zhao, Haocheng,et al."Computer vision-based growth prediction and digestive tract assessment in pacific white shrimp (Litopenaeus vannamei)".AQUACULTURE REPORTS 45(2025):14.

入库方式： OAI收割

来源：海洋研究所

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