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

• Main Authors: Mark F. Hansen, Emma M. Baxter, Kenneth M. D. Rutherford, Agnieszka Futro, Melvyn L. Smith, Lyndon N. Smith
• Format: Article
• Language: English
• Published: MDPI AG 2021-09-01
• Series: Agriculture
• Subjects: animal welfare; pigs; deep learning; computer vision; stress detection; facial expression recognition
• Online Access: https://www.mdpi.com/2077-0472/11/9/847

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.