Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images

Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using thermal infrared thermography. However, the use of this detection technique to determine the health of the cow's udder is susceptible to external factors, resulting in inaccurate detection of dairy cow mastitis. Therefore, this study explored a new and comprehensive detection method of dairy cow mastitis based on infrared thermal images. This method combined the left and right udder skin surface temperature (USST) difference detection method with the ocular surface temperature and USST difference detection method with improvements. The effect of external factors on dairy cow USST was effectively reduced. In addition, after comparing different target localisation algorithms, this paper used the You Only Look Once v5 (YOLOv5) deep learning network model to obtain the temperature information of eyes and udders, and mastitis detection of dairy cows was performed. A total of 105 dairy cows passing through a passage were randomly selected from the thermal infrared video and detected by the new and comprehensive detection method, and the results of cow mastitis detection were compared with somatic cell count. The results showed that the accuracy, specificity, and sensitivity of mastitis detection were 87.62, 84.62, and 96.30%, respectively. Using the YOLOv5 deep learning network model to locate the key parts of the cow had a good effect, with an average accuracy of 96.1%, and an average frame rate of 116.3f/s. The detection accuracy of dairy cow mastitis by deep learning technology combined with the detection method in this paper reached 85.71%. The results showed that the new and comprehensive detection method based on infrared thermal images can be used for the detection of dairy cow mastitis with high detection accuracy. This method can reduce the influence of external factors and can be integrated into the automatic identification system of dairy mastitis based on YOLOv5 to realise on-site monitoring of dairy mastitis.