The influence of different examiners on the Body Condition Score (BCS) in South American camelids-Experiences from a mixed llama and alpaca herd.

Particularly in unshorn llamas and alpacas with a dense fiber coat, changes in body condition often remain undetected for a long time. Manual palpation of the lumbar vertebrae is hence a simple and practical method for the objective assessment of body condition in South American camelids (SAC). Depending on tissue coverage, a body condition score (BCS) of 1 (emaciated) to 5 (obese) with an optimum of 3 is assigned. To date, there is a lack of detailed information on the comparability of the results when the BCS in llamas or alpacas is assessed by different examiners. Reliability of BCS assessment of 20 llamas and nine alpacas during a veterinary herd visit by six examiners was hence evaluated in this study. A gold standard BCS (gsBCS) was calculated from the results of the two most experienced examiners. The other examiners deviated by a maximum of 0.5 score points from the gsBCS in more than 80% of the animals. Inter-rater reliability statistics between the assessors were comparable to those in body condition scoring in sheep and cattle (r = 0.52-0.89; τ = 0.43-0.80; κw = 0.50-0.79). Agreements were higher among the more experienced assessors. Based on the results, the assessment of BCS in SAC by palpation of the lumbar vertebrae can be considered as a simple and reproducible method to reliably determine nutritional status in llamas and alpacas.

The Effect of Rider:Horse Bodyweight Ratio on the Superficial Body Temperature of Horse's Thoracolumbar Region Evaluated by Advanced Thermal Image Processing.

Appropriate matching of rider-horse sizes is becoming an increasingly important issue of riding horses' care, as the human population becomes heavier. Recently, infrared thermography (IRT) was considered to be effective in differing the effect of 10.6% and 21.3% of the rider:horse bodyweight ratio, but not 10.1% and 15.3%. As IRT images contain many pixels reflecting the complexity of the body's surface, the pixel relations were assessed by image texture analysis using histogram statistics (HS), gray-level run-length matrix (GLRLM), and gray level co-occurrence matrix (GLCM) approaches. The study aimed to determine differences in texture features of thermal images under the impact of 10-12%, >12 ≤15%, >15 <18% rider:horse bodyweight ratios, respectively. Twelve horses were ridden by each of six riders assigned to light (L), moderate (M), and heavy (H) groups. Thermal images were taken pre- and post-standard exercise and underwent conventional and texture analysis. Texture analysis required image decomposition into red, green, and blue components. Among 372 returned features, 95 HS features, 48 GLRLM features, and 96 GLCH features differed dependent on exercise; whereas 29 HS features, 16 GLRLM features, and 30 GLCH features differed dependent on bodyweight ratio. Contrary to conventional thermal features, the texture heterogeneity measures, InvDefMom, SumEntrp, Entropy, DifVarnc, and DifEntrp, expressed consistent measurable differences when the red component was considered.

Selection of Image Texture Analysis and Color Model in the Advanced Image Processing of Thermal Images of Horses following Exercise.

As the detection of horse state after exercise is constantly developing, a link between blood biomarkers and infrared thermography (IRT) was investigated using advanced image texture analysis. The aim of the study was to determine which combinations of RGB (red-green-blue), YUI (brightness-UV-components), YIQ (brightness-IQ-components), and HSB (hue-saturation-brightness) color models, components, and texture features are related to the blood biomarkers of exercise effect. Twelve Polish warmblood horses underwent standardized exercise tests for six consecutive days. Both thermal images and blood samples were collected before and after each test. All 144 obtained IRT images were analyzed independently for 12 color components in four color models using eight texture-feature approaches, including 88 features. The similarity between blood biomarker levels and texture features was determined using linear regression models. In the horses' thoracolumbar region, 12 texture features (nine in RGB, one in YIQ, and two in HSB) were related to blood biomarkers. Variance, sum of squares, and sum of variance in the RGB were highly repeatable between image processing protocols. The combination of two approaches of image texture (histogram statistics and gray-level co-occurrence matrix) and two color models (RGB, YIQ), should be considered in the application of digital image processing of equine IRT.