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|Title:||Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging||Contributor(s):||McPhee, Malcolm J (author); Walmsley, Bradley J (author) ; Skinner, B (author); Littler, B (author); Siddell, J (author); Cafe, L (author); Wilkins, J F (author); Oddy, Hutton (author) ; Alempijevic, A (author)||Publication Date:||2017||DOI:||10.2527/jas.2016.1292||Handle Link:||https://hdl.handle.net/1959.11/23209||Abstract:||The objective of this study was to develop a proof of concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras to objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows and steers. Data from low and high muscled cattle (156 cows and 79 steers) were collected at multiple locations and time points. The following steps were required for the 3-dimensional (3D) image data and subsequent machine learning techniques to learn the traits: 1) reduce the high dimensionality of the point cloud data by extracting features from the input signals to produce a compact and representative feature vector, 2) perform global optimization of the signatures using machine learning algorithms and a parallel genetic algorithm, and 3) train a sensor model using regression-supervised learning techniques on the ultrasound P8 fat and the classified learning techniques for the assessed MS for each animal in the data set. The correlation of estimating hip height (cm) between visually measured and assessed 3D data from RGB-D cameras on cows and steers was 0.75 and 0.90, respectively. The supervised machine learning and global optimization approach correctly classified MS (mean [SD]) 80 (4.7) and 83% [6.6%] for cows and steers, respectively. Kappa tests of MS were 0.74 and 0.79 in cows and steers, respectively, indicating substantial agreement between visual assessment and the learning approaches of RGB-D camera images. A stratified 10-fold cross-validation for P8 fat did not find any differences in the mean bias (P = 0.62 and P = 0.42 for cows and steers, respectively). The root mean square error of P8 fat was 1.54 and 1.00 mm for cows and steers, respectively. Additional data is required to strengthen the capacity of machine learning to estimate measured P8 fat and assessed MS. Data sets for Bos indicus and continental cattle are also required to broaden the use of 3D cameras to assess cattle. The results demonstrate the importance of capturing curvature as a form of representing body shape. A data-driven model from shape to trait has established a proof of concept using optimized machine learning techniques to assess P8 fat and MS in Angus cows and steers.||Publication Type:||Journal Article||Source of Publication:||Journal of Animal Science, 95(4), p. 1847-1857||Publisher:||Oxford University Press||Place of Publication:||United States of America||ISSN:||1525-3163
|Fields of Research (FoR) 2008:||070201 Animal Breeding||Fields of Research (FoR) 2020:||300305 Animal reproduction and breeding||Socio-Economic Objective (SEO) 2008:||830301 Beef Cattle||Socio-Economic Objective (SEO) 2020:||100401 Beef cattle||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal|
|Appears in Collections:||Animal Genetics and Breeding Unit (AGBU)|
School of Environmental and Rural Science
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