To stay competitive in the growing dairy market, farmers must continuously
improve their livestock production systems. Precision livestock farming
technologies provide individualised monitoring of animals on commercial farms,
optimising livestock production. Continuous acoustic monitoring is a widely
accepted sensing technique used to estimate the daily rumination and grazing
time budget of free-ranging cattle. However, typical environmental and natural
noises on pasture noticeably affect the performance and generalisation of
current acoustic methods. In this study, we present an acoustic method called
Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method
determines foraging activity bouts by analysing fixed-length segments of
identified jaw movement events associated with grazing and rumination. The
additive noise robustness of NRFAR was evaluated for several signal-to-noise
ratios, using stationary Gaussian white noise and four different non-stationary
natural noise sources. In noiseless conditions, NRFAR reaches an average
balanced accuracy of 89%, outperforming two previous acoustic methods by more
than 7%. Additionally, NRFAR presents better performance than previous acoustic
methods in 66 out of 80 evaluated noisy scenarios (p<0.01). NRFAR operates
online with a similar computational cost to previous acoustic methods. The
combination of these properties and the high performance in harsh free-ranging
environments render NRFAR an excellent choice for real-time implementation in a
low-power embedded device. The instrumentation and computational algorithms
presented within this publication are protected by a pending patent
application: AR P20220100910. Web demo available at:
this https URL

本研究提出了噪声鲁棒的饲草活动识别器 (NRFAR)，通过分析固定长度的下颌运动事件识别牛的觅食活动，经过在不同噪声环境下的验证，NRFAR 在牛在自由放牧环境下的实时应用中具有显著的性能优势，能够更好地优化畜牧生产。