Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull

The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by includ...

Full description

Saved in:

Bibliographic Details
Published in:	Artificial intelligence in agriculture Vol. 14; pp. 86 - 98
Main Authors:	Biszkup, Miklós, Vásárhelyi, Gábor, Setiawan, Nuri Nurlaila, Márton, Aliz, Szentes, Szilárd, Balogh, Petra, Babay-Török, Barbara, Pajor, Gábor, Drexler, Dóra
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2024 KeAi Communications Co., Ltd
Subjects:	agriculture algorithms artificial intelligence bulls cameras Cattle Charolais Complex behaviour head Machine learning Motion sensors Multi-dimensional Parallel pedometer PLF prediction RumiWatch RumiWatch Complex behaviour Multi-dimensional Cattle Machine learning PLF Parallel Motion sensors
ISSN:	2589-7217, 2589-7217
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy. •Motion sensors enables farmers to monitor their cattle's welfare more efficiently.•We enriched the algorithm of motion sensors with complex movements for more accuracy.•We used two machine learning classifications for complex movement recognition.•All forms of movement were successfully recognized with a rate of 90 % and above.
AbstractList	The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy. The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While most studies work with one dimensional output with disjunct behaviour categories, more accurate prediction can still be achieved by including complex movements and enriching the sensor algorithm to detect multi-dimensional movements, i.e., more than one movement occurring simultaneously. This paper presents such a machine-learning method for analysing overlapping independent movements. The output of the method consists of automatically recognized complex behaviour patterns that can be used for measuring animal welfare, predicting calving, or detecting early signs of diseases. This study combines automated motion sensors (i.e., halter and pedometer) for ruminants known as RumiWatch mounted on a Charolais fattening bull and camera observation. Fourteen types of complex movements were identified, i.e., defecating-urinating, eating, drinking, getting up, head movement, licking, lying down, lying, playing-aggression, rubbing, ruminating, sleeping, standing, and stepping. As multiple parallel binary classificators were used, the system was able to recognize parallel behavioural patterns with high fidelity. Two types of machine learning, i.e., Support Vector Classification (SVC) and RandomForest were used to recognize different general and non-general forms of movement. Results from these two supervised learning systems were compared. A continuous forty-eight hours of video were annotated to train the systems and validate their predictions. The success rate of both classifiers in recognizing special movements from both sensors or separately in different settings (i.e., window and padding) was examined. Although the two classifiers produced different results, the ideal settings showed that all forms of movement in the subject animal were successfully recognized with high accuracy. More studies using more individual animals and different ruminants would increase our knowledge on enhancing the system's performance and accuracy. •Motion sensors enables farmers to monitor their cattle's welfare more efficiently.•We enriched the algorithm of motion sensors with complex movements for more accuracy.•We used two machine learning classifications for complex movement recognition.•All forms of movement were successfully recognized with a rate of 90 % and above.
Author	Balogh, Petra Márton, Aliz Pajor, Gábor Biszkup, Miklós Szentes, Szilárd Babay-Török, Barbara Setiawan, Nuri Nurlaila Drexler, Dóra Vásárhelyi, Gábor
Author_xml	– sequence: 1 givenname: Miklós surname: Biszkup fullname: Biszkup, Miklós email: miklos.biszkup@biokutatas.hu organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 2 givenname: Gábor surname: Vásárhelyi fullname: Vásárhelyi, Gábor organization: CollMot Robotics Ltd., Budapest, Hungary – sequence: 3 givenname: Nuri Nurlaila surname: Setiawan fullname: Setiawan, Nuri Nurlaila organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 4 givenname: Aliz surname: Márton fullname: Márton, Aliz organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 5 givenname: Szilárd surname: Szentes fullname: Szentes, Szilárd organization: University of Veterinary Medicine, Animal Breeding, Nutrition and Laboratory Animal Science Department, Budapest, Hungary – sequence: 6 givenname: Petra surname: Balogh fullname: Balogh, Petra organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 7 givenname: Barbara surname: Babay-Török fullname: Babay-Török, Barbara organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 8 givenname: Gábor surname: Pajor fullname: Pajor, Gábor organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary – sequence: 9 givenname: Dóra surname: Drexler fullname: Drexler, Dóra organization: Hungarian Research Institute of Organic Agriculture, Budapest, Hungary
BookMark	eNp9kc9u1DAQxiNUJErpC3DykUuC7fxHXNBSoFIlDsDZGjuT3Vk5drGdlfoWPDJOFyTEoRfbY32_b-z5XhYXzjssiteCV4KL7u2xAiKoJJdNJUTFuXxWXMp2GMteiv7in_OL4jrGI8-KYSPHy-LXR0xoEmiylB6Yn9my2kTlRAu6SN6BZYs_Ya4SAzcxjQc4kV8DI8cMpGSRrZHcnsUM-MAmSPCoXMAcyCGzCMFtArB7HygdlviOfUvrlNs5Bmx3gOAtUGR6tfZV8XwGG_H6z35V_Ph08333pbz7-vl29-GuNE1Tp7LWYz3oDhuj517PWjbQj5OAuqlHlKjzmmsUY9t3I3SDHgYx8aHTWretAaivituz7-ThqO4DLRAelAdSjxc-7BWERMaiQpRTPw0CZe7XCq3roamzd7Y1hvdN9npz9roP_ueKMamFokFrwaFfo6plI3lT93yTDmepCT7GgLMylCDlQacAZJXgaktGHdWWqdoyVUKonFhG5X_o31c_Cb0_Q5hneSIMKhpCZ3CikHPPn6Wn8N-qf7-u
CitedBy_id	crossref_primary_10_1016_j_compag_2025_110464 crossref_primary_10_1016_j_rineng_2025_107391
Cites_doi	10.2527/af.2017.0102 10.1016/j.prevetmed.2010.06.013 10.1016/j.patrec.2020.07.042 10.1016/j.compag.2019.105179 10.1016/j.livsci.2019.02.003 10.1016/j.compag.2018.12.023 10.1016/j.livsci.2021.104488 10.1016/j.applanim.2021.105402 10.1016/j.compag.2024.108894 10.1016/j.sbsr.2020.100367 10.3389/fvets.2021.634338 10.1080/1745039X.2020.1721260 10.1371/journal.pone.0077814 10.3168/jds.2018-15701 10.2527/jas1970.302203x 10.1016/j.jveb.2017.02.002 10.1111/j.1365-2494.1950.tb01285.x 10.1086/402403 10.1017/S0022029917000668 10.1016/j.patrec.2005.10.010 10.3390/dairy2010010 10.3389/fvets.2019.00289 10.1111/2041-210X.12584 10.1016/j.physleta.2007.03.050 10.3389/fanim.2021.737213 10.3168/jds.2018-15711 10.1016/j.compag.2023.107945 10.1016/j.compag.2017.02.021 10.3390/ani11123438 10.3390/ani12091060 10.1016/j.anireprosci.2017.10.003 10.1016/j.vetpar.2012.10.023 10.3390/ani11102779 10.1016/S0376-6357(02)00081-5 10.3168/jds.2009-2326 10.3168/jds.S0022-0302(04)73293-2 10.1016/j.applanim.2018.12.003
ContentType	Journal Article
Copyright	2024 The Authors
Copyright_xml	– notice: 2024 The Authors
DBID	6I. AAFTH AAYXX CITATION 7S9 L.6 DOA
DOI	10.1016/j.aiia.2024.11.002
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef AGRICOLA AGRICOLA - Academic DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website
DeliveryMethod	fulltext_linktorsrc
Discipline	Agriculture
EISSN	2589-7217
EndPage	98
ExternalDocumentID	oai_doaj_org_article_ee2d7d81e26e451bb3843de1a68cc074 10_1016_j_aiia_2024_11_002 S2589721724000412
GroupedDBID	0R~ 6I. AAEDW AAFTH AAHBH AALRI AAXUO ACLIJ ADVLN AEXQZ AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ EBS EJD FDB GROUPED_DOAJ M41 NCXOZ OK1 ROL SSZ AAYWO AAYXX CITATION 7S9 L.6
ID	FETCH-LOGICAL-c443t-3b938b6e4cbf7bfb24a79d1a3439e2eb39e79de195769a68b881d086bbb55caa3
IEDL.DBID	DOA
ISICitedReferencesCount	5
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001415909200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2589-7217
IngestDate	Fri Oct 03 12:48:23 EDT 2025 Fri Aug 22 20:24:10 EDT 2025 Sat Nov 29 02:42:59 EST 2025 Tue Nov 18 22:01:43 EST 2025 Sat Dec 21 16:00:45 EST 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	RumiWatch Complex behaviour Multi-dimensional Cattle Machine learning PLF Parallel Motion sensors
Language	English
License	This is an open access article under the CC BY-NC-ND license.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c443t-3b938b6e4cbf7bfb24a79d1a3439e2eb39e79de195769a68b881d086bbb55caa3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
OpenAccessLink	https://doaj.org/article/ee2d7d81e26e451bb3843de1a68cc074
PQID	3242043704
PQPubID	24069
PageCount	13
ParticipantIDs	doaj_primary_oai_doaj_org_article_ee2d7d81e26e451bb3843de1a68cc074 proquest_miscellaneous_3242043704 crossref_citationtrail_10_1016_j_aiia_2024_11_002 crossref_primary_10_1016_j_aiia_2024_11_002 elsevier_sciencedirect_doi_10_1016_j_aiia_2024_11_002
PublicationCentury	2000
PublicationDate	2024-12-01
PublicationDateYYYYMMDD	2024-12-01
PublicationDate_xml	– month: 12 year: 2024 text: 2024-12-01 day: 01
PublicationDecade	2020
PublicationTitle	Artificial intelligence in agriculture
PublicationYear	2024
Publisher	Elsevier B.V KeAi Communications Co., Ltd
Publisher_xml	– name: Elsevier B.V – name: KeAi Communications Co., Ltd
References	Fraser, Broom (bb0075) 1997 Wang, Pei, Zou, Cheung, He (bb0240) 2007; 368 Gerencsér, Vásárhelyi, Nagy, Vicsek, Miklósi (bb0095) 2013; 8 Barrell (bb0010) 2019; 6 Gary, Stterritt, Hale (bb0085) 1970; 30 Ting (bb0215) 2010 Raynor, Derner, Soder, Augustine (bb0165) 2021; 242 Peng, Kondo, Fujiura, Suzuki, Wulandari, Itoyama (bb0150) 2019; 157 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (bb0145) 2011; 12 Jin, Shu, Hu, Jiang, Yan, Qi, Wang, Guo (bb0110) 2024; 220 Zehner, Niederhauser, Nydegger, Grothmann, Keller, Hoch, Haeussermann, Schick (bb0275) 2012 Banhazi, Lehr, Black, Crabtree, Schofield, Tscharke, Berckman (bb0005) 2012 Berckmans (bb0020) 2017; 7 Tribe (bb0225) 1950; 5 von Keyserlingk, Rushen, de Passillé, Weary (bb0235) 2009 D’Andrea, Guccione, Alsaaod, Deiss, Di Loria, Steiner, Ciaramella (bb0035) 2017; 84 DeVries, Von Keyserlingk, Weary (bb0050) 2004; 87 Eibl-Eibesfeldt, Kramer (bb0055) 1958; 33 Désiré, Boissy, Veissier (bb0045) 2002; 60 Lowe, Sutherland, Waas, Schaefer, Cox, Stewart (bb0130) 2019; 102 Bochner, Chandrasekharan (bb0025) 1949 Fan, Liu, Li, Zhao (bb0065) 2023; 211 Li, Shu, Bindelle, Xu, Zhang, Jin, Guo, Wang (bb0125) 2022; 12 Riaboff, Poggi, Madouasse, Couvreur, Aubin, Bédère, Goumand, Chauvin, Plantier (bb0175) 2020; 169 Werner, Schulte, Dickhöfer (bb0260) 2019 (bb0190) 2010 Minegishi, Heins, Pereira (bb0135) 2019; 221 Zehner, Umstätter, Niederhauser, Schick (bb0280) 2017; 136 Ferrari, Piccinini, Silva, Exadaktylos, Berckmans, Guarino (bb0281) 2010; 96 Lee, Seo (bb0115) 2021; 11 (bb0185) 2010 Wottlin, Carstens, Kayser, Pinchak, Pinedo, Richeson (bb0270) 2021; 248 Rau, Chelotti, Vanrell, Giovanini (bb0160) 2020 Werner, Zehner, Umstatter, Nydegger, Schick (bb0255) 2014 Islam, Lomax, Doughty, Islam, Jay, Thomson, Clark (bb0105) 2021; 2 Steinmetz, von Soosten, Hummel, Meyer, Dänicke (bb0195) 2020; 74 Guidotti, Monreale, Ruggieri, Turini, Giannotti, Pedreschi (bb0100) 2018; 51 Reid (bb0170) 2010 Watanabe, Bernardes, Romanzini, Teobaldo, Reis, Munari, Braga, Brito (bb0250) 2021; 11 Gengler (bb0090) 2019; 102 Cerqueira, Araújo, Blanco-Penedo, Cantalapiedra, Sørensen, Niza-Ribeiro (bb0030) 2017; 19 Fawcett (bb0070) 2006; 27 Ting (bb0220) 2010 Benaissa, Tuyttens, Plets, Cattrysse, Martens, Vandaele, Joseph, Sonck (bb0015) 2019; 211 Wang, Fan, Wang (bb0245) 2021; 141 Ting (bb0210) 2010 Neethirajan (bb0140) 2020 de La Torre Capitan, Anglard, Barbet, Le Morvan, Agabriel, Baumont (bb0040) 2016 R Core Team (bb0155) 2023 Stygar, Gómez, Berteselli, Dalla Costa, Canali, Niemi, Llonch, Pastell (bb0200) 2021 Wickham (bb0265) 2009 Tsoumakas, Katakis (bb0230) 2006 (bb0180) 2010 Szyszka, Tolkamp, Edwards, Kyriazakis (bb0205) 2013; 193 Li, Cheng, Cullen (bb0120) 2021; 2 Fadul, Bogdahn, Alsaaod, Hüsler, Starke, Steiner, Hirsbrunner (bb0060) 2017; 187 Friard, Gamba (bb0080) 2016; 7 Cerqueira (10.1016/j.aiia.2024.11.002_bb0030) 2017; 19 Stygar (10.1016/j.aiia.2024.11.002_bb0200) 2021 Reid (10.1016/j.aiia.2024.11.002_bb0170) 2010 Ting (10.1016/j.aiia.2024.11.002_bb0210) 2010 Ting (10.1016/j.aiia.2024.11.002_bb0220) 2010 Fawcett (10.1016/j.aiia.2024.11.002_bb0070) 2006; 27 Islam (10.1016/j.aiia.2024.11.002_bb0105) 2021; 2 Jin (10.1016/j.aiia.2024.11.002_bb0110) 2024; 220 D’Andrea (10.1016/j.aiia.2024.11.002_bb0035) 2017; 84 Fraser (10.1016/j.aiia.2024.11.002_bb0075) 1997 Gerencsér (10.1016/j.aiia.2024.11.002_bb0095) 2013; 8 Benaissa (10.1016/j.aiia.2024.11.002_bb0015) 2019; 211 (10.1016/j.aiia.2024.11.002_bb0180) 2010 Gengler (10.1016/j.aiia.2024.11.002_bb0090) 2019; 102 Watanabe (10.1016/j.aiia.2024.11.002_bb0250) 2021; 11 Fan (10.1016/j.aiia.2024.11.002_bb0065) 2023; 211 Szyszka (10.1016/j.aiia.2024.11.002_bb0205) 2013; 193 Peng (10.1016/j.aiia.2024.11.002_bb0150) 2019; 157 Wang (10.1016/j.aiia.2024.11.002_bb0245) 2021; 141 Désiré (10.1016/j.aiia.2024.11.002_bb0045) 2002; 60 Zehner (10.1016/j.aiia.2024.11.002_bb0275) 2012 Bochner (10.1016/j.aiia.2024.11.002_bb0025) 1949 Tribe (10.1016/j.aiia.2024.11.002_bb0225) 1950; 5 Lee (10.1016/j.aiia.2024.11.002_bb0115) 2021; 11 Tsoumakas (10.1016/j.aiia.2024.11.002_bb0230) 2006 Friard (10.1016/j.aiia.2024.11.002_bb0080) 2016; 7 Fadul (10.1016/j.aiia.2024.11.002_bb0060) 2017; 187 Ferrari (10.1016/j.aiia.2024.11.002_bb0281) 2010; 96 Wang (10.1016/j.aiia.2024.11.002_bb0240) 2007; 368 Lowe (10.1016/j.aiia.2024.11.002_bb0130) 2019; 102 Gary (10.1016/j.aiia.2024.11.002_bb0085) 1970; 30 R Core Team (10.1016/j.aiia.2024.11.002_bb0155) 2023 Li (10.1016/j.aiia.2024.11.002_bb0120) 2021; 2 Ting (10.1016/j.aiia.2024.11.002_bb0215) 2010 de La Torre Capitan (10.1016/j.aiia.2024.11.002_bb0040) 2016 Zehner (10.1016/j.aiia.2024.11.002_bb0280) 2017; 136 Steinmetz (10.1016/j.aiia.2024.11.002_bb0195) 2020; 74 Riaboff (10.1016/j.aiia.2024.11.002_bb0175) 2020; 169 (10.1016/j.aiia.2024.11.002_bb0185) 2010 Banhazi (10.1016/j.aiia.2024.11.002_bb0005) 2012 Berckmans (10.1016/j.aiia.2024.11.002_bb0020) 2017; 7 Guidotti (10.1016/j.aiia.2024.11.002_bb0100) 2018; 51 Raynor (10.1016/j.aiia.2024.11.002_bb0165) 2021; 242 Li (10.1016/j.aiia.2024.11.002_bb0125) 2022; 12 Rau (10.1016/j.aiia.2024.11.002_bb0160) 2020 Minegishi (10.1016/j.aiia.2024.11.002_bb0135) 2019; 221 Pedregosa (10.1016/j.aiia.2024.11.002_bb0145) 2011; 12 Neethirajan (10.1016/j.aiia.2024.11.002_bb0140) 2020 (10.1016/j.aiia.2024.11.002_bb0190) 2010 Werner (10.1016/j.aiia.2024.11.002_bb0255) 2014 Wickham (10.1016/j.aiia.2024.11.002_bb0265) 2009 Werner (10.1016/j.aiia.2024.11.002_bb0260) 2019 Wottlin (10.1016/j.aiia.2024.11.002_bb0270) 2021; 248 Barrell (10.1016/j.aiia.2024.11.002_bb0010) 2019; 6 DeVries (10.1016/j.aiia.2024.11.002_bb0050) 2004; 87 Eibl-Eibesfeldt (10.1016/j.aiia.2024.11.002_bb0055) 1958; 33 von Keyserlingk (10.1016/j.aiia.2024.11.002_bb0235) 2009
References_xml	– volume: 2 start-page: 104 year: 2021 end-page: 111 ident: bb0120 article-title: Validation and use of the RumiWatch noseband sensor for monitoring grazing Behaviours of lactating dairy cows publication-title: Dairy – start-page: 781 year: 2010 ident: bb0210 article-title: Precision and recall publication-title: Encyclopedia of Machine Learning – volume: 211 start-page: 9 year: 2019 end-page: 16 ident: bb0015 article-title: Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers publication-title: Appl. Anim. Behav. Sci. – volume: 11 year: 2021 ident: bb0250 article-title: Strategy to predict high and low frequency behaviors using triaxial accelerometers in grazing of beef cattle publication-title: Animals – volume: 7 start-page: 6 year: 2017 end-page: 11 ident: bb0020 article-title: General introduction to precision livestock farming publication-title: Anim. Front. – year: 2012 ident: bb0275 article-title: Validation of a new health monitoring system (RumiWatch) for combined automatic measurement of rumination, feed intake, water intake and locomotion in dairy cows publication-title: Proceedings of International Conference of Agricultural Engineering CIGR-Ageng – volume: 60 start-page: 165 year: 2002 end-page: 180 ident: bb0045 article-title: Emotions in farm animals: a new approach to animal welfare in applied ethology publication-title: Behav. Process. – volume: 221 start-page: 144 year: 2019 end-page: 154 ident: bb0135 article-title: Peri-estrus activity and rumination time and its application to estrus prediction: evidence from dairy herds under organic grazing and low-input conventional production publication-title: Livest. Sci. – volume: 141 start-page: 61 year: 2021 end-page: 67 ident: bb0245 article-title: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning publication-title: Pattern Recogn. Lett. – start-page: 1 year: 2016 end-page: 15 ident: bb0040 article-title: Are physical and feeding activities at pasture impacted by cattle breed and previous feeding restriction? publication-title: In: 67. Annual Meeting of the European Association for Animal Production (EAAP) – volume: 368 start-page: 38 year: 2007 end-page: 47 ident: bb0240 article-title: The asymptotic deterministic randomness publication-title: Phys. Lett. A – volume: 136 start-page: 31 year: 2017 end-page: 41 ident: bb0280 article-title: System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows publication-title: Comput. Electron. Agric. – year: 2020 ident: bb0140 article-title: The role of sensors, big data and machine learning in modern animal farming publication-title: Sens. Biosensing Res. – volume: 187 start-page: 37 year: 2017 end-page: 46 ident: bb0060 article-title: Prediction of calving time in dairy cattle publication-title: Anim. Reprod. Sci. – volume: 248 year: 2021 ident: bb0270 article-title: Efficacy of statistical process control procedures to monitor deviations in physical behavior for preclinical detection of bovine respiratory disease in feedlot cattle publication-title: Livest. Sci. – year: 2010 ident: bb0190 publication-title: F1-Measure. Encyclopedia of Machine Learning – volume: 211 year: 2023 ident: bb0065 article-title: Bottom-up cattle pose estimation via concise multi-branch network publication-title: Comput. Electron. Agric. – year: 2014 ident: bb0255 article-title: Application of a noseband pressure sensor for automatic measurement of horses’ chewing activity: A pilot study publication-title: International Conference of Agricultural Engineering – year: 2021 ident: bb0200 article-title: A systematic review on commercially available and validated sensor Technologies for Welfare Assessment of dairy cattle publication-title: Front. Vet. Sci. – volume: 19 start-page: 72 year: 2017 end-page: 77 ident: bb0030 article-title: Relationship between stepping and kicking behavior and milking management in dairy cattle herds publication-title: J. Vet. Behav. – volume: 193 start-page: 214 year: 2013 end-page: 222 ident: bb0205 article-title: Do the changes in the behaviours of cattle during parasitism with Ostertagia ostertagi have a potential diagnostic value? publication-title: Vet. Parasitol. – volume: 51 year: 2018 ident: bb0100 article-title: A survey of methods for explaining black box models publication-title: ACM Comput. Surv. – year: 2009 ident: bb0235 article-title: Invited review: the welfare of dairy cattle-key concepts and the role of science publication-title: J. Dairy Sci. – volume: 87 start-page: 1432 year: 2004 end-page: 1438 ident: bb0050 article-title: Effect of feeding space on the inter-cow distance, aggression, and feeding behavior of free-stall housed lactating dairy cows publication-title: J. Dairy Sci. – volume: 242 year: 2021 ident: bb0165 article-title: Noseband sensor validation and behavioural indicators for assessing beef cattle grazing on extensive pastures publication-title: Appl. Anim. Behav. Sci. – volume: 102 start-page: 5389 year: 2019 end-page: 5402 ident: bb0130 article-title: Physiological and behavioral responses as indicators for early disease detection in dairy calves publication-title: J. Dairy Sci. – volume: 11 start-page: 2779 year: 2021 ident: bb0115 article-title: Wearable wireless biosensor technology for monitoring cattle: a review publication-title: Animals – year: 2010 ident: bb0185 publication-title: Noise. Encyclopedia of Machine Learning – start-page: 209 year: 2010 ident: bb0220 article-title: Confusion matrix publication-title: Encyclopedia of Machine Learning – start-page: 780 year: 2010 ident: bb0215 article-title: Precision publication-title: Encyclopedia of Machine Learning – year: 2006 ident: bb0230 article-title: Multi-Label Classification: An Overview International Journal of Data Warehousing and Mining. The Label Powerset Algorithm is called PT3 3 – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bb0145 article-title: Scikit-learn: machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 84 start-page: 391 year: 2017 end-page: 394 ident: bb0035 article-title: Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo publication-title: J. Dairy Res. – volume: 169 year: 2020 ident: bb0175 article-title: Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data publication-title: Comput. Electron. Agric. – year: 2009 ident: bb0265 article-title: ggplot2: Elegant Graphics for Data Analysis – volume: 30 start-page: 203 year: 1970 end-page: 206 ident: bb0085 article-title: Behaviour of Charolais cattle on pasture publication-title: J. Anim. Sci. – volume: 6 year: 2019 ident: bb0010 article-title: An appraisal of methods for measuring welfare of grazing ruminants publication-title: Front. Vet. Sci. – volume: 5 start-page: 209 year: 1950 end-page: 224 ident: bb0225 article-title: The behaviour of the grazing animal: a critical reiew of present knowledge publication-title: Grass Forage Sci. – year: 2023 ident: bb0155 article-title: R: A language and environment for statistical computing publication-title: R: A Language and Environment for Statistical Computing – start-page: 447 year: 2010 end-page: 454 ident: bb0170 article-title: Generalization bounds publication-title: Encyclopedia of Machine Learning – volume: 74 start-page: 164 year: 2020 end-page: 172 ident: bb0195 article-title: Validation of the RumiWatch converter V0.7.4.5 classification accuracy for the automatic monitoring of behavioural characteristics in dairy cows publication-title: Arch. Anim. Nutr. – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: bb0070 article-title: An introduction to ROC analysis publication-title: Pattern Recogn. Lett. – start-page: 78 year: 2019 ident: bb0260 article-title: Pilot study to assess the accuracy of the RumiWatch noseband sensor for detecting grazing behaviour of sheep publication-title: Precision Livestock Farming. Cork, Ireland – year: 1949 ident: bb0025 article-title: Fourier transforms – year: 2010 ident: bb0180 publication-title: Supervised Learning. Encyclopedia of Machine Learning – volume: 12 start-page: 1060 year: 2022 ident: bb0125 article-title: Classification and analysis of multiple cattle unitary behaviors and movements based on machine learning methods publication-title: Animals – start-page: 771 year: 2020 end-page: 776 ident: bb0160 article-title: Developments on real-time monitoring of grazing cattle feeding behavior using sound publication-title: 2020 IEEE International Conference on Industrial Technology (ICIT) – volume: 102 start-page: 5756 year: 2019 end-page: 5763 ident: bb0090 article-title: Symposium review: challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation publication-title: J. Dairy Sci. – volume: 157 start-page: 247 year: 2019 end-page: 253 ident: bb0150 article-title: Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units publication-title: Comput. Electron. Agric. – year: 1997 ident: bb0075 article-title: Farm Animal Behaviour and Welfa3re – volume: 2 year: 2021 ident: bb0105 article-title: Automated monitoring of cattle heat stress and its mitigation publication-title: Front. Anim. Sci. – volume: 7 start-page: 1325 year: 2016 end-page: 1330 ident: bb0080 article-title: BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations publication-title: Methods Ecol. Evol. – year: 2012 ident: bb0005 article-title: Precision Livestock Farming: An International Review of Scientific and Commercial Aspects 5 – volume: 8 year: 2013 ident: bb0095 article-title: Identification of behaviour in freely moving dogs (Canis familiaris) using inertial sensors publication-title: PLoS One – volume: 220 year: 2024 ident: bb0110 article-title: Behavior classification and spatiotemporal analysis of grazing sheep using deep learning publication-title: Comput. Electron. Agric. – volume: 96 start-page: 276 year: 2010 end-page: 280 ident: bb0281 article-title: Cough sound description in relation to respiratory diseases in dairy calves publication-title: Prev. Vet. Med. – volume: 33 year: 1958 ident: bb0055 article-title: Ethology, the comparative study of animal behaviour publication-title: Q. Rev. Biol. – volume: 7 start-page: 6 year: 2017 ident: 10.1016/j.aiia.2024.11.002_bb0020 article-title: General introduction to precision livestock farming publication-title: Anim. Front. doi: 10.2527/af.2017.0102 – volume: 96 start-page: 276 issue: 3–4 year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0281 article-title: Cough sound description in relation to respiratory diseases in dairy calves publication-title: Prev. Vet. Med. doi: 10.1016/j.prevetmed.2010.06.013 – volume: 141 start-page: 61 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0245 article-title: Comparative analysis of image classification algorithms based on traditional machine learning and deep learning publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2020.07.042 – volume: 169 year: 2020 ident: 10.1016/j.aiia.2024.11.002_bb0175 article-title: Development of a methodological framework for a robust prediction of the main behaviours of dairy cows using a combination of machine learning algorithms on accelerometer data publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.105179 – volume: 221 start-page: 144 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0135 article-title: Peri-estrus activity and rumination time and its application to estrus prediction: evidence from dairy herds under organic grazing and low-input conventional production publication-title: Livest. Sci. doi: 10.1016/j.livsci.2019.02.003 – year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0190 – volume: 157 start-page: 247 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0150 article-title: Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.12.023 – year: 2006 ident: 10.1016/j.aiia.2024.11.002_bb0230 – volume: 248 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0270 article-title: Efficacy of statistical process control procedures to monitor deviations in physical behavior for preclinical detection of bovine respiratory disease in feedlot cattle publication-title: Livest. Sci. doi: 10.1016/j.livsci.2021.104488 – volume: 242 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0165 article-title: Noseband sensor validation and behavioural indicators for assessing beef cattle grazing on extensive pastures publication-title: Appl. Anim. Behav. Sci. doi: 10.1016/j.applanim.2021.105402 – start-page: 771 year: 2020 ident: 10.1016/j.aiia.2024.11.002_bb0160 article-title: Developments on real-time monitoring of grazing cattle feeding behavior using sound – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.aiia.2024.11.002_bb0145 article-title: Scikit-learn: machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 220 year: 2024 ident: 10.1016/j.aiia.2024.11.002_bb0110 article-title: Behavior classification and spatiotemporal analysis of grazing sheep using deep learning publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2024.108894 – year: 2020 ident: 10.1016/j.aiia.2024.11.002_bb0140 article-title: The role of sensors, big data and machine learning in modern animal farming publication-title: Sens. Biosensing Res. doi: 10.1016/j.sbsr.2020.100367 – year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0200 article-title: A systematic review on commercially available and validated sensor Technologies for Welfare Assessment of dairy cattle publication-title: Front. Vet. Sci. doi: 10.3389/fvets.2021.634338 – year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0180 – year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0185 – volume: 74 start-page: 164 year: 2020 ident: 10.1016/j.aiia.2024.11.002_bb0195 article-title: Validation of the RumiWatch converter V0.7.4.5 classification accuracy for the automatic monitoring of behavioural characteristics in dairy cows publication-title: Arch. Anim. Nutr. doi: 10.1080/1745039X.2020.1721260 – volume: 8 year: 2013 ident: 10.1016/j.aiia.2024.11.002_bb0095 article-title: Identification of behaviour in freely moving dogs (Canis familiaris) using inertial sensors publication-title: PLoS One doi: 10.1371/journal.pone.0077814 – volume: 102 start-page: 5389 issue: 6 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0130 article-title: Physiological and behavioral responses as indicators for early disease detection in dairy calves publication-title: J. Dairy Sci. doi: 10.3168/jds.2018-15701 – year: 1997 ident: 10.1016/j.aiia.2024.11.002_bb0075 – volume: 30 start-page: 203 year: 1970 ident: 10.1016/j.aiia.2024.11.002_bb0085 article-title: Behaviour of Charolais cattle on pasture publication-title: J. Anim. Sci. doi: 10.2527/jas1970.302203x – volume: 19 start-page: 72 year: 2017 ident: 10.1016/j.aiia.2024.11.002_bb0030 article-title: Relationship between stepping and kicking behavior and milking management in dairy cattle herds publication-title: J. Vet. Behav. doi: 10.1016/j.jveb.2017.02.002 – volume: 5 start-page: 209 year: 1950 ident: 10.1016/j.aiia.2024.11.002_bb0225 article-title: The behaviour of the grazing animal: a critical reiew of present knowledge publication-title: Grass Forage Sci. doi: 10.1111/j.1365-2494.1950.tb01285.x – year: 2023 ident: 10.1016/j.aiia.2024.11.002_bb0155 article-title: R: A language and environment for statistical computing – start-page: 78 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0260 article-title: Pilot study to assess the accuracy of the RumiWatch noseband sensor for detecting grazing behaviour of sheep – volume: 33 year: 1958 ident: 10.1016/j.aiia.2024.11.002_bb0055 article-title: Ethology, the comparative study of animal behaviour publication-title: Q. Rev. Biol. doi: 10.1086/402403 – volume: 84 start-page: 391 year: 2017 ident: 10.1016/j.aiia.2024.11.002_bb0035 article-title: Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo publication-title: J. Dairy Res. doi: 10.1017/S0022029917000668 – volume: 27 start-page: 861 year: 2006 ident: 10.1016/j.aiia.2024.11.002_bb0070 article-title: An introduction to ROC analysis publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 2 start-page: 104 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0120 article-title: Validation and use of the RumiWatch noseband sensor for monitoring grazing Behaviours of lactating dairy cows publication-title: Dairy doi: 10.3390/dairy2010010 – volume: 6 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0010 article-title: An appraisal of methods for measuring welfare of grazing ruminants publication-title: Front. Vet. Sci. doi: 10.3389/fvets.2019.00289 – volume: 7 start-page: 1325 year: 2016 ident: 10.1016/j.aiia.2024.11.002_bb0080 article-title: BORIS: a free, versatile open-source event-logging software for video/audio coding and live observations publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.12584 – volume: 368 start-page: 38 issue: 1–2 year: 2007 ident: 10.1016/j.aiia.2024.11.002_bb0240 article-title: The asymptotic deterministic randomness publication-title: Phys. Lett. A doi: 10.1016/j.physleta.2007.03.050 – year: 2012 ident: 10.1016/j.aiia.2024.11.002_bb0275 article-title: Validation of a new health monitoring system (RumiWatch) for combined automatic measurement of rumination, feed intake, water intake and locomotion in dairy cows – volume: 2 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0105 article-title: Automated monitoring of cattle heat stress and its mitigation publication-title: Front. Anim. Sci. doi: 10.3389/fanim.2021.737213 – volume: 102 start-page: 5756 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0090 article-title: Symposium review: challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation publication-title: J. Dairy Sci. doi: 10.3168/jds.2018-15711 – start-page: 781 year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0210 article-title: Precision and recall – volume: 211 year: 2023 ident: 10.1016/j.aiia.2024.11.002_bb0065 article-title: Bottom-up cattle pose estimation via concise multi-branch network publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2023.107945 – volume: 136 start-page: 31 year: 2017 ident: 10.1016/j.aiia.2024.11.002_bb0280 article-title: System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2017.02.021 – volume: 11 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0250 article-title: Strategy to predict high and low frequency behaviors using triaxial accelerometers in grazing of beef cattle publication-title: Animals doi: 10.3390/ani11123438 – start-page: 447 year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0170 article-title: Generalization bounds – start-page: 1 year: 2016 ident: 10.1016/j.aiia.2024.11.002_bb0040 article-title: Are physical and feeding activities at pasture impacted by cattle breed and previous feeding restriction? – volume: 12 start-page: 1060 issue: 9 year: 2022 ident: 10.1016/j.aiia.2024.11.002_bb0125 article-title: Classification and analysis of multiple cattle unitary behaviors and movements based on machine learning methods publication-title: Animals doi: 10.3390/ani12091060 – year: 2014 ident: 10.1016/j.aiia.2024.11.002_bb0255 article-title: Application of a noseband pressure sensor for automatic measurement of horses’ chewing activity: A pilot study – volume: 187 start-page: 37 year: 2017 ident: 10.1016/j.aiia.2024.11.002_bb0060 article-title: Prediction of calving time in dairy cattle publication-title: Anim. Reprod. Sci. doi: 10.1016/j.anireprosci.2017.10.003 – volume: 51 year: 2018 ident: 10.1016/j.aiia.2024.11.002_bb0100 article-title: A survey of methods for explaining black box models publication-title: ACM Comput. Surv. – volume: 193 start-page: 214 issue: 1–3 year: 2013 ident: 10.1016/j.aiia.2024.11.002_bb0205 article-title: Do the changes in the behaviours of cattle during parasitism with Ostertagia ostertagi have a potential diagnostic value? publication-title: Vet. Parasitol. doi: 10.1016/j.vetpar.2012.10.023 – volume: 11 start-page: 2779 issue: 10 year: 2021 ident: 10.1016/j.aiia.2024.11.002_bb0115 article-title: Wearable wireless biosensor technology for monitoring cattle: a review publication-title: Animals doi: 10.3390/ani11102779 – year: 1949 ident: 10.1016/j.aiia.2024.11.002_bb0025 – start-page: 209 year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0220 article-title: Confusion matrix – year: 2009 ident: 10.1016/j.aiia.2024.11.002_bb0265 – volume: 60 start-page: 165 year: 2002 ident: 10.1016/j.aiia.2024.11.002_bb0045 article-title: Emotions in farm animals: a new approach to animal welfare in applied ethology publication-title: Behav. Process. doi: 10.1016/S0376-6357(02)00081-5 – year: 2009 ident: 10.1016/j.aiia.2024.11.002_bb0235 article-title: Invited review: the welfare of dairy cattle-key concepts and the role of science publication-title: J. Dairy Sci. doi: 10.3168/jds.2009-2326 – year: 2012 ident: 10.1016/j.aiia.2024.11.002_bb0005 – volume: 87 start-page: 1432 issue: 5 year: 2004 ident: 10.1016/j.aiia.2024.11.002_bb0050 article-title: Effect of feeding space on the inter-cow distance, aggression, and feeding behavior of free-stall housed lactating dairy cows publication-title: J. Dairy Sci. doi: 10.3168/jds.S0022-0302(04)73293-2 – start-page: 780 year: 2010 ident: 10.1016/j.aiia.2024.11.002_bb0215 article-title: Precision – volume: 211 start-page: 9 year: 2019 ident: 10.1016/j.aiia.2024.11.002_bb0015 article-title: Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers publication-title: Appl. Anim. Behav. Sci. doi: 10.1016/j.applanim.2018.12.003
SSID	ssj0002810169
Score	2.309131
Snippet	The development of motion sensors for monitoring cattle behaviour has enabled farmers to predict the state of their cattle's welfare more efficiently. While...
SourceID	doaj proquest crossref elsevier
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	86
SubjectTerms	agriculture algorithms artificial intelligence bulls cameras Cattle Charolais Complex behaviour head Machine learning Motion sensors Multi-dimensional Parallel pedometer PLF prediction RumiWatch
Title	Detectability of multi-dimensional movement and behaviour in cattle using sensor data and machine learning algorithms: Study on a Charolais bull
URI	https://dx.doi.org/10.1016/j.aiia.2024.11.002 https://www.proquest.com/docview/3242043704 https://doaj.org/article/ee2d7d81e26e451bb3843de1a68cc074
Volume	14
WOSCitedRecordID	wos001415909200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2589-7217 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002810169 issn: 2589-7217 databaseCode: DOA dateStart: 20190101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nj9UgFCVm4kIXxs_4xtFg4s5UH4VOwd34MXFhJi7UzI5wKX12Mq81bZ-J_2J-svcCnYwuxo1LGigELtx74HBg7AUiBKFrCUXb-rJQGkRhAHyhQNTtGqTw8Xr0t0_1yYk-PTWfrzz1RZywJA-cOu51CGVTN1qE8jCoSgBIrWQThDvU3qP_o9V3XZsrYOosbhkRKjX5lkwidLmuI6GhUr0i2c68j7J4oijY_4dD-mtpjv7m-C67kwNFfpQaeI_dCP19dvtoM2axjPCAXbwPdASQlLZ_8aHlkR5YNKTYn9Q2-HaIguAzd33D85383ci7nvsoX8yJ-L7hExYYRk580ZhzGzmWgedHJTbcnW-GsZu_b6c3nLiHWF3PHafTegTH3cQBsexD9vX4w5d3H4v8wkLhlZJzIcFIDdixHtoaWiiVq00jnMQwJZSIs03AdBAGUYnBHgeN4S2CIACoKu-cfMT2-qEPjxmvMI4ECCZoxDgewEnwGLpguvYaPfGKiaW3rc_y4_QKxrldeGZnlkbI0gghLrE4Qiv28rLMjyS-cW3utzSIlzlJODt-QHOy2Zzsv8xpxarFBGyOQVJsgb_qrq38-WIvFiconbq4Pgy7yVLESgJSa7X_Pxr4hN2iahOj5oDtzeMuPGU3_c-5m8ZncRb8BsWCEHE
linkProvider	Directory of Open Access Journals
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detectability+of+multi-dimensional+movement+and+behaviour+in+cattle+using+sensor+data+and+machine+learning+algorithms%3A+Study+on+a+Charolais+bull&rft.jtitle=Artificial+intelligence+in+agriculture&rft.au=Biszkup%2C+Mikl%C3%B3s&rft.au=V%C3%A1s%C3%A1rhelyi%2C+G%C3%A1bor&rft.au=Setiawan%2C+Nuri+Nurlaila&rft.au=M%C3%A1rton%2C+Aliz&rft.date=2024-12-01&rft.issn=2589-7217&rft.eissn=2589-7217&rft.volume=14&rft.spage=86&rft.epage=98&rft_id=info:doi/10.1016%2Fj.aiia.2024.11.002&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_aiia_2024_11_002
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2589-7217&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2589-7217&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2589-7217&client=summon