Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle

Simple Summary Animal monitoring normally requires procedures that are time- and labour-consuming. The implementation of novel non-invasive technologies could be a good approach to monitor animal health and welfare. This study aimed to evaluate the use of images and computer-based methods to track specific features of the face and to assess temperature; respiration rate and heart rate in cattle. The measurements were compared with measures obtained with conventional methods during the same time period. The data were collected from ten dairy cows that were recorded during six handling procedures across two consecutive days. The results from this study show over 92% of accuracy from the computer algorithm that was developed to track the areas selected on the videos collected. In addition, acceptable correlation was observed between the temperature calculated from thermal infrared images and temperature collected using intravaginal loggers. Moreover, there was acceptable correlation between the respiration rate calculated from infrared videos and from visual observation. Furthermore, a low to high relationship was found between the heart rate obtained from videos and from attached monitors. The study also showed that both the position of the cameras and the area analysed on the images are very important, as both had large impact on the accuracy of the methods. The positive outcomes and the limitations observed in this study suggest the need for further research Abstract Precision livestock farming has emerged with the aim of providing detailed information to detect and reduce problems related to animal management. This study aimed to develop and validate computer vision techniques to track required features of cattle face and to remotely assess eye temperature, ear-base temperature, respiration rate, and heart rate in cattle. Ten dairy cows were recorded during six handling procedures across two consecutive days using thermal infrared cameras and RGB (red, green, blue) video cameras. Simultaneously, core body temperature, respiration rate and heart rate were measured using more conventional 'invasive' methods to be compared with the data obtained with the proposed algorithms. The feature tracking algorithm, developed to improve image processing, showed an accuracy between 92% and 95% when tracking different areas of the face of cows. The results of this study also show correlation coefficients up to 0.99 between temperature measures obtained invasively and those obtained remotely, with the highest values achieved when the analysis was performed within individual cows. In the case of respiration rate, a positive correlation (r = 0.87) was found between visual observations and the analysis of non-radiometric infrared videos. Low to high correlation coefficients were found between the heart rates (0.09-0.99) obtained from attached monitors and from the proposed method. Furthermore, camera location and the area analysed appear to have a relevant impact on the performance of the proposed techniques. This study shows positive outcomes from the proposed computer vision techniques when measuring physiological parameters. Further research is needed to automate and improve these techniques to measure physiological changes in farm animals considering their individual characteristics.