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Article

Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision

Laboratory of Veterinary Surgery, Department of Veterinary Science, University of Miyazaki, 1-1 Gakuen Kibana-dai Nishi, Miyazaki 889-2192, Japan
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Animals 2021, 11(1), 207; https://doi.org/10.3390/ani11010207
Received: 6 December 2020 / Revised: 14 January 2021 / Accepted: 14 January 2021 / Published: 16 January 2021
Breathing patterns are commonly used to assess cattle health and welfare parameters such as stress, pain, and disease. Infrared thermography has recently been accepted as a non-invasive tool for breathing pattern measurement. In this study, we applied a computer vision method (Mask R-CNN) to infrared thermography and made it possible to automatically estimate the breathing pattern in cattle. Breathing patterns identified by computer vision were highly correlated with those measured through thermal image observation. As this method is not labor-intensive and can handle numerable big data, it might be possible to analyze breathing patterns from various angles in the future.
Breathing patterns can be considered a vital sign providing health information. Infrared thermography is used to evaluate breathing patterns because it is non-invasive. Our study used not only sequence temperature data but also RGB images to gain breathing patterns in cattle. Mask R-CNN was used to detect the ROI (region of interest, nose) in the cattle RGB images. Mask segmentation from the ROI detection was applied to the corresponding temperature data. Finally, to visualize the breathing pattern, we calculated the temperature values in the ROI by averaging all temperature values in the ROI. The results in this study show 76% accuracy with Mask R-CNN in detecting cattle noses. With respect to the temperature calculation methods, the averaging method showed the most appropriate breathing pattern compared to other methods (maximum temperature in the ROI and integrating all temperature values in the ROI). Finally, we compared the breathing pattern from the averaging method and that from the thermal image observation and found them to be highly correlated (R2 = 0.91). This method is not labor-intensive, can handle big data, and is accurate. In addition, we expect that the characteristics of the method might enable the analysis of temperature data from various angles. View Full-Text
Keywords: breathing pattern; infrared thermography; cattle health and welfare; computer vision; machine learning breathing pattern; infrared thermography; cattle health and welfare; computer vision; machine learning
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MDPI and ACS Style

Kim, S.; Hidaka, Y. Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision. Animals 2021, 11, 207. https://doi.org/10.3390/ani11010207

AMA Style

Kim S, Hidaka Y. Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision. Animals. 2021; 11(1):207. https://doi.org/10.3390/ani11010207

Chicago/Turabian Style

Kim, Sueun, and Yuichi Hidaka. 2021. "Breathing Pattern Analysis in Cattle Using Infrared Thermography and Computer Vision" Animals 11, no. 1: 207. https://doi.org/10.3390/ani11010207

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