Slob, N., Catal, C. & Kassahun, A. Software of machine studying to enhance dairy farm administration: a scientific literature overview. Prev. Know. With. 187105237 (2021).
Google Scholar
Lovarelli, D., Bacenetti, J. & Guarino, M. A overview on dairy cattle farming: is precision livestock farming the compromise for an environmental, financial and social sustainable manufacturing? J. Clear. Prod. 262121409 (2020).
Google Scholar
Michie, C. et al. The web of issues enhancing animal welfare and farm operational effectivity. J. Dairy Res. 8720–27 (2020).
Google Scholar
Tzanidakis, C., Tzamaloukas, O., Simitzis, P. & Panagakis, P. Precision livestock farming purposes (plf) for grazing animals. Agriculture 13 (2023).
Banhazi, T. M. et al. Precision livestock farming: a world overview of scientific and industrial features. Int. J. Agric. Biol. Eng. 51–9 (2012).
Google Scholar
Hodgson, J. & Illius, AW The Ecology and Administration of Grazing Techniques (Wallingford (United Kingdom) CAB Worldwide, 1998).
Garcia, R., Aguilar, J., Toro, M., Pinto, A. & Rodriguez, P. A scientific literature overview on using machine studying in precision livestock farming. Comput. Electron. Agric. 179105826 (2020).
Google Scholar
Aquilani, C., Confessore, A., Bozzi, R., Sirtori, F. & Pugliese, C. Evaluation: precision livestock farming applied sciences in pasture-based livestock programs. Animal 16100429 (2022).
Google Scholar
Mahmud, M. S., Zahid, A., Das, A. Ok., Muzammil, M. & Khan, M. U. A scientific literature overview on deep studying purposes for precision cattle farming. Comput. Electron. Agric. 187106313 (2021).
Google Scholar
Riaboff, L. et al. Predicting livestock behaviour utilizing accelerometers: a scientific overview of processing methods for ruminant behaviour prediction from uncooked accelerometer information. Comput. Electron. Agric. 192106610 (2022).
Google Scholar
Lovarelli, D. et al. Growth of a brand new wearable 3d sensor node and modern open classification system for dairy cows’ habits. Animals 121447 (2022).
Google Scholar
Andriamandroso, A., Bindelle, J., Mercatoris, B. & Lebeau, F. A overview on using sensors to watch cattle jaw actions and habits when grazing. Biotechnol. Agron. Soc. Environ. 20 (2016).
Ferrero, M. et al. A full end-to-end deep method for detecting and classifying jaw actions from acoustic indicators in grazing cattle. Eng. Appl. Artif. Intell. 121106016 (2023).
Google Scholar
Li, G., Xiong, Y., Du, Q., Shi, Z. & Gates, R. S. Classifying ingestive habits of dairy cows by way of computerized sound recognition. Sensors 215231 (2021).
Google Scholar
Duan,G. et al. Quick-term feeding behaviour sound classification technique for sheep utilizing lstm networks. Int. J. Agric. Biol. Eng. 1443–54 (2021).
Google Scholar
Wang, Ok., Wu, P., Cui, H., Xuan, C. & Su, H. Identification and classification for sheep foraging habits based mostly on acoustic sign and deep studying. Comput. Electron. Agric. 187106275 (2021).
Google Scholar
Ungar, E. D. & Rutter, S. M. Classifying cattle jaw actions: evaluating iger behaviour recorder and acoustic methods. Appl. Anim. Behav. Sci. 9811–27 (2006).
Google Scholar
Schirmann, Ok., von Keyserlingk, M., Weary, D., Veira, D. & Heuwieser, W. Technical be aware: validation of a system for monitoring rumination in dairy cows. J. Dairy Sci. 926052–6055 (2009).
Google Scholar
Goldhawk, C., Schwartzkopf-Genswein, Ok. & Beauchemin, Ok. A. Technical Word: Validation of rumination collars for beef cattle. Journal of Animal Science 912858–2862 (2013).
Google Scholar
Martínez Rau, L., Chelotti, J. O., Vanrell, S. R. & Giovanini, L. L. Developments on real-time monitoring of grazing cattle feeding habits utilizing sound. In 2020 IEEE Worldwide Convention on Industrial Know-how (ICIT)771–776 (2020).
Hairspray, EA et al. Acoustic measurement of consumption and grazing behaviour of cattle. Grass Forage Sci. 5597–104 (2000).
Google Scholar
Galli, J. et al. Monitoring and evaluation of ingestive chewing sounds for prediction of herbage consumption price in grazing cattle. Animal 12973–982 (2018).
Google Scholar
Ritter, C., Mills, Ok. E., Weary, D. M. & von Keyserlingk, M. A. Views of western canadian dairy farmers on the way forward for farming. J. Dairy Sci. 10310273–10282 (2020).
Google Scholar
Cockburn, M. Evaluation: utility and potential dialogue of machine studying for the administration of dairy farms. Animals 10 (2020).
Vanrell, MR et al. Audio recordings dataset of grazing jaw actions in dairy cattle. Knowledge Transient 30105623 (2020).
Google Scholar
Jung, D.-H. et al. Deep learning-based cattle vocal classification mannequin and real-time livestock monitoring system with noise filtering. Animals 11 (2021).
Pandeya, Y. R., Bhattarai, B. & Lee, J. Visible object detector for cow sound occasion detection. IEEE Entry 8162625–162633 (2020).
Google Scholar
Deniz, N.N. et al. Embedded system for real-time monitoring of foraging habits of grazing cattle utilizing acoustic indicators. Comput. Electron. Agric. 138167–174 (2017).
Google Scholar
Martinez-Rau, LS, Weißbrich, M. & Payá-Vayá, G. A 4 m w low-power audio processor system for real-time jaw actions recognition in grazing cattle. Journal of Sign Processing Techniques 95407–424 (2023).
Google Scholar
Martinez-Rau, LS et al. A strong computational method for jaw motion detection and classification in grazing cattle utilizing acoustic indicators. Comput. Electron. Agric. 192106569 (2022).
Google Scholar
Chelotti, JO et al. A web based technique for estimating grazing and rumination bouts utilizing acoustic indicators in grazing cattle. Comput. Electron. Agric. 173105443 (2020).
Google Scholar
Chelotti, JO et al. Utilizing segment-based options of jaw actions to recognise foraging actions in grazing cattle. Biosystems Engineering 22969–84 (2023).
Google Scholar
Martinez-Rau, L. S., Adin, V., Giovanini, L. L., Oelmann, B. & Bader, S. Actual-time acoustic monitoring of foraging habits of grazing cattle utilizing low-power embedded gadgets. In 2023 IEEE Sensors Purposes Symposium (SAS)01–06 (2023).
Bishop, C. M. Sample Recognition and Machine Studying (Springer Verlag, 2006).
Meng, T., Jing, X., Yan, Z. & Pedrycz, W. A survey on machine studying for information fusion. Data Fusion 57115–129 (2020).
Google Scholar
Watt, L. et al. Differential rumination, consumption, and enteric methane manufacturing of dairy cows in a pasture-based computerized milking system. J. Dairy Sci. 987248–7263 (2015).
Google Scholar
Milone, D. H., Galli, J. R., Cangiano, C. A., Rufiner, H. L. & Laca, E. A. Computerized recognition of ingestive sounds of cattle based mostly on hidden markov fashions. Comput. Electron. Agric. 8751–55 (2012).
Google Scholar
Chelotti, JO et al. An actual-time algorithm for acoustic monitoring of ingestive habits of grazing cattle. Comput. Electron. Agric. 12764–75 (2016).
Google Scholar
Chelotti, J. O., Vanrell, S. R., Galli, J. R., Giovanini, L. L. & Rufiner, H. L. A sample recognition method for detecting and classifying jaw actions in grazing cattle. Comput. Electron. Agric. 14583–91 (2018).
Google Scholar
Galli, J. R. et al. Discriminative energy of acoustic options for jaw motion classification in cattle and sheep. Bioacoustics 29602–616 (2020).
Google Scholar
Martinez-Rau, LS. et al. Open dataset of acoustic recordings of foraging habits in dairy cows, figsharehttps://doi.org/10.6084/m9.figshare.c.6465301.v1 (2023).
Bosi , M. & Goldberg , RE MPEG-1 Audio265–313 (Springer US, Boston, MA, 2003).
Ungar, ED et al. The implications of compound chew–chunk jaw actions for chunk price in grazing cattle. Utilized Animal Behaviour Science 98183–195 (2006).
Google Scholar
Loizou, PC Speech Enhancement: Idea and Apply (CRC press, 2013).
Oppenheim, A. V., Willsky, A. S., Nawab, S. H. & Ding, J.-J. Indicators and Techniques, vol. 2 (Prentice corridor Higher Saddle River, NJ, 1997).
Cannam, C., Landone, C. & Sandler, M. Sonic visualiser: An open supply utility for viewing, analysing, and annotating music audio information. In Proceedings of the 18th ACM Worldwide Convention on Multimedia1467–1468 (2010).
Model, A., Allen, L., Altman, M., Hlava, M. & Scott, J. Past authorship: attribution, contribution, collaboration, and credit score. Be taught. Publ. 28151–155 (2015).
Google Scholar
