Using segment-based features of jaw movements to recognise foraging activities in grazing cattle

Precision livestock farming optimises livestock production through the use of sensor in-formation 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 im-provements 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 ( thorn 19%). The JMFAR-ns variant reduced the computational cost by 25.4%, but achieved a slightly lower performance than the JMFAR. The good per-formance 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/ (c) 2023 IAgrE. Published by Elsevier Ltd. All rights reserved.