Assessment of Ventral Tail Base Surface Temperature for the Early Detection of Japanese Black Calves with Fever

SIMPLE SUMMARY: Bovine respiratory disease is the most common and costly disease in beef cattle. We previously elucidated risk factors for bovine respiratory disease in Japanese Black calves reared in a backgrounding operation. In this operation, calves are reared in an intensive freestall system that contains several calves, and it is important to develop an early detection system to identify calves with any problems at an early stage. This study examined the assessment of the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. A wearable wireless tail ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the backgrounding farm. Data obtained from the ST sensor were analyzed using supervised machine learning algorithms that use a random forest. This study found that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor. This knowledge could contribute to decreasing labor and the burden on clinical veterinarians. ABSTRACT: The objective in the present study was to assess the ventral tail base surface temperature (ST) for the early detection of Japanese Black calves with fever. This study collected data from a backgrounding operation in Miyazaki, Japan, that included 153 calves aged 3–4 months. A wearable wireless ST sensor was attached to the surface of the ventral tail base of each calf at its introduction to the farm. The ventral tail base ST was measured every 10 min for one month. The present study conducted an experiment to detect calves with fever using the estimated residual ST (rST), calculated as the estimated rST minus the mean estimated rST for the same time on the previous 3 days, which was obtained using machine learning algorithms. Fever was defined as an increase of ≥1.0 °C for the estimated rST of a calf for 4 consecutive hours. The machine learning algorithm that applied was a random forest, and 15 features were included. The variable importance scores that represented the most important predictors for the detection of calves with fever were the minimum and maximum values during the last 3 h and the difference between the current value and 24- and 48-h minimum. For this prediction model, accuracy, precision, and sensitivity were 98.8%, 72.1%, and 88.1%, respectively. The present study indicated that the early detection of calves with fever can be predicted by monitoring the ventral tail base ST using a wearable wireless sensor.