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Livestock feeding behaviour is an influential research area in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Current automated monitoring systems mainly use motion, acoustic, pressure and image sensors to collect and analyse patterns related to ingestive behaviour, foraging activities and daily intake. The performance evaluation of existing methods is a complex task and direct comparison s between studies is difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. This review on the analysis of the feeding behaviour of ruminants emphasise the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies and the main techniques to analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potential of the valuable information provided by automated monitoring systems to expand knowledge in the field, positively impacting production systems and research. The paper closes by discussing future engineering challenges and opportunities in livestock feeding behaviour monitoring. • The ruminant feeding process and its sensing variables are described. • Monitoring systems based on movement, sound, images and pressure are analysed. • Features of the acquisition, management, and availability of the data are discussed. • Signal processing and machine learning methods used in the algorithms are analysed. • Trends in sensing technology and computational methods are presented. [ABSTRACT FROM AUTHOR]

Precision livestock farming optimises livestock production through the use of sensor information and communication technologies to support decision making in real-time. Among available technologies to monitor foraging behaviour, the acoustic method has been highly reliable and repeatable, but there is a room for further computational improvements to increase precision and specificity of recognition of foraging activities. In this study, an algorithm called Jaw Movement segment-based Foraging Activity Recogniser (JMFAR) is proposed. The method is based on the computation and analysis of temporal, statistical and spectral features of jaw movement sounds for detection of rumination and grazing bouts. They are called JM-segment features because they are extracted from a sound segment and expect to capture JM information of the whole segment rather than individual JMs. Additionally, two variants of the method are proposed and tested: (i) one considering the temporal and statistical features only (JMFAR-ns); and (ii) another considering a feature selection process (JMFAR-sel). The JMFAR was tested on signals registered in a free grazing environment, achieving an average weighted F1-score of 93%. Then, it was compared with a state-of-the-art algorithm, showing improved performance for estimation of grazing bouts (+19%). The JMFAR-ns variant reduced the computational cost by 25.4%, but achieved a slightly lower performance than the JMFAR. The good performance and low computational cost of JMFAR-ns supports the feasibility of using this algorithm variant for real-time implementation in low-cost embedded systems. The method presented within this publication is protected by a pending patent application: AR P20220100910. Web demo available at: https://sinc.unl.edu.ar/web-demo/jmfar/ • An acoustic method to recognize foraging activities in grazing cattle is presented. • A set of features independent of the identification of jaw-movements is proposed. • Two variants of the base method are proposed. • The algorithm improves grazing time estimation compared to previous acoustic methods. • Its low computational cost allows real-time execution on low-cost embedded platforms. [ABSTRACT FROM AUTHOR]