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Abstract
Scientific studies of farm animals’ minds are skewed towards primarily traits that are easier to quantify than emotions. In addition, human biases pose a barrier to fully exploring and understanding the emotions of animals. Forexample: as humans we often rely on language to make sense of emotions and misconstrue an animal’s expressionof emotions from the framework of human emotional expression. Emotions help individual farm animals to formand navigate social relationships. Understanding animal emotions is a key to unlocking methods for improvinganimal welfare. Knowledge on how farm animals feel is an absolute requirement in developing complete animalwelfare auditing tools.Emotions consist of both a valence (positive vs. negative) and arousal (high vs. low) dimension. Currently researchers use physiological measures by collecting blood samples or saliva to look for cortisol, lactate, oxytocin andother hormones or biochemical markers in determining emotional valence and/or arousal in farm animals. However, many of these methods are invasive. Researchers also use behaviour such as body postures and vocalizationsas indicators of farm animals’ mental states. Here we present a Wageningen University technology that automatically measures farm animal emotions to overcome the subjectivity associated in human based measures; to reducemanhandling of animals and animal-based experiments; and to establish non-invasive ways to assess good andpoor welfare of farm animals from their positive and negative emotional states.The Artificial Intelligence based facial coding platform developed and named after the mascot WUR Wolf has theability to extract features such as eye white, ear postures and facial cues in determining the mental make up of thefarm animals such as cows and pigs. Python based machine learning algorithms measures the facial features andcorrelate with the established indicators of the positive, neutral and negative emotions of cows and pigs from theacquired data set. The facial coding platform is expected to enhance the capacity of modern animal farming in preventing, monitoring and controlling animal diseases including emerging risks, and provide integrated approachesfor animal welfare.
Original language | English |
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Title of host publication | Proceedings of the 54th Congress of the ISAE |
Subtitle of host publication | Developing animal behaviour and welfare: Real solutions for real problems |
Editors | Cathy M. Dwyer, Moira Harris, S. Adbul Rahman, Susanne Waiblinger, T. Bas Rodenburg |
Publisher | International Society for Applied Ethology (ISAE) |
Pages | 154-154 |
Publication status | Published - 2021 |
Event | 54th Congress of the International Society for Applied Ethology - online, Bangalore, India Duration: 26 Jul 2021 → 6 Aug 2021 |
Conference/symposium
Conference/symposium | 54th Congress of the International Society for Applied Ethology |
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Country/Territory | India |
City | Bangalore |
Period | 26/07/21 → 6/08/21 |
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Dive into the research topics of 'WUR Wolf - A Facial Recognition System for Animal Welfare 2.0'. Together they form a unique fingerprint.Activities
- 1 Oral presentation
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WUR Wolf - A Facial Recognition System for Animal Welfare 2.0
Neethirajan, S. (Speaker) & Kemp, B. (Contributor)
2021Activity: Talk or presentation › Oral presentation › Academic