The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle

Abstract

The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle AbstractThe objective of this study was to predict body weight (BW) of pre-slaughtering beef cattle using digital image analysis. Data used in this study were collected from slaughterhouses in Isparta and nearby provinces from 140 animals. Selected body measurements such as body weight (BW), wither height (WH), body length (BL), chest depth (CD), hip width (HW), hip height (HH) and body area (BA) of different breeds of beef cattle were combined and compared by digital image analysis. The body area was included as a different parameter for prediction of BW instead of chest girth. However, regression equation that included only body area gave the lowest R 2 value for Holstein (18.0%), but the R 2 value was 43.2 and 51.7% for Brown Swiss and crossbred animals, respectively. The regression equation which included all body traits resulted in R 2 values 35.3, 85.1, and 79.6% for Holstein, Brown Swiss and crossbred, respectively. The regression equation which included body area and body length showed that prediction ability of digital image analysis was high for prediction of BW in Brown Swiss and crossbred animals compared to Holsteins (R 2 82.6, 76.5, and 29.5%, respectively). Results indicated that the prediction ability of digital image analysis was low for prediction of BW. Although possibility of using body area as a parameter in predicting BW is low it can be developed by further and better designed experiments.

A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three-dimensional camera system

“…A sample size of 30% was considered sufficient to estimate the mean BCS of a group of cows. Ozkaya and Bozkurt (2008) found predictive ability of digital image analysis for estimating BW was poor when the animal has a large frame, as is the case for Holstein cows. To overcome these drawbacks of using a standard camera for assessing body shape, researchers have examined the use of other imaging system such as ultrasound (Domecq et al, 1995;Schröder and Staufenbiel, 2006) and thermal cameras (Halachmi et al, 2008).…”

The accuracy of prediction of body weight from body measurements in beef cattle

“…The parameters such as body weight, chest girth, wither height, body length, chest depth, hip width and hip height were measured using measuring stick and tape (Hauptner, Germany) when animals were standing as described in OZKAYA and BOZKURT (2008).…”

Monitoring liveweight in sheep is a valuable management strategy: a review of available technologies

“…So as to be suitable for the liveweight estimation in sheep, however, images will need to have clearly defined boundaries that enable the measurement of certain truss points, being an optimistic objective, given that colour uniformity and wool cover will most likely distort such images (Burke et al 2004). Furthermore, experience from the cattle industry suggests that animals will need to be tightly restrained at a known distance for optimal images to be recorded (Ozkaya and Bozkurt 2008). Given these challenges and the impracticality of regimented restraint of animals for imaging, it is unlikely that the technology will be used by livestock managers to produce liveweight estimates of sheep.…”

“…Measurements of the 3D outline, combined with knowledge of the density of the object, enable the mass of the object to be calculated. This method has been used to effectively estimate the liveweight of salmon (Beddow et al 1996;Lines et al 2001), pigs (Schofield 1990;Schofield et al 1999) and cattle (Ozkaya and Bozkurt 2008), and may have potential for estimating the liveweight of sheep in a paddock (Burke et al 2004). So as to be suitable for the liveweight estimation in sheep, however, images will need to have clearly defined boundaries that enable the measurement of certain truss points, being an optimistic objective, given that colour uniformity and wool cover will most likely distort such images (Burke et al 2004).…”