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: https://sinc.unl.edu.ar/web-demo/nrfar

翻译：为了在日益增长的乳制品市场中保持竞争力，养殖户必须持续改进其畜牧生产系统。精准畜牧业技术能够对商业牧场中的动物进行个体化监测，从而优化畜牧生产。连续声学监测是一种广泛使用的感知技术，用于估算自由放牧牛的每日反刍和放牧时间预算。然而，草场上典型的环境噪声和自然噪声会明显影响当前声学方法的性能与泛化能力。本研究提出了一种名为“抗噪觅食活动识别器”（NRFAR）的声学方法。该方法通过分析与放牧和反刍相关的已识别下颌运动事件的固定长度片段，来确定觅食活动时段。我们使用平稳高斯白噪声和四种不同的非平稳自然噪声源，针对多个信噪比评估了NRFAR的加性噪声鲁棒性。在无噪声条件下，NRFAR的平均平衡准确率达到89%，比两种先前声学方法高出超过7%。此外，在评估的80个噪声场景中，NRFAR在66个场景下表现优于先前声学方法（p<0.01）。NRFAR可在线运行，且计算成本与先前声学方法相当。这些特性结合在恶劣的自由放牧环境中的高性能，使得NRFAR成为低功耗嵌入式设备实时实现的绝佳选择。本文中介绍的仪器和计算算法受到正在申请的专利保护：AR P20220100910。网络演示见：https://sinc.unl.edu.ar/web-demo/nrfar