alexa Potential for estimation of body condition scores in dairy cattle from digital images.
Veterinary Sciences

Veterinary Sciences

Journal of Veterinary Science & Technology

Author(s): Bewley JM, Peacock AM, Lewis O, Boyce RE, Roberts DJ,

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Abstract Body condition scoring, an indirect measure of the level of subcutaneous fat in dairy cattle, has been widely adopted for research and field assessment or for management purposes on farms. The feasibility of utilizing digital images to determine body condition score (BCS) was assessed for lactating dairy cows at the Scottish Agricultural College Crichton Royal Farm. Two measures of BCS were obtained by using the primary systems utilized in the United Kingdom (UK-BCS) and the United States (USBCS). Means were 2.12 (+/-0.35) and 2.89 (+/-0.40), modes were 2.25 and 2.75, and ranges were 1.0 to 3.5 and 1.5 to 4.5 for the UKBCS (n = 2,346) and USBCS (n = 2,571), respectively. Up to 23 anatomical points were manually identified on images captured automatically as cows passed through a weigh station. Points around the hooks were easier to identify on images than points around pins and the tailhead. All identifiable points were used to define and formulate measures describing the cow's contour. For both BCS systems, hook angle, posterior hook angle, and tailhead depression were significant predictors of BCS. When the full data set testing only the angles around the hooks was used, 100\% of predicted BCS were within 0.50 points of actual USBCS and 92.79\% were within 0.25 points; and 99.87\% of predicted BCS were within 0.50 points of actual UKBCS and 89.95\% were within 0.25 points. In a reduced data set considering only observations in which the tailhead depression angle was available, adding the tailhead depression to models did not improve model predictions. The relationships of the calculated angles with USBCS were stronger than those with UKBCS. This research demonstrates the potential for using digital images for assessing BCS. Future efforts should explore ways to automate this process by using a larger number of animals to predict scores accurately for cows across all levels of body condition. This article was published in J Dairy Sci and referenced in Journal of Veterinary Science & Technology

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