  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.
