**Preface to "Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming"**

Animal production (e.g., milk, meat, and eggs) provides valuable protein for human beings and animals. However, animal production is facing a number of challenges worldwide such as environmental impacts and animal welfare/health concerns. Maintaining the good health and welfare of livestock and poultry is very important in terms of production efficiency, social economy, and sustainability. In livestock and poultry farming operations, accurate and efficient monitoring of livestock and poultry information can help us to analyze the health and welfare status of animals. Early detection of sick or abnormal individuals can help reduce economic losses and protect animal welfare. In recent years, there has been growing interest in animal welfare. At present, livestock and poultry farming mainly relies on manual observation to obtain animal information, but the method is labor intensive and subjective to human errors. The contact method of implanting devices/sensors into animals to monitor animals' physiological conditions has been tested widely. The concern of this contact method is causing animal stress responses and impacting animal wellbeing. Noninvasive monitoring technologies of computer vision systems can reduce or avoid the impact of observers on animals and related stress response to animals in the monitoring of animal behaviors and welfare, while there is a lack of artificial intelligent strategies, e.g., machine learning or deep learning, that can track animals and extract welfare indicators accurately and quantitatively. Therefore, innovating engineering strategies, such as computer vision-based systems, to identify issues related to animal health and welfare automatically in real-time are critical for enhancing animal production efficiency and welfare. This book therefore aims to gather information and updated research on "Advances in Sensors, Big Data and Machine Learning in Intelligent Animal Farming".

> **Yongliang Qiao, Lilong Chai, Dongjian He, and Daobilige Su** *Editors*
