Construction of Method for Age Identification of Sardinops sagax in the North Pacific Ocean

doi:10.13304/j.nykjdb.2022.0636

Abstract

Abstract:

To make fish age identification more accurate and efficient， based on the biological data such as fork length， weight， and otolith weight measured by 948 samples of Sardinops sagax from 2020 to 2021， and combined with the traditional otolith age wheel observation method， a new method for age identification of Sardinops sagax was constructed. Firstly， the otolith was buried， ground， and photographed. Its accurate age was preliminarily interpreted by the wheel pattern observation method scenario. Three ways were newly built based on the univariate linear regression equation， the multiple linear regression equation， and the deep learning model. The construction and accuracy improvement process of the age identification method of Sardinops sagax were elaborated. By analyzing the R²of the univariate linear regression equation fitted by a single biological feature， the correlation coefficient between the weight of the otolith and the age observed by the rotting method was the highest. According to the age identification of the unary linear regression equation fitted by the weight of the otolith and compared with the age identification results obtained by the multiple regression linear equation and the deep learning model， it could be seen that the identification results of the univariate regression linear equation， the multiple linear regression equation， and the deep learning model were 43.6%， 54.0%， and 71.6% respectively under the standard of no error compared with the wheel pattern observation method. This method would greatly improve the accuracy and efficiency of the age identification of Sardinops sagax， and provide new methods and ideas for fish age identification.

Key words: Sardinops sagaxs, otolith weight, age identification, deep learning

摘要：

为使鱼类年龄鉴定工作更加准确高效，基于2020—2021年的远东拟沙丁鱼（Sardinops sagax）948尾样本测量的叉长、体质量与耳石质量等生物学数据，结合传统耳石年龄轮纹观察法构建远东拟沙丁鱼年龄鉴定新方法。首先将其矢耳石包埋、研磨和拍照，通过轮纹观察法初步判读其准确年龄，之后构建基于一元线性回归方程、多元线性回归方程以及深度学习模型共3种年龄鉴定方法，阐述了远东拟沙丁鱼年龄鉴定方法构建和准确度提升过程。通过分析单个生物学特征所拟合的一元线性回归方程，对比其R²得出，耳石重量与轮纹观察法所得年龄的相关系数最高。据耳石重量拟合出一元线性回归方程进行年龄鉴定，与多元回归线性方程与深度学习模型所得的年龄鉴定结果进行比较可知，一元回归线性方程、多元线性回归方程及深度学习模型的鉴定结果与轮纹观察法相比在无误差的标准下其准确率分别为43.6%、54.0%和71.6%。该方法将极大提高远东拟沙丁鱼年龄鉴定的准确率及效率，为鱼类年龄鉴定提供新的方法与思路。

关键词: 远东拟沙丁鱼, 耳石重量, 年龄鉴定, 深度学习

CLC Number:

S931.1

Chao YANG, Haibin HAN, Bo WEI, Heng ZHANG, Chen SHANG, Bing SU, Siyuan LIU, Peiwei JIANG, Delong XIANG. Construction of Method for Age Identification of Sardinops sagax in the North Pacific Ocean[J]. Journal of Agricultural Science and Technology, 2023, 25(4): 225-233.

杨超, 韩海斌, 韦波, 张衡, 商宸, 苏冰, 刘思源, 蒋沛雯, 相德龙. 北太平洋远东拟沙丁鱼年龄鉴定方法的构建[J]. 中国农业科技导报, 2023, 25(4): 225-233.

Figures/Tables 8

Table 1 Information of fork length，weight and otolish weight of Sardinops sagax

生物学特征 Biological feature

体质量Weight/g

耳石质量Otolish weight/mg

5.6～144.6

0.20～3.35

43.7

1.37

Fig. 1 Otolith image of the Sardinops sagaxA：Untreated otoliths；B：Otolith after embedding and manual grinding；C：Otolith overground after embedding. The picture shows the otoliths of Sardinops sagax with fork length of 165 mm and age of 2+ ； the position indicated by the white arrow is the otoliths growth ring

Fig. 2 Whole framework of self-built neural network

Fig. 3 Age composition of otolith ring observation method

Fig. 4 Box diagram of otolith weight， fork length and weight to age distribution of Sardinops sagax

Fig. 5 Age identified by measured surface observation and linear fitting regression

Fig. 6 Iteration diagram of accuracy、recall rate、loss rate、F1 value and training timesNote： The figure shows the parameters of each index without error.

Table 2 Prediction accuracy results of three methods

方法Method	准确率Accuracy/%
一元拟合方程Unary fitting equation	43.6
多元拟合方程 Multivariate fitting equation	54.0
深度神经网络 Deep neural network	71.6

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