Deep learning to obtain high-throughput morphological phenotypes and its genetic correlation with swimming performance in juvenile large yellow croaker

Zeng, Junjia; Feng, Miaosheng; Deng, Yacheng; Jiang, Pengxin; Bai, Yinlin; Wang, Jiaying; Qu, Ang; Liu, Wei; Jiang, Zhou; He, Qian; Wang, Zhijun; Xu, Peng

doi:10.1016/j.aquaculture.2023.740051

Cited by 2 publications

(1 citation statement)

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“…Additionally, the model exhibits a notable recognition speed of up to 32 frames per second for fish targets and key points. Zeng et al [28] proposed the HRNet key-point detection model tailored to the morphological features of juvenile yellowtail, achieving a prediction accuracy exceeding 96%. This model demonstrates versatility in detecting fishes of various sizes and complex morphological traits.…”

Section: Discussionmentioning

confidence: 99%

Research on Measuring the Bodies of Underwater Fish with Inclined Positions Using the YOLOv8 Model and a Line-Laser System

Li,

Zhang,

et al. 2024

Fishes

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Fish body measurement is essential for monitoring fish farming and evaluating growth. Non-destructive underwater measurements play a significant role in aquaculture management. This study involved annotating images of fish in aquaculture settings and utilized a line laser for underwater distance calibration and fish body inclined-angle calculation. The YOLOv8 model was employed for fish identification and key-point detection, enabling the determination of actual body dimensions through a mathematical model. The results show a root-mean-square error of 6.8 pixels for underwater distance calibration using the line laser. The pre-training YOLOv8-n, with its lower parameter counts and higher MAP values, proved more effective for fish identification and key-point detection, considering speed and accuracy. Average body length measurements within 1.5 m of the camera showed a minor deviation of 2.46% compared to manual measurements. The average relative errors for body length and width were 2.46% and 5.11%, respectively, with corresponding average absolute errors. This study introduces innovative techniques for fish body measurement in aquaculture, promoting the digitization and informatization of aquaculture processes.

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Section: Discussionmentioning

confidence: 99%