[en] 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.
Disciplines :
Animal production & animal husbandry Computer science Agriculture & agronomy
Author, co-author :
Martinez-Rau, Luciano S. ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina ; Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden
Chelotti, José ; Université de Liège - ULiège > Département GxABT > Animal Sciences (AS) ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina
Ferrero, Mariano ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina
Galli, Julio R. ; Instituto de Investigaciones en Ciencias Agrarias de Rosario, IICAR, UNR-CONICET, Argentina ; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Argentina
Utsumi, Santiago A. ; W.K. Kellogg Biological Station and Department of Animal Science, Michigan State University, United States ; Department of Animal and Range Science, New Mexico State University, United States
Planisich, Alejandra M. ; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Argentina
Rufiner, H. Leonardo ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina ; Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Argentina
Giovanini, Leonardo L.; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Argentina
Language :
English
Title :
A noise-robust acoustic method for recognizing foraging activities of grazing cattle
Knowledge Foundation UNR - National University of Rosario NIFA - National Institute of Food and Agriculture UNL - Universidad Nacional del Litoral ASaCTeI - Agencia Santafesina de Ciencia, Tecnología e Innovación CONICET - National Scientific and Technical Research Council
Funding text :
The authors wish to express their gratitude to the staff of the KBS Robotic Dairy Farm, who participated in the investigation. Additionally, we acknowledge the direct support from AgBioResearch-MSU. The authors would like to thank Constanza Quaglia (technical staff, CONICET) and J. Tom\u00E1s Molas G. (technical staff, UNER-UNL) for their technical support in achieving the web demo. This work was supported by the Universidad Nacional del Litoral [CAID 50620190100080LI and 50620190100151LI ]; Universidad Nacional de Rosario [AGR216, 2013 - AGR266, 2016 - and 80020180300053UR , 2019]; Agencia Santafesina de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n [ IO-2018\u201300082 ], CONICET [PUE sinc(I), 2017]; and USDA-NIFA [ MICL0222 and MICL0406 ].The authors wish to express their gratitude to the staff of the KBS Robotic Dairy Farm, who participated in the investigation. Additionally, we acknowledge the direct support from AgBioResearch-MSU. The authors would like to thank Constanza Quaglia (technical staff, CONICET) and J. Tom\u00E1s Molas G. (technical staff, UNER-UNL) for their technical support in achieving the web demo. This work was supported by the Universidad Nacional del Litoral, Argentina [CAID 50620190100080LI and 50620190100151LI]; Universidad Nacional de Rosario, Argentina [AGR216, 2013 - AGR266, 2016 - and 80020180300053UR, 2019]; Agencia Santafesina de Ciencia, Tecnolog\u00EDa e Innovaci\u00F3n [IO-2018\u201300082], CONICET, Argentina [PUE sinc(I), 2017]; USDA-NIFA [MICL0222 and MICL0406] and Knowledge Foundation, Sweden [CAID NIIT 20180170. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Utsumi Santiago reports financial support was provided by National Institute of Food and Agriculture. Giovanini, Leonardo Luis has patent #AR P20220100910 pending to Assignee.
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