| 基于计算机视觉的大黄鱼体尺、体重性状表型测量装置开发和应用 |
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| 引用本文： | 王禹莎,王家迎,辛瑞,柯巧珍,江鹏鑫,周涛,徐鹏.基于计算机视觉的大黄鱼体尺、体重性状表型测量装置开发和应用[J].水产学报,2023,47(1):019516-019516. |
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| 作者姓名： | 王禹莎 王家迎 辛瑞 柯巧珍 江鹏鑫 周涛 徐鹏 |
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| 作者单位： | 厦门大学海洋与地球学院，福建省海洋生物遗传育种重点实验室，福建 厦门 361102;厦门大学航空航天学院，福建 厦门 361005;厦门大学海洋与地球学院，福建省海洋生物遗传育种重点实验室，福建 厦门 361102;大黄鱼育种国家重点实验室，福建 宁德 352103 |
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| 基金项目： | 国家重点研发计划(2022YFD2401001)；福建省科技重大专项(2020NZ08003)；福建省种业创新与产业化项目(2021FJSCZY01)；国家杰出青年科学基金(32225049) |
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| 摘 要： | 鱼类的体重、体长等表型性状是水产养殖和遗传育种中非常重要的经济性状，为了避免人工测量的不确定性、误差随机性和效率低下的问题，本研究开发出一种基于Mask Region Convolutional Neural Network (Mask R-CNN) 的自动化、无侵入式鱼类图像分割和表型性状测量的装置。该装置包括图像采集装置和控制软件两部分，其中图像采集装置可以测量不同规格鱼类 (体长1~40 cm)。基于Mask R-CNN的控制软件，可以对图片进行目标性状的训练和预测，实现目标数据的测量、存储和管理。本研究利用该装置对477尾3月龄大黄鱼进行了图像采集和基于大黄鱼图像的体长、体高、体重性状预测。研究表明，利用该装置测量的大黄鱼体长和体高的平均相对误差均小于4%。基于体长、体高、体表面积的多元回归模型对体重进行拟合，测量值与真实体重的相关系数为0.99，平均相对误差为4%，对每张图片的平均处理时间为3 s，测量速率是人工的8倍。该系统可以实现自动化、高效、准确地获取大黄鱼体型与体重性状，为大黄鱼种质资源评价、良种选育和种质创新提供更加便捷高效的表型测评工具。
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| 关 键 词： | 大黄鱼 图像分析 体尺性状 体重估测 |
| 收稿时间： | 2022/8/23 0:00:00 |
| 修稿时间： | 2022/10/18 0:00:00 |
Application of computer vision in morphological and body weight measurements of large yellow croaker (Larimichthys crocea) |
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| WANG Yush,WANG Jiaying,XIN Rui,KE Qiaozhen,JIANG Pengxin,ZHOU Tao,XU Peng.Application of computer vision in morphological and body weight measurements of large yellow croaker (Larimichthys crocea)[J].Journal of Fisheries of China,2023,47(1):019516-019516. |
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| Authors: | WANG Yush WANG Jiaying XIN Rui KE Qiaozhen JIANG Pengxin ZHOU Tao XU Peng |
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| Affiliation: | Fujian Province Key Laboratory of Marine Biological Genetic Breeding, College of Oceanic and Earth, Xiamen University, Xiamen 361102, China;School of Aerospace Engineering, Xiamen University, Xiamen 361005, China; Fujian Province Key Laboratory of Marine Biological Genetic Breeding, College of Oceanic and Earth, Xiamen University, Xiamen 361102, China;State Key Laboratory of Larimichthys crocea Breeding, Ningde 352103, China |
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| Abstract: | Phenotypic traits such as body weight and body length of fish are very important economic traits in aquaculture and genetic breeding. In order to avoid the uncertainty, error randomness and low efficiency of manual measurement, this paper develops an automated, non-invasive device based on Mask Region Convolutional Neural Network (Mask R-CNN) for fish image segmentation and phenotypic traits measurement. The device consists of two parts: an image acquisition device able to measure fish of different sizes (body length 1-40 cm) and control software. The control software based on Mask R-CNN can train and predict the target traits of images, and realize the measurement, storage and management of target data. The experimental results show that the average relative error in body length and body height of Larimichthys crocea measured by the device is less than 4%. The body weight was fitted with multiple regression models based on body length, body height and body surface area. The correlation coefficient between measured values and the real body weight was 0.99, the average relative error was 4%, and the average processing time for each image was 3 seconds, which was 8 times as fast as manual measurement. The data measurement device based on machine vision and image capture developed in this study can automatically, efficiently and accurately obtain morphological and weight data of L. crocea, which provides a more convenient and efficient phenotype evaluation tool for the evaluation of L. crocea germplasm resources, breeding of improved varieties and germplasm innovation. |
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| Keywords: | Larimichthys crocea image analysis morphological traits mass estimation |
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