Next Article in Journal
A Functional 3′ UTR Polymorphism of FADS2 Affects Cow Milk Composition through Modifying Mir-744 Binding
Previous Article in Journal
Chewing and Drinking Activity during Transition Period and Lactation in Dairy Cows Fed Partial Mixed Rations
Open AccessArticle

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

1
Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
2
Animal Welfare Science Centre, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Animals 2019, 9(12), 1089; https://doi.org/10.3390/ani9121089
Received: 5 November 2019 / Revised: 29 November 2019 / Accepted: 4 December 2019 / Published: 6 December 2019
(This article belongs to the Section Cattle)
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
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. View Full-Text
Keywords: computer vision; physiological parameters; animal monitoring; imagery computer vision; physiological parameters; animal monitoring; imagery
Show Figures

Figure 1

MDPI and ACS Style

Jorquera-Chavez, M.; Fuentes, S.; Dunshea, F.R.; Warner, R.D.; Poblete, T.; Jongman, E.C. Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle. Animals 2019, 9, 1089. https://doi.org/10.3390/ani9121089

AMA Style

Jorquera-Chavez M, Fuentes S, Dunshea FR, Warner RD, Poblete T, Jongman EC. Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle. Animals. 2019; 9(12):1089. https://doi.org/10.3390/ani9121089

Chicago/Turabian Style

Jorquera-Chavez, Maria; Fuentes, Sigfredo; Dunshea, Frank R.; Warner, Robyn D.; Poblete, Tomas; Jongman, Ellen C. 2019. "Modelling and Validation of Computer Vision Techniques to Assess Heart Rate, Eye Temperature, Ear-Base Temperature and Respiration Rate in Cattle" Animals 9, no. 12: 1089. https://doi.org/10.3390/ani9121089

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop