Livestock feeding behaviour is an influential research area for those involved in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Despite the developments accomplished in the last decade, there is still much to do and learn about the methods for measuring and analysing livestock feeding behaviour. Automated monitoring systems mainly use motion, acoustic, and image sensors to collect animal behavioural data. The performance evaluation of existing methods is a complex task and direct comparisons between studies are difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. To the best of our knowledge, this work represents the first tutorial-style review on the analysis of the feeding behaviour of ruminants, emphasising the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies (i.e. based on movement, sound, images/videos, and pressure) and the main techniques to measure and analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potentiality of automated monitoring systems to provide valuable information that improves our understanding of livestock feeding behaviour. The relevance of these systems is increasingly important due to their impact on production systems and research. Finally, the paper closes by discussing future challenges and opportunities in livestock feeding behaviour monitoring.

翻译：家畜采食行为是畜牧业与农业领域的重要研究方向。近年来，针对反刍动物行为的自动化监测系统日益受到关注。尽管过去十年已取得诸多进展，但在家畜采食行为的测量与分析方法方面仍存在大量有待探索的空间。自动化监测系统主要利用运动、声学及图像传感器采集动物行为数据。现有方法的性能评估是一项复杂任务，研究间的直接比较存在困难。这种困难首先源于实验所用数据与性能指标的多样性。据我们所知，本文首次以教程式综述的形式系统探讨反刍动物采食行为分析，重点阐释传感方法、信号处理与计算智能方法之间的关联。研究评估了主流传感方法（基于运动、声音、图像/视频及压力）以及测量分析采食行为相关信号的主要技术，并评价了它们在不同场景下的适用性。同时，本文强调了自动化监测系统在提供改善家畜采食行为认知的关键信息方面的潜力。鉴于其对生产体系与研究的重要影响，这类系统的现实意义日益凸显。最后，本文通过探讨家畜采食行为监测领域的未来挑战与机遇作结。