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      Body measurement of riding horses with a versatile tablet-type 3D scanning device

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          ABSTRACT

          The measurement of various body dimensions of horses plays a significant role in quality improvement, genetic breeding, health, and soundness. There has been significant advancement in the technology for acquiring stereoscopic images with a three-dimensional (3D) scanner. This study aimed to validate the accuracy of body measurements obtained from stereoscopic images taken with a 3D scanner. We manually took the following body measurements for 8 riding horses: height at the withers, height at the back, height at the croup, chest depth, width of the chest, width of the croup, width of the waist, girth circumference, cannon circumference, and body length. Using a versatile tablet-type 3D scanning device, we captured a 3D image of each horse. Relative errors varied from −1.37% to 6.25%. The correlation coefficient between manual and 3D measurements was significant for all body measurements (P<0.01) except for width of the waist and cannon circumference. The low accuracy of cannon circumference (r=0.248) was due to effect of hair. A simple regression analysis of all body measurements revealed a strong correlation (P<0.001, R 2=0.9994, root-mean-square error [RMSE]=1.522). Notable advantages of this methodology include high accuracy, good operability, non-contact, high versatility, and low cost. Further studies are required for the establishment of an accurate measurement methodology that can scan the whole body in a shorter time.

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          STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

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            A Comparison of Weight Estimation Methods in Adult Horses

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              Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows.

              Body condition is an indirect estimation of the level of body reserves, and its variation reflects cumulative variation in energy balance. It interacts with reproductive and health performance, which are important to consider in dairy production but not easy to monitor. The commonly used body condition score (BCS) is time consuming, subjective, and not very sensitive. The aim was therefore to develop and validate a method assessing BCS with 3-dimensional (3D) surfaces of the cow's rear. A camera captured 3D shapes 2 m from the floor in a weigh station at the milking parlor exit. The BCS was scored by 3 experts on the same day as 3D imaging. Four anatomical landmarks had to be identified manually on each 3D surface to define a space centered on the cow's rear. A set of 57 3D surfaces from 56 Holstein dairy cows was selected to cover a large BCS range (from 0.5 to 4.75 on a 0 to 5 scale) to calibrate 3D surfaces on BCS. After performing a principal component analysis on this data set, multiple linear regression was fitted on the coordinates of these surfaces in the principal components' space to assess BCS. The validation was performed on 2 external data sets: one with cows used for calibration, but at a different lactation stage, and one with cows not used for calibration. Additionally, 6 cows were scanned once and their surfaces processed 8 times each for repeatability and then these cows were scanned 8 times each the same day for reproducibility. The selected model showed perfect calibration and a good but weaker validation (root mean square error=0.31 for the data set with cows used for calibration; 0.32 for the data set with cows not used for calibration). Assessing BCS with 3D surfaces was 3 times more repeatable (standard error=0.075 versus 0.210 for BCS) and 2.8 times more reproducible than manually scored BCS (standard error=0.103 versus 0.280 for BCS). The prediction error was similar for both validation data sets, indicating that the method is not less efficient for cows not used for calibration. The major part of reproducibility error incorporates repeatability error. An automation of the anatomical landmarks identification is required, first to allow broadband measures of body condition and second to improve repeatability and consequently reproducibility. Assessing BCS using 3D imaging coupled with principal component analysis appears to be a very promising means of improving precision and feasibility of this trait measurement.
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                Author and article information

                Journal
                J Equine Sci
                J Equine Sci
                JES
                Journal of Equine Science
                The Japanese Society of Equine Science
                1340-3516
                1347-7501
                06 September 2021
                2021
                : 32
                : 3
                : 73-80
                Affiliations
                [1 ] Department of Animal Science, School of Veterinary Medicine, Kitasato University, Aomori 034-8628, Japan
                [2 ] National Institute of Animal Health (NIAH), National Agriculture and Food Research Organization (NARO), Hokkaido 062-0045, Japan
                [3 ] Department of Sustainable Agriculture, College of Agriculture, Food and Environment Sciences, Rakuno Gakuen University, Hokkaido 069-8501, Japan
                [4 ] Present address: Department of Sustainable Agriculture, College of Agriculture, Food and Environment Sciences, Rakuno Gakuen University, Hokkaido 069-8501, Japan
                Author notes
                *Corresponding author. e-mail: matsuura@ 123456vmas.kitasato-u.ac.jp
                Article
                2033
                10.1294/jes.32.73
                8437753
                34539208
                39ab1947-4b2a-4fa3-8681-8c09b28c8a94
                ©2021 The Japanese Society of Equine Science

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. (CC-BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/ )

                History
                : 30 October 2020
                : 07 June 2021
                Categories
                Full Paper

                conformation,horse,light detection and ranging (lidar),non-contact,3d images

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