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.

翻译：牲畜健康与福利监测传统上是一项劳动密集型的人工任务。近年来，人工智能和计算机视觉技术（尤其是深度学习模型）作为决策工具在畜牧业中的应用逐渐兴起。这些模型已被用于动物识别、追踪、身体部位识别和物种分类等任务。过去十年间，利用这些模型探索牲畜行为与健康问题关联的研究兴趣日益增长。虽然以往的综述研究较为泛化，目前尚无专门聚焦于深度学习在牲畜行为识别领域应用的综述。因此，本研究开展了系统性文献综述。通过在电子数据库中进行初步检索，共获得1101篇出版物。经过既定筛选标准，选定126篇出版物。进一步基于质量标准过滤后，最终纳入44篇高质量主要研究。对这些研究的分析回答了研究问题。结果表明，深度学习成功解决了涵盖44种不同行为类别的13个行为识别问题。研究采用了多种深度学习模型和网络，其中CNN、Faster R-CNN、YOLOv5和YOLOv4是最常用的模型，VGG16、CSPDarknet53、GoogLeNet、ResNet101和ResNet50则是流行的网络架构。性能评估涉及十种不同指标，精确率和准确率最为常用。主要研究识别出包括遮挡、粘连、数据不平衡及养殖环境复杂性在内的挑战。本系统性文献综述还讨论了促进自主牲畜行为识别系统开发的潜在解决方案和研究方向。