In horse racing the most acceptable way to objectively evaluate adaptation to increased exertion is to measure lactate blood concentration. However, this may be stressful for the horse, therefore, a simple, noninvasive procedure to monitor race progress is desirable. Forty Thoroughbreds attended race training, with blood samples collected at rest, immediately after, and 30 min after exercise. The lactate concentration was determined 60 s after blood collection using an Accusport®. Thermal imaging of the neck and trunk areas was performed following international veterinary standards from a distance of approximately 2 m from the horse using the same protocol as the blood sampling. The Spearman rank correlation coefficients (ρ) between the changes in the blood lactate concentration and surface temperature measures were found for the regions of interest. The highest positive correlation coefficients were found in the musculus trapezius pars thoracica region for the maximal temperature (T Max; ρ = 0.83; p < 0.0001), the minimal temperature (T Min; ρ = 0.83; p < 0.0001), and the average temperature (T Aver; ρ = 0.85; p < 0.0001) 30 min after the exercise. The results showed that infrared thermography may supplement blood measurements to evaluate adaptation to increased workload during race training, however, more research and references values are needed.

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Background The horses’ backs are particularly exposed to overload and injuries due to direct contact with the saddle and the influence of e.g. the rider’s body weight. The maximal load for a horse’s back during riding has been suggested not to exceed 20% of the horses’ body weight. The common prevalence of back problems in riding horses prompted the popularization of thermography of the thoracolumbar region. However, the analysis methods of thermographic images used so far do not distinguish loaded horses with body weight varying between 10 and 20%. Results The superficial body temperature (SBT) of the thoracolumbar region of the horse’s back was imaged using a non-contact thermographic camera before and after riding under riders with LBW (low body weight, 10%) and HBW (high body weight, 15%). Images were analyzed using six methods: five recent SBT analyses and the novel approach based on Gray Level Co-Occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM). Temperatures of the horse’s thoracolumbar region were higher (p < 0.0001) after then before the training, and did not differ depending on the rider’s body weight (p > 0.05), regardless of used SBT analysis method. Effort-dependent differences (p < 0.05) were noted for six features of GLCM and GLRLM analysis. The values of selected GLCM and GLRLM features also differed (p < 0.05) between the LBW and HBW groups. Conclusion The GLCM and GLRLM analyses allowed the differentiation of horses subjected to a load of 10 and 15% of their body weights while horseback riding in contrast to the previously used SBT analysis methods. Both types of analyzing methods allow to differentiation thermal images obtained before and after riding. The textural analysis, including selected features of GLCM or GLRLM, seems to be promising tools in considering the quantitative assessment of thermographic images of horses’ thoracolumbar region.

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.

Infrared thermography is a non‐invasive technique which allows to distinguish between pregnant and non‐pregnant animals. Detecting accurate body surface temperatures can be challenging due to external factors altering thermograph measurements. This study aimed to determine the associations between the ambient temperature, the hair coat features and the temperatures of mares' abdomens. It compared pregnant and non‐pregnant mares throughout 11 months. The research was carried out on 40 Konik Polski mares, which were divided into pregnant and non‐pregnant groups. The temperature (Tmax, maximal; Taver, average; Tmin, minimal) of the mares' abdomen was evaluated in two regions of interest: the whole area of the lateral surface of the mares' abdomen (Px1) and the flank area of the lateral surface of mares' abdomen (Px2). During the increasing period, the slopes in the linear regression equation did not differ significantly for ambient (Tamb) and surface temperatures in both groups. In the decreasing period, the slopes did not differ significantly for Tamb and Tmax in the non‐pregnant group. They also did not differ for Tamb and Taver in Px1 and Tamb and Tmin in Px1 in both pregnant and non‐pregnant groups respectively. Other slopes varied significantly (p < .001). There was no evidence of parallel changes in hair coat features and measured temperatures. The flank area appears more suitable for thermal imaging in pregnant mares due to the seasonal fluctuations in hair coat lengths.