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何志鹏, 巩高瑞, 熊阳, 蒋有渤, 胡景琦, 皇培培, 梅洁. 基于计算机视觉的黄颡鱼表型特征测量和体重预测模型研究[J]. 水生生物学报. DOI: 10.7541/2024.2023.0254
引用本文: 何志鹏, 巩高瑞, 熊阳, 蒋有渤, 胡景琦, 皇培培, 梅洁. 基于计算机视觉的黄颡鱼表型特征测量和体重预测模型研究[J]. 水生生物学报. DOI: 10.7541/2024.2023.0254
HE Zhi-Peng, GONG Gao-Rui, XIONG Yang, JIANG You-Bo, HU Jing-Qi, HUANG Pei-Pei, MEI Jie. A PHENOTYPIC MEASUREMENT AND WEIGHT PREDICTION MODEL OF PELTEOBAGRUS FULVIDRACO BASED ON COMPUTER VISION[J]. ACTA HYDROBIOLOGICA SINICA. DOI: 10.7541/2024.2023.0254
Citation: HE Zhi-Peng, GONG Gao-Rui, XIONG Yang, JIANG You-Bo, HU Jing-Qi, HUANG Pei-Pei, MEI Jie. A PHENOTYPIC MEASUREMENT AND WEIGHT PREDICTION MODEL OF PELTEOBAGRUS FULVIDRACO BASED ON COMPUTER VISION[J]. ACTA HYDROBIOLOGICA SINICA. DOI: 10.7541/2024.2023.0254

基于计算机视觉的黄颡鱼表型特征测量和体重预测模型研究

A PHENOTYPIC MEASUREMENT AND WEIGHT PREDICTION MODEL OF PELTEOBAGRUS FULVIDRACO BASED ON COMPUTER VISION

  • 摘要: 为提高黄颡鱼表型数据的获取效率, 研究开发出一种简易表型获取装置, 通过YOLOv8网络模型快速、高效、精确地测量所采集图像中的黄颡鱼表型特征参数。研究共采集1752张黄颡鱼图像数据, 基于YOLOv8网络对图像集训练和验证后, 完成对584尾黄颡鱼的体面积、头面积、全长、体长、头长、体高、头高、体宽、头宽、腹部面积、胸鳍长度共11种表型特征数据的测量。结果表明, 利用该系统测量长度相关的表型性状的平均相对误差均在1%左右, 在测量面积相关的表型性状的平均相对误差在3%左右, 且所有表型性状的测量时间均在1s之内。进而, 将获得的表型数据与鱼体重进行相关性分析、通径分析、回归分析。结果显示, 头高对黄颡鱼体重的直接作用最大, 其次为头宽、体面积、体长、腹部面积、胸鳍长度, 并以这6个性状建立多元回归模型对体重拟合, 得到最大的相关指数(0.948), 表明这些性状是影响黄颡鱼体重的重要性状。研究提供了一种快速测量黄颡鱼表型数据的技术, 为黄颡鱼的良种创制提供基础。

     

    Abstract: To improve the efficiency of phenotypic data acquisition of yellow catfish (Pelteobagrus fulvidraco Richardson), this study developed a simple phenotype acquisition device, which can quickly, efficiently and accurately measure the phenotypic characteristic parameters of fish in the collected images through the YOLOv8 network. In this study, a total of 1752 image data of yellow catfish were collected. Following the training and validation of the image set using the YOLOv8 network, we completed the measurement of 11 phenotypic characteristics in 584 yellow catfish, including body area, head area, total length, body length, head length, body height, head height, body width, head width, abdominal area, and pectoral fin length. The results showed an average relative error of approximately 1% for length-related phenotypic traits and the average relative error for measuring area-related phenotypic traits was around 3%. Moreover, the time required for measuring all phenotypic traits was within 1s. Furthermore, correlation analysis, path analysis, and regression analysis were performed on the obtained phenotype and body weight data for yellow catfish. The results showed that the head height had the greatest effect on the body weight of yellow catfish, followed by head width, body area, body length, abdominal area, and pectoral fin length. A multiple model was established to fit the weight based on these six traits, and the largest correlation index (0.948) was obtained, indicating that these traits were important traits associated with the body weight of yellow catfish. This study estabolishs a rapid method to measure the phenotypic data and provides a basis for the creation of novel varieties of yellow catfish.

     

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