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Published in Journal of Agriculture and Food Research, 2022
This study proposed using non-invasive video acquisition and biometric analysis of dairy cows in a robotic dairy farm (RDF) to assess milk productivity, quality traits, and welfare for RDF and conventional dairy farms.
Recommended citation: Sigfredo Fuentes, Claudia Gonzalez Viejo, Eden Tongson, Frank R. Dunshea, Hai Ho Dac, Nir Lipovetzky. (2020). "Animal biometric assessment using non-invasive computer vision and machine learning are good predictors of dairy cows age and welfare: The future of automated veterinary support systems." Journal of Agriculture and Food Research, Volume 10, 2022. https://doi.org/10.1016/j.jafr.2022.100388
Published in Sensors, 2022
This study aimed to develop a face recognition system for dairy farm cows using advanced deep learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment.
Recommended citation: Dac Hai Ho, Claudia Gonzalez Viejo, Nir Lipovetzky, Eden Tongson, Frank R. Dunshea, and Sigfredo Fuentes. 2022. "Livestock Identification Using Deep Learning for Traceability" Sensors 22, no. 21: 8256. https://doi.org/10.3390/s22218256
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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