  Livestock health and welfare monitoring has traditionally been a
labor-intensive task performed manually. Recent advances have led to the
adoption of AI and computer vision techniques, particularly deep learning
models, as decision-making tools within the livestock industry. These models
have been employed for tasks like animal identification, tracking, body part
recognition, and species classification. In the past decade, there has been a
growing interest in using these models to explore the connection between
livestock behaviour and health issues. While previous review studies have been
rather generic, there is currently no review study specifically focusing on DL
for livestock behaviour recognition. Hence, this systematic literature review
(SLR) was conducted. The SLR involved an initial search across electronic
databases, resulting in 1101 publications. After applying defined selection
criteria, 126 publications were shortlisted. These publications were further
filtered based on quality criteria, resulting in the selection of 44
high-quality primary studies. These studies were analysed to address the
research questions. The results showed that DL successfully addressed 13
behaviour recognition problems encompassing 44 different behaviour classes. A
variety of DL models and networks were employed, with CNN, Faster R-CNN,
YOLOv5, and YOLOv4 being among the most common models, and VGG16, CSPDarknet53,
GoogLeNet, ResNet101, and ResNet50 being popular networks. Performance
evaluation involved ten different matrices, with precision and accuracy being
the most frequently used. Primary studies identified challenges, including
occlusion, adhesion, data imbalance, and the complexities of the livestock
environment. The SLR study also discussed potential solutions and research
directions to facilitate the development of autonomous livestock behaviour
recognition systems.
