Assessment of Thermal Imaging to Objectively Body Condition Score Mature Horses and Multiparous Gestating Beef Cows

Lay Summary This study explored the efficacy of using thermal imaging of external body surface temperature as a tool to estimate body condition in horses and pregnant beef cows. Body condition scoring (BCS) is a widely used method of visually assessing an animal’s nutritional status, but scoring relies heavily on human interpretation. The purpose of this study was to determine if body surface temperature obtained from thermal cameras could provide an objective way to estimate BCS. Horses and cows had thermal images taken while concurrently assigning BCS to seven or five specific body regions, respectively, with additional factors like breed, weather, and housing conditions recorded as covariates. Considerably between-scorer variation existed in assigning BCS for horses and cattle. Machine learning models were created for each body region to estimate BCS from body surface temperatures. Models were able to predict BCS within 0.5 units of the labeled BCS 80 to 90.3% of the time in the equine dataset and 66.7 to 82.5% of the time in the beef cow dataset. Overall, thermal imaging for surface temperature shows promise as an objective tool to monitor body condition in livestock, but further development is necessary to address the environmental and management factors that can affect accuracy.