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This article in JAS

  1. Vol. 95 No. 4, p. 1847-1857
     
    Received: Dec 08, 2016
    Accepted: Feb 01, 2017
    Published: April 13, 2017


    2 Corresponding author(s): malcolm.mcphee@dpi.nsw.gov.au
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doi:10.2527/jas.2016.1292

Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging1

  1. M. J. McPhee 2*,
  2. B. J. Walmsley*,
  3. B. Skinner,
  4. B. Littler,
  5. J. P. Siddell§,
  6. L. M. Cafe*,
  7. J. F. Wilkins#,
  8. V. H. Oddy* and
  9. A. Alempijevic
  1. * NSW Department of Primary Industries, Beef Industry Centre, University of New England, Armidale, NSW 2351, Australia
     Centre for Autonomous Systems, University of Technology Sydney, Broadway, N.S.W. 2007, Australia
     NSW Department of Primary Industries, Mudgee, NSW 2850, Australia
    § NSW Department of Primary Industries, Agricultural Research and Advisory Station, Glen Innes, NSW 2370, Australia
    # NSW Department Primary Industries, Agricultural Institute, Private Mail Bag, Wagga Wagga, NSW 2650, Australia

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.

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