Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs
Towards Facial Expression Recognition for On-Farm Welfare Assessment in Pigs
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DOI：
10.3390/agriculture11090847
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发表时间：
2021-09-01
影响因子：
3.6
通讯作者：
Smith, Lyndon N.
中科院分区：
文献类型：
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作者：
Hansen, Mark F.;Baxter, Emma M.;Smith, Lyndon N.
动物福利不仅是良好畜牧业的伦理重要考虑因素，而且对动物的生产力也有显着影响。本文的目的是表明，动物福利的减少，在压力增加的形式，可以确定在猪的正面图像的动物。我们使用留一法设计训练了一个卷积神经网络（CNN），并表明它能够区分压力和非压力猪，在看不见的动物中准确率>90%。Grad-CAM用于识别所用动物区域，这些区域支持手动评估中使用的区域，如猪鬼脸量表。这项创新工作为进一步研究积极和消极的福利状态铺平了道路，目的是开发一种可用于精准畜牧业的自动化系统，以改善动物福利。
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.