Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras
Web of Science
AuthorFuentes, S; Viejo, CG; Chauhan, SS; Joy, A; Tongson, E; Dunshea, FR
University of Melbourne Author/sGonzalez Viejo Duran, Claudia; Dunshea, Frank; Chauhan, Surinder Singh; Tongson, Eden; Fuentes Jara, Sigfredo Augusto
AffiliationAgriculture and Food Systems
Document TypeJournal Article
CitationsFuentes, S., Viejo, C. G., Chauhan, S. S., Joy, A., Tongson, E. & Dunshea, F. R. (2020). Non-Invasive Sheep Biometrics Obtained by Computer Vision Algorithms and Machine Learning Modeling Using Integrated Visible/Infrared Thermal Cameras. Sensors, 20 (21), https://doi.org/10.3390/s20216334.
Access StatusOpen Access
Open Access at PMChttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664231
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
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