Lowe, G., Sutherland, M., Waas, J. et al. 2019. Infrared thermography—A non-invasive method of measuring respiration rate in calves. Animals 9(8), 535.
Respiration rate (RR) is commonly used to assess states of cattle health and welfare such as pain, stress and disease. Traditionally, RR is measured by counting flank movements, a method often considered to be labour-intensive and impractical. This study investigated the use of infrared thermography (IRT) to non-invasively measure RR in calves, based on thermal fluctuations around the nostrils during inhalation and exhalation. Infrared estimates of RR were highly correlated with RR measured by observing flank movements. Through future development, the integration of IRT into existing automated systems (e.g., automated calf feeders) could support regular monitoring of calf health and welfare. Respiration rate (RR) is a common measure of cattle health and welfare. Traditionally, measuring RR involves counting flank movements as the animal inhales and exhales with each breath. This method is often considered difficult, labour-intensive and impractical. We validated the use of infrared thermography (IRT) as an alternative method of non-invasively measuring RR in young calves. RR was simultaneously recorded in two ways: (1) by observing flank movements from video recordings; and (2) by observing thermal fluctuations around the nostrils during inhalations and exhalations from infrared recordings. For each method, the time taken to complete five consecutive breaths (a breath being a complete inhalation/exhalation cycle) was recorded and used to calculate RR (breaths/min). From a group of five calves, a total of 12 video recordings and 12 infrared recordings were collected. For each procedure, 47 sets of five consecutive breaths were assessed. The RRs measured from video recordings of flank movements and thermal fluctuations around the nostrils from infrared recordings were highly correlated (R2 = 0.93). Validated as a suitable method for recording RR, future research can now focus on the development of algorithms to automate the use of IRT to support its integration into existing automated systems to remotely monitor calf health and welfare.