Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs

Mark F. Hansen, Emma M. Baxter, Kenneth M. D. Rutherford, Agnieszka Futro, Melvyn L. Smith and Lyndon N. Smith
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Mark F. Hansen: Centre for Machine Vision, BRL, UWE Bristol, Bristol BS16 1QY, UK
Emma M. Baxter: Animal Behaviour and Welfare, Animal and Veterinary Sciences Research Group, SRUC, West Mains Road, Edinburgh EH9 3JG, UK
Kenneth M. D. Rutherford: Animal Behaviour and Welfare, Animal and Veterinary Sciences Research Group, SRUC, West Mains Road, Edinburgh EH9 3JG, UK
Agnieszka Futro: Animal Behaviour and Welfare, Animal and Veterinary Sciences Research Group, SRUC, West Mains Road, Edinburgh EH9 3JG, UK
Melvyn L. Smith: Centre for Machine Vision, BRL, UWE Bristol, Bristol BS16 1QY, UK
Lyndon N. Smith: Centre for Machine Vision, BRL, UWE Bristol, Bristol BS16 1QY, UK

Agriculture, 2021, vol. 11, issue 9, 1-15

Abstract: Animal welfare is not only an ethically important consideration in good animal husbandry but can also have a significant effect on an animal’s productivity. The aim of this paper was to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We trained a convolutional neural network (CNN) using a leave-one-out design and showed that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM was used to identify the animal regions used, and these supported those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with the aim of developing an automated system that can be used in precision livestock farming to improve animal welfare.

Keywords: animal welfare; pigs; deep learning; computer vision; stress detection; facial expression recognition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
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