Qiao Yongliang, Kong He, Clark Cameron, Lomax Sabrina, Su Daobilige, Eiffert Stuart, Sukkarieh Salah
Australian Centre for Field Robotics (ACFR), Faculty of Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
Livestock Production and Welfare Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia.
Animals (Basel). 2021 Oct 22;11(11):3033. doi: 10.3390/ani11113033.
The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.
世界人口的不断增长增加了对动物源蛋白质的需求。然而，畜牧业生产力面临着来自传统养殖方式、社会经济地位和气候变化的挑战。近年来，智能传感器、大数据和深度学习已应用于动物福利测量和畜牧养殖应用，包括行为识别和健康监测。为了促进该领域的研究，本综述总结并分析了智能畜牧养殖中使用的一些主要技术，重点关注与牛跛行检测和行为识别相关的技术。在本研究中，对100多篇关于牛跛行检测和行为识别的相关论文进行了评估和讨论。基于对近期技术和方法的综述与比较，我们预计牛行为和福利监测的智能感知将朝着标准化、更大规模和智能化的方向发展，并与物联网（IoT）和深度学习技术相结合。此外，还突出并讨论了未来研究的关键挑战和机遇。
