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基于图像的猪足部和腿部形态性状估计的遗传参数。

Genetic parameters for image-based estimations of swine feet and leg conformation traits.

作者信息

Peppmeier Zack C, Huang Yijian, Bartholomew Jan-Marie B, Jiang Jicai, Knauer Mark T, Leonard Suzanne M

机构信息

Department of Animal Science, North Carolina State University, Raleigh, NC 27607, USA.

Smithfield Premium Genetics, Roanoke Rapids, NC 27870, USA.

出版信息

J Anim Sci. 2025 Jan 4;103. doi: 10.1093/jas/skaf103.

Abstract

The objectives of this study were to develop and evaluate a novel algorithm for image extraction of structural conformation traits and estimate variance components among skeletal conformation, growth, and herd retention traits. An Intel RealSense D435i camera was used to obtain left-side-view RGB images on individual purebred Duroc pigs (n = 846) at 156 d of age. Frames were selected by a trained swine evaluator when either the left front leg (n = 1056), left back leg (n = 888), or both left legs (n = 728) were present in the field of view and the respective foot pads from toe to heel were in contact with the ground. Selected images were processed through Apple Inc's image segmentation algorithm to extract the pig from the background. Segmented pig images were then processed through a novel algorithm developed in this study. The algorithm identified the leg and estimated 21 skeletal conformation traits from each leg. Steps for user intervention were added to assist the algorithm in identifying which leg(s) were present and the general location of each leg to increase the accuracy of leg identification and trait acquisition. The algorithm correctly identified at least one front and one back leg from an image for 99.9% and 98.0% of the pigs, respectively. Heritability estimates ranged from 0.01 to 0.33 for all conformation traits with the quadratic term for the curvature of the anterior side of the front and the height of the back leg having the highest heritability for each location (h2 = 0.33 and 0.30, respectively). Genetic correlations among image feet and leg conformation traits and production traits (finishing average daily gain, weight per day of age, and finishing feed efficiency) ranged from -0.37 to 0.19. Boars that remained in the breeding herd for longer than 200 d tended (P = 0.08) to have greater curvature of the front leg and lower (P = 0.07) angularity between the midpoint of the foot and the anterior point of the pastern and had significantly (P = 0.03) shorter distance between the pastern and the top of the shoulder than those that were removed prior to 200 d. Gilts that remained in the breeding herd for longer than 200 d tended (P = 0.08) to have less curvature of the back leg. The current study presents an algorithm that extracts novel, objective structural conformation traits and reports corresponding genetic and phenotypic parameters.

摘要

本研究的目的是开发和评估一种用于结构构象性状图像提取的新算法,并估计骨骼构象、生长和群体保留性状之间的方差分量。使用英特尔实感D435i相机在156日龄时获取个体纯种杜洛克猪(n = 846)的左侧视图RGB图像。当左前腿(n = 1056)、左后腿(n = 888)或两条左腿(n = 728)出现在视野中且相应的脚垫从脚趾到脚跟与地面接触时,由训练有素的猪评估员选择帧。所选图像通过苹果公司的图像分割算法进行处理,以从背景中提取猪。然后,对分割后的猪图像通过本研究开发的一种新算法进行处理。该算法识别腿部并从每条腿估计21个骨骼构象性状。添加了用户干预步骤,以帮助算法识别存在哪些腿以及每条腿的大致位置,从而提高腿部识别和性状获取的准确性。该算法分别从99.9%和98.0%的猪的图像中正确识别出至少一条前腿和一条后腿。所有构象性状的遗传力估计值范围为0.01至0.33,前腿前侧曲率和后腿高度的二次项在每个位置具有最高的遗传力(分别为h2 = 0.33和0.30)。图像足部和腿部构象性状与生产性状(育肥平均日增重、日龄体重和育肥饲料效率)之间的遗传相关性范围为-0.37至0.19。在繁殖群体中停留超过200天的公猪,其前腿曲率往往更大(P = 0.08),足部中点与系关节前点之间的角度更小(P = 0.07),并且系关节与肩部顶部之间的距离明显更短(P = 0.03),而在200天之前被淘汰的公猪则不然。在繁殖群体中停留超过200天的后备母猪,其后腿曲率往往更小(P = 0.08)。本研究提出了一种算法,该算法可提取新的、客观的结构构象性状,并报告相应的遗传和表型参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee01/12010699/6ba2a50db1de/skaf103_fig1.jpg

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