Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Abstract. Dairy cows' milk production is closely related to their health status. One of the indicators reflecting their health status is their body temperature. Infrared thermal imaging has been demonstrated to process a high potential for non-contact measurement of dairy cow body temperature, which is crucial for establishing an automated health monitoring system for dairy cow management. Although several studies have reported on the dairy cow temperature measurement by handheld thermal imaging cameras, manual measurement is not a feasible approach for practical application in the dairy industry as it is laborious and time-consuming. To solve these problems, this work proposes an automated non-contact thermal imaging monitoring system that can efficiently take dairy cow eye temperature measurement from thermal video stream in real time. The system utilizes a deep learning approach for dairy cow eye detection. A YOLOv4 model for real-time dairy cow eye detection was trained and optimized; it yielded a hit rate of 0.99 and an F1-score of 0.99. For each detected sub-image containing the dairy cow eye in the video stream, a further image processing algorithm was applied to determine the mean temperature with its variance. With this approach, multiple temperature measurements are taken from each dairy cow walking by the thermal camera. The system was installed in the university experimental dairy farm and long-term experiments were carried out to assess the variations of temperature measurement. It was found that both the ambient temperature and the thermal camera distance have strong effect on the temperature measurement, indicating that the eye temperature measurement needs to be corrected with the ambient temperature and measured temperatures need to be preprocessed in order to increase its accuracy. The experimental results also show that the proposed system has potential in regard to detecting dairy cow fever or assessing of heat stress.