Estimation of Muscle Scores of Live Pigs Using a Kinect Camera

Ahmad Alsahaf*, George Azzopardi, Bart Ducro, Egiel Hanenberg, Roel F. Veerkamp, Nicolai Petkov

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

The muscle grading of livestock is a primary component of valuation in the meat industry. In pigs, the muscularity of a live animal is traditionally estimated by visual and tactile inspection from an experienced assessor. In addition to being a time-consuming process, scoring of this kind suffers from inconsistencies inherent to the subjectivity of human assessment. On the other hand, accurate, computer-driven methods for carcass composition estimation, such as magnetic resonance imaging (MRI) and computed tomography scans (CT-scans), are expensive and cumbersome to both the animals and their handlers. In this paper, we propose a method that is fast, inexpensive, and non-invasive for estimating the muscularity of live pigs, using RGB-D computer vision and machine learning. We used morphological features extracted from the depth images of pigs to train a classifier that estimates the muscle scores that are likely to be given by a human assessor. The depth images were obtained from a Kinect v1 camera which was placed over an aisle through which the pigs passed freely. The data came from 3246 pigs, each having 20 depth images, and a muscle score from 1 to 7 (reduced later to 5 scores) assigned by an experienced assessor. The classification based on morphological features of the pig's body shape-using a gradient boosted classifier-resulted in a mean absolute error of 0.65 in tenfold cross-validation. Notably, the majority of the errors corresponded to pigs being classified as having muscle scores adjacent to the groundtruth labels given by the assessor. According to the end users of this application, the proposed approach could be used to replace expert assessors at the farm.

Original languageEnglish
Article number8698234
Pages (from-to)52238-52245
Number of pages8
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 24 Apr 2019

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Keywords

  • Computer vision
  • machine learning
  • precision farming
  • RGB-D imaging

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