###### Abstract  
This study developed an automated temperature measurement and monitoring platform for dairy cattle. The platform used the YOLO V3-tiny (you only look once, YOLO) deep learning algorithm to identify and classify dairy cattle images. The system included a total of three layers of YOLO V3-tiny identification: (1) dairy cow body; (2) individual number (identity, ID); (3) thermal image of eye socket identification. We recorded each cow's individual number and body temperature data after the three layers of identification, and carried out long-term body temperature tracking. The average prediction score of the recognition rate was 96%, and the accuracy was 90.0%. The thermal image of eye socket recognition rate was >99%. The area under the receiver operating characteristic curves (AUC) index of the prediction model was 0.813 (0.717-0.910). This showed that the model had excellent predictive ability. This system provides a rapid and convenient temperature measurement solution for ranchers. The improvement in dairy cattle image recognition can be optimized by collecting more image data. In the future, this platform is expected to replace the traditional solution of intrusive radio-frequency identification for individual recognition. YOLO V3-tiny; deep learning; cattle face detection; thermal imaging system; automated body temperature detection +
Footnote †: journal: Medical Imaging and Image Analysis
Many systems have been proposed for the automatic monitoring and identification of cattle but most of these systems require sensors to be placed on the body of the cow, which can represent a burden for the cow [7]. The challenge facing dairy farmers in the southeastern United States is heat stress and the strains that cause heat stress in lactating dairy cows [8]. Taiwan, which has a subtropical climate, has a high temperature and humidity environment for at least 4-6 months each year, and the resulting heat stress on lactating dairy cattle represents a serious issue for cattle farmers in Taiwan. Therefore, early warning of symptoms, given health care, and the reduction of the huge economic damage caused by diseases, are the priority. Our research team conducted body temperature tests on 52 Holstein-Friesian cattle in 2016. We compared the rectal temperatures of the cows with the temperature of an infrared camera. We found that the average temperature around the eyes (38.1 \({}^{\circ}\)C) was the closest to the rectal temperature (38.6 \({}^{\circ}\)C). This study, published in the Ministry of Science and Technology Agriculture Special Issue ([[https://web.most.gov.tw/sd_ebook/530/sd-530.pdf](https://web.most.gov.tw/sd_ebook/530/sd-530.pdf)]([https://web.most.gov.tw/sd_ebook/530/sd-530.pdf](https://web.most.gov.tw/sd_ebook/530/sd-530.pdf))) (accessed on 28 February 2017), verified that the temperature around the eyes can indeed represent the rectal temperature of dairy cattle. Past studies have shown that dairy cows cannot adapt to long periods of high humidity and high temperature. The decline in milk production and its efficiency in dairy cows are related to the air temperature exceeding a critical threshold, the temperature-humidity index, an increase in rectal temperature and dry matter intake [8]. Radio frequency identification (RFID) technology is a common method currently used for the individual identification of dairy cows. Its convenience and high accuracy (>99%) make farm management easier [9]. However, this technology is invasive for dairy cattle as RFID tags must be implanted in their ears. The cattle may bite each other's tags, and cause the RFID tags to fall off and the ears to tear. Furthermore, they have additional limitations regarding the desired time period or distance. Telemetry is an option for the real-time transmission of recorded data to the receiver software [10, 1]. Convolutional neural networks have been used in image recognition for many years, including handwritten, face recognition, behavior recognition, body condition score of cows, and crop recognition [11, 12].