Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 27 July 2021 / Approved: 19 August 2021 / Online: 19 August 2021 (13:17:08 CEST)

A peer-reviewed article of this Preprint also exists.

Hansen, M.F.; Baxter, E.M.; Rutherford, K.M.D.; Futro, A.; Smith, M.L.; Smith, L.N. Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs. Agriculture 2021, 11, 847. Hansen, M.F.; Baxter, E.M.; Rutherford, K.M.D.; Futro, A.; Smith, M.L.; Smith, L.N. Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs. Agriculture 2021, 11, 847.

Journal reference: Agriculture 2021, 11, 847
DOI: 10.3390/agriculture11090847

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 is 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 train a Convolutional Neural Network (CNN) using a leave-one-out design and show that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM is used to identify the animal regions used, and these support 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 a view to the development of 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

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