Computer Vision Techniques to Support Animal Welfare and Veterinary Public Health
Rachele Urbani, Tommaso Bergamasco, Giacomo Nalesso, Vittoria Tregnaghi, Francesca Menegon, Massimiano Bassan, Grazia Manca, Guido Di Martino
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Rachele Urbani
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Tommaso Bergamasco
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Giacomo Nalesso
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Vittoria Tregnaghi
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Francesca Menegon
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Massimiano Bassan
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Grazia Manca
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Guido Di Martino
Istituto Zooprofilattico Sperimentale delle Venezie, Legnaro, Padova, Italy
Cite this paper as:Urbani, R., Bergamasco, T., Nalesso, G., Tregnaghi, V., Menegon, F., Bassan, M., Manca, G., Di Martino, G. (2025). Computer Vision Techniques to Support Animal Welfare and Veterinary Public Health.
Journal of Systemics, Cybernetics and Informatics, 23(5), 24-27. https://doi.org/10.54808/JSCI.23.05.24
Online ISSN (Journal): 1690-4524
Abstract
The application of artificial intelligence in animal husbandry and veterinary medicine is gaining increasing attention. Using computer vision systems for assessing animal welfare seems promising in the latter field. The Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe) is developing systems for assessing animal welfare based on innovative technological tools leveraging deep learning algorithms for complex computer vision tasks. These tools enable the automation of data processing, significantly increasing efficiency and scalability. By replacing labor-intensive manual analysis, the system allows for the rapid processing of large volumes of data, ensuring the extraction of critical information that would otherwise be lost or impractical to obtain through conventional methods.