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Open AccessArticle

Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras

1
Digital Agriculture, Food and Wine Sciences Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
2
Animal Nutrition and Physiology, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville 3010, Australia
3
Faculty of Biological Sciences, The University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6334; https://doi.org/10.3390/s20216334
Received: 26 September 2020 / Revised: 2 November 2020 / Accepted: 4 November 2020 / Published: 6 November 2020
Live sheep export has become a public concern. This study aimed to test a non-contact biometric system based on artificial intelligence to assess heat stress of sheep to be potentially used as automated animal welfare assessment in farms and while in transport. Skin temperature (°C) from head features were extracted from infrared thermal videos (IRTV) using automated tracking algorithms. Two parameter engineering procedures from RGB videos were performed to assess Heart Rate (HR) in beats per minute (BPM) and respiration rate (RR) in breaths per minute (BrPM): (i) using changes in luminosity of the green (G) channel and (ii) changes in the green to red (a) from the CIELAB color scale. A supervised machine learning (ML) classification model was developed using raw RR parameters as inputs to classify cutoff frequencies for low, medium, and high respiration rate (Model 1). A supervised ML regression model was developed using raw HR and RR parameters from Model 1 (Model 2). Results showed that Models 1 and 2 were highly accurate in the estimation of RR frequency level with 96% overall accuracy (Model 1), and HR and RR with R = 0.94 and slope = 0.76 (Model 2) without statistical signs of overfitting View Full-Text
Keywords: animal welfare; skin temperature; artificial intelligence; heart rate; respiration rate animal welfare; skin temperature; artificial intelligence; heart rate; respiration rate
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MDPI and ACS Style

Fuentes, S.; Gonzalez Viejo, C.; Chauhan, S.S.; Joy, A.; Tongson, E.; Dunshea, F.R. Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras. Sensors 2020, 20, 6334. https://doi.org/10.3390/s20216334

AMA Style

Fuentes S, Gonzalez Viejo C, Chauhan SS, Joy A, Tongson E, Dunshea FR. Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras. Sensors. 2020; 20(21):6334. https://doi.org/10.3390/s20216334

Chicago/Turabian Style

Fuentes, Sigfredo; Gonzalez Viejo, Claudia; Chauhan, Surinder S.; Joy, Aleena; Tongson, Eden; Dunshea, Frank R. 2020. "Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras" Sensors 20, no. 21: 6334. https://doi.org/10.3390/s20216334

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