[en] Monitoring livestock feeding behavior may help assess animal welfare and nutritional status, and to optimize pasture management. The need for continuous and sustained monitoring requires the use of automatic techniques based on the acquisition and analysis of sensor data. This work describes an open dataset of acoustic recordings of the foraging behavior of dairy cows. The dataset includes 708 h of daily records obtained using unobtrusive and non-invasive instrumentation mounted on five lactating multiparous Holstein cows continuously monitored for six non-consecutive days in pasture and barn. Labeled recordings precisely delimiting grazing and rumination bouts are provided for a total of 392 h and for over 6,200 ingestive and rumination jaw movements. Companion information on the audio recording quality and expert-generated labels is also provided to facilitate data interpretation and analysis. This comprehensive dataset is a useful resource for studies aimed at exploring new tools and solutions for precision livestock farming.
Disciplines :
Animal production & animal husbandry
Author, co-author :
Martinez-Rau, Luciano S ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina. luciano.martinezrau@miun.se ; Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden. luciano.martinezrau@miun.se
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, Santa Fe, Argentina
Ferrero, Mariano ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
Utsumi, Santiago A; W.K. Kellogg Biological Station and Department of Animal Science, Michigan State University, East Lansing, USA ; Department of Animal and Range Science, New Mexico State University, Las Cruces, USA
Planisich, Alejandra M; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Santa Fe, Argentina
Vignolo, Leandro D ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
Giovanini, Leonardo L; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina
Rufiner, H Leonardo ; Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i), FICH-UNL/CONICET, Santa Fe, Argentina ; Laboratorio de Cibernética, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Entre Ríos, Argentina
Galli, Julio R; Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, Santa Fe, Argentina ; Instituto de Investigaciones en Ciencias Agropecuarias de Rosario, IICAR, UNR-CONICET, Santa Fe, Argentina
Language :
English
Title :
Daylong acoustic recordings of grazing and rumination activities in dairy cows.
USDA NIFA - United States. Department of Agriculture. National Institute of Food and Agriculture [US-DC] UNL - Universidad Nacional del Litoral [AR] CONICET - Consejo Nacional de Investigaciones Científicas y Técnicas [AR] UNR - Universidad Nacional de Rosario [AR]
Slob, N., Catal, C. & Kassahun, A. Application of machine learning to improve dairy farm management: a systematic literature review. Prev. Vet. Med. 187, 105237 (2021). DOI: 10.1016/j.prevetmed.2020.105237
Lovarelli, D., Bacenetti, J. & Guarino, M. A review on dairy cattle farming: is precision livestock farming the compromise for an environmental, economic and social sustainable production? J. Clean. Prod. 262, 121409 (2020). DOI: 10.1016/j.jclepro.2020.121409
Michie, C. et al. The internet of things enhancing animal welfare and farm operational efficiency. J. Dairy Res. 87, 20–27 (2020). DOI: 10.1017/S0022029920000680
Tzanidakis, C., Tzamaloukas, O., Simitzis, P. & Panagakis, P. Precision livestock farming applications (plf) for grazing animals. Agriculture 13 (2023).
Banhazi, T. M. et al. Precision livestock farming: an international review of scientific and commercial aspects. Int. J. Agric. Biol. Eng. 5, 1–9 (2012).
Hodgson, J. & Illius, A. W. The Ecology and Management of Grazing Systems (Wallingford (United Kingdom) CAB International, 1998).
Garcia, R., Aguilar, J., Toro, M., Pinto, A. & Rodriguez, P. A systematic literature review on the use of machine learning in precision livestock farming. Comput. Electron. Agric. 179, 105826 (2020). DOI: 10.1016/j.compag.2020.105826
Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F. & Pugliese, C. Review: precision livestock farming technologies in pasture-based livestock systems. Animal 16, 100429 (2022). DOI: 10.1016/j.animal.2021.100429
Mahmud, M. S., Zahid, A., Das, A. K., Muzammil, M. & Khan, M. U. A systematic literature review on deep learning applications for precision cattle farming. Comput. Electron. Agric. 187, 106313 (2021). DOI: 10.1016/j.compag.2021.106313
Riaboff, L. et al. Predicting livestock behaviour using accelerometers: a systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data. Comput. Electron. Agric. 192, 106610 (2022). DOI: 10.1016/j.compag.2021.106610
Lovarelli, D. et al. Development of a new wearable 3d sensor node and innovative open classification system for dairy cows’ behavior. Animals 12, 1447 (2022). DOI: 10.3390/ani12111447
Andriamandroso, A., Bindelle, J., Mercatoris, B. & Lebeau, F. A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnol. Agron. Soc. Environ. 20 (2016).
Ferrero, M. et al. A full end-to-end deep approach for detecting and classifying jaw movements from acoustic signals in grazing cattle. Eng. Appl. Artif. Intell. 121, 106016 (2023). DOI: 10.1016/j.engappai.2023.106016
Li, G., Xiong, Y., Du, Q., Shi, Z. & Gates, R. S. Classifying ingestive behavior of dairy cows via automatic sound recognition. Sensors 21, 5231 (2021). DOI: 10.3390/s21155231
Duan, G. et al. Short-term feeding behaviour sound classification method for sheep using lstm networks. Int. J. Agric. Biol. Eng. 14, 43–54 (2021).
Wang, K., Wu, P., Cui, H., Xuan, C. & Su, H. Identification and classification for sheep foraging behavior based on acoustic signal and deep learning. Comput. Electron. Agric. 187, 106275 (2021). DOI: 10.1016/j.compag.2021.106275
Ungar, E. D. & Rutter, S. M. Classifying cattle jaw movements: comparing iger behaviour recorder and acoustic techniques. Appl. Anim. Behav. Sci. 98, 11–27 (2006). DOI: 10.1016/j.applanim.2005.08.011
Schirmann, K., von Keyserlingk, M., Weary, D., Veira, D. & Heuwieser, W. Technical note: validation of a system for monitoring rumination in dairy cows. J. Dairy Sci. 92, 6052–6055 (2009). DOI: 10.3168/jds.2009-2361
Goldhawk, C., Schwartzkopf-Genswein, K. & Beauchemin, K. A. Technical Note: Validation of rumination collars for beef cattle. Journal of Animal Science 91, 2858–2862 (2013). DOI: 10.2527/jas.2012-5908
Martínez Rau, L., Chelotti, J. O., Vanrell, S. R. & Giovanini, L. L. Developments on real-time monitoring of grazing cattle feeding behavior using sound. In 2020 IEEE International Conference on Industrial Technology (ICIT), 771–776 (2020).
Laca, E. A. et al. Acoustic measurement of intake and grazing behaviour of cattle. Grass Forage Sci. 55, 97–104 (2000). DOI: 10.1046/j.1365-2494.2000.00203.x
Galli, J. et al. Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle. Animal 12, 973–982 (2018). DOI: 10.1017/S1751731117002415
Ritter, C., Mills, K. E., Weary, D. M. & von Keyserlingk, M. A. Perspectives of western canadian dairy farmers on the future of farming. J. Dairy Sci. 103, 10273–10282 (2020). DOI: 10.3168/jds.2020-18430
Cockburn, M. Review: application and prospective discussion of machine learning for the management of dairy farms. Animals 10 (2020).
Vanrell, S. R. et al. Audio recordings dataset of grazing jaw movements in dairy cattle. Data Brief 30, 105623 (2020). DOI: 10.1016/j.dib.2020.105623
Jung, D.-H. et al. Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals 11 (2021).
Pandeya, Y. R., Bhattarai, B. & Lee, J. Visual object detector for cow sound event detection. IEEE Access 8, 162625–162633 (2020). DOI: 10.1109/ACCESS.2020.3022058
Deniz, N. N. et al. Embedded system for real-time monitoring of foraging behavior of grazing cattle using acoustic signals. Comput. Electron. Agric. 138, 167–174 (2017). DOI: 10.1016/j.compag.2017.04.024
Martinez-Rau, L. S., Weißbrich, M. & Payá-Vayá, G. A 4 μ w low-power audio processor system for real-time jaw movements recognition in grazing cattle. Journal of Signal Processing Systems 95, 407–424 (2023). DOI: 10.1007/s11265-022-01822-y
Martinez-Rau, L. S. et al. A robust computational approach for jaw movement detection and classification in grazing cattle using acoustic signals. Comput. Electron. Agric. 192, 106569 (2022). DOI: 10.1016/j.compag.2021.106569
Chelotti, J. O. et al. An online method for estimating grazing and rumination bouts using acoustic signals in grazing cattle. Comput. Electron. Agric. 173, 105443 (2020). DOI: 10.1016/j.compag.2020.105443
Chelotti, J. O. et al. Using segment-based features of jaw movements to recognise foraging activities in grazing cattle. Biosystems Engineering 229, 69–84 (2023). DOI: 10.1016/j.biosystemseng.2023.03.014
Martinez-Rau, L. S., Adin, V., Giovanini, L. L., Oelmann, B. & Bader, S. Real-time acoustic monitoring of foraging behavior of grazing cattle using low-power embedded devices. In 2023 IEEE Sensors Applications Symposium (SAS), 01–06 (2023).
Bishop, C. M. Pattern Recognition and Machine Learning (Springer Verlag, 2006).
Meng, T., Jing, X., Yan, Z. & Pedrycz, W. A survey on machine learning for data fusion. Information Fusion 57, 115–129 (2020). DOI: 10.1016/j.inffus.2019.12.001
Watt, L. et al. Differential rumination, intake, and enteric methane production of dairy cows in a pasture-based automatic milking system. J. Dairy Sci. 98, 7248–7263 (2015). DOI: 10.3168/jds.2015-9463
Milone, D. H., Galli, J. R., Cangiano, C. A., Rufiner, H. L. & Laca, E. A. Automatic recognition of ingestive sounds of cattle based on hidden markov models. Comput. Electron. Agric. 87, 51–55 (2012). DOI: 10.1016/j.compag.2012.05.004
Chelotti, J. O. et al. A real-time algorithm for acoustic monitoring of ingestive behavior of grazing cattle. Comput. Electron. Agric. 127, 64–75 (2016). DOI: 10.1016/j.compag.2016.05.015
Chelotti, J. O., Vanrell, S. R., Galli, J. R., Giovanini, L. L. & Rufiner, H. L. A pattern recognition approach for detecting and classifying jaw movements in grazing cattle. Comput. Electron. Agric. 145, 83–91 (2018). DOI: 10.1016/j.compag.2017.12.013
Galli, J. R. et al. Discriminative power of acoustic features for jaw movement classification in cattle and sheep. Bioacoustics 29, 602–616 (2020). DOI: 10.1080/09524622.2019.1633959
Martinez-Rau, LS. et al. Open dataset of acoustic recordings of foraging behavior in dairy cows, figshare, 10.6084/m9.figshare.c.6465301.v1 (2023).
Bosi, M. & Goldberg, R. E. MPEG-1 Audio, 265–313 (Springer US, Boston, MA, 2003).
Ungar, E. D. et al. The implications of compound chew–bite jaw movements for bite rate in grazing cattle. Applied Animal Behaviour Science 98, 183–195 (2006). DOI: 10.1016/j.applanim.2005.09.001
Loizou, P. C. Speech Enhancement: Theory and Practice (CRC press, 2013).
Oppenheim, A. V., Willsky, A. S., Nawab, S. H. & Ding, J.-J. Signals and Systems, vol. 2 (Prentice hall Upper Saddle River, NJ, 1997).
Cannam, C., Landone, C. & Sandler, M. Sonic visualiser: An open source application for viewing, analysing, and annotating music audio files. In Proceedings of the 18th ACM International Conference on Multimedia, 1467–1468 (2010).
Brand, A., Allen, L., Altman, M., Hlava, M. & Scott, J. Beyond authorship: attribution, contribution, collaboration, and credit. Learn. Publ. 28, 151–155 (2015). DOI: 10.1087/20150211
