  Farmers must continuously improve their livestock production systems to
remain competitive in the growing dairy market. Precision livestock farming
technologies provide individualized monitoring of animals on commercial farms,
optimizing 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 pastures noticeably affect the performance limiting the practical
application of current acoustic methods. In this study, we present the
operating principle and generalization capability of an acoustic method called
Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method
determines foraging activity bouts by analyzing fixed-length segments of
identified jaw movement events produced during grazing and rumination. The
additive noise robustness of the NRFAR was evaluated for several
signal-to-noise ratios using stationary Gaussian white noise and four different
nonstationary natural noise sources. In noiseless conditions, NRFAR reached an
average balanced accuracy of 86.4%, outperforming two previous acoustic methods
by more than 7.5%. Furthermore, NRFAR performed better than previous acoustic
methods in 77 of 80 evaluated noisy scenarios (53 cases with p<0.05). NRFAR has
been shown to be effective in harsh free-ranging environments and could be used
as a reliable solution to improve pasture management and monitor the health and
welfare of dairy cows. The instrumentation and computational algorithms
presented in this publication are protected by a pending patent application: AR
P20220100910. Web demo available at: https://sinc.unl.edu.ar/web-demo/nrfar
