{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T10:42:41Z","timestamp":1779273761368,"version":"3.51.4"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T00:00:00Z","timestamp":1660694400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFD1600200"],"award-info":[{"award-number":["2021YFD1600200"]}]},{"name":"National Key R&amp;D Program of China","award":["T202107"],"award-info":[{"award-number":["T202107"]}]},{"name":"Sichuan Agriculture University","award":["2021YFD1600200"],"award-info":[{"award-number":["2021YFD1600200"]}]},{"name":"Sichuan Agriculture University","award":["T202107"],"award-info":[{"award-number":["T202107"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Agriculture"],"abstract":"<jats:p>Cattle behaviour is a significant indicator of cattle welfare. With the advancements in electronic equipment, monitoring and classifying multiple cattle behaviour patterns is becoming increasingly important in precision livestock management. The aim of this study was to detect important cattle physiological states using a neural network model and wearable electronic sensors. A novel long short-term memory (LSTM) recurrent neural network model that uses two-way information was developed to accurately classify cattle behaviour and compared with baseline LSTM. Deep residual bidirectional LSTM and baseline LSTM were used to classify six behavioural patterns of cows with window sizes of 64, 128 and 256 (6.4 s, 12.8 s and 25.6 s, respectively). The results showed that when using deep residual bidirectional LSTM with window size 128, four classification performance indicators, namely, accuracy, precision, recall, and F1-score, achieved the best results of 94.9%, 95.1%, 94.9%, and 94.9%, respectively. The results showed that the deep residual bidirectional LSTM model can be used to classify time-series data collected from twelve cows using inertial measurement unit collars. Six aim cattle behaviour patterns can be classified with high accuracy. This method can be used to quickly detect whether a cow is suffering from bovine dermatomycosis. Furthermore, this method can be used to implement automated and precise cattle behaviour classification techniques for precision livestock farming.<\/jats:p>","DOI":"10.3390\/agriculture12081237","type":"journal-article","created":{"date-parts":[[2022,8,17]],"date-time":"2022-08-17T21:23:56Z","timestamp":1660771436000},"page":"1237","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Recognising Cattle Behaviour with Deep Residual Bidirectional LSTM Model Using a Wearable Movement Monitoring Collar"],"prefix":"10.3390","volume":"12","author":[{"given":"Yiqi","family":"Wu","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoyuan","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meiqi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8651-718X","authenticated-orcid":false,"given":"Yingqi","family":"Peng","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya\u2019an 625014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106772","DOI":"10.1016\/j.compag.2022.106772","article-title":"Basic Motion Behavior Recognition of Single Dairy Cow Based on Improved Rexnet 3D Network","volume":"194","author":"Ma","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105627","DOI":"10.1016\/j.compag.2020.105627","article-title":"Deep Learning-Based Hierarchical Cattle Behavior Recognition with Spatio-Temporal Information","volume":"177","author":"Fuentes","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","first-page":"427","article-title":"Automatic Recognition of Ingestive-Related Behaviors of Dairy Cows Based on Triaxial Acceleration","volume":"7","author":"Shen","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.rvsc.2017.10.005","article-title":"On the Use of On-Cow Accelerometers for the Classification of Behaviours in Dairy Barns","volume":"125","author":"Benaissa","year":"2019","journal-title":"Res. Vet. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"105491","DOI":"10.1016\/j.applanim.2021.105491","article-title":"Use of an Ear-Tag Accelerometer and a Radio-Frequency Identification (RFID) System for Monitoring the Licking Behaviour in Grazing Cattle","volume":"244","author":"Simanungkalit","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Haladjian, J., Haug, J., N\u00fcske, S., and Bruegge, B. (2018). A Wearable Sensor System for Lameness Detection in Dairy Cattle. Multimodal Technol. Interact., 2.","DOI":"10.3390\/mti2020027"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.vetimm.2013.04.007","article-title":"Control of Bovine Ringworm by Vaccination in Norway","volume":"158","author":"Lund","year":"2014","journal-title":"Vet. Immunol. Immunopathol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.applanim.2005.11.019","article-title":"Behaviour and Welfare in Relation to Pathology","volume":"97","author":"Broom","year":"2006","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"10432","DOI":"10.3168\/jds.2017-13298","article-title":"A 100-Year Review: Animal Welfare in the Journal of Dairy Science\u2014The First 100 Years","volume":"100","author":"Weary","year":"2017","journal-title":"J. Dairy Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105178","DOI":"10.1016\/j.compag.2019.105178","article-title":"Dam Behavior Patterns in Japanese Black Beef Cattle Prior to Calving: Automated Detection Using LSTM-RNN","volume":"169","author":"Peng","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.applanim.2019.01.006","article-title":"Use of Social Network Analysis to Improve the Understanding of Social Behaviour in Dairy Cattle and Its Impact on Disease Transmission","volume":"213","author":"Belkhiria","year":"2019","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.tvjl.2016.09.005","article-title":"Early Detection of Health and Welfare Compromises through Automated Detection of Behavioural Changes in Pigs","volume":"217","author":"Matthews","year":"2016","journal-title":"Vet. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compag.2009.03.002","article-title":"Evaluation of Three-Dimensional Accelerometers to Monitor and Classify Behavior Patterns in Cattle","volume":"67","author":"Robert","year":"2009","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compag.2017.01.021","article-title":"Development of a Threshold-Based Classifier for Real-Time Recognition of Cow Feeding and Standing Behavioural Activities from Accelerometer Data","volume":"134","author":"Arcidiacono","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105219","DOI":"10.1016\/j.applanim.2021.105219","article-title":"Changes in the Suckling Behaviour of Beef Calves at 1 Month and 4 Months of Age and Effect on Cow Production Variables","volume":"236","author":"Kour","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.compag.2018.12.023","article-title":"Classification of Multiple Cattle Behavior Patterns Using a Recurrent Neural Network with Long Short-Term Memory and Inertial Measurement Units","volume":"157","author":"Peng","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1111\/myc.12174","article-title":"Trichophyton Verrucosum Infection in Cattle Farms of Umbria (Central Italy) and Transmission to Humans","volume":"57","author":"Agnetti","year":"2014","journal-title":"Mycoses"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1007\/s00105-012-2379-y","article-title":"Dermatomykosen durch Haus-und Nutztiere: Vernachl\u00e4ssigte Infektionen?","volume":"63","author":"Nenoff","year":"2012","journal-title":"Hautarzt"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.zool.2007.07.011","article-title":"Acceleration versus Heart Rate for Estimating Energy Expenditure and Speed during Locomotion in Animals: Tests with an Easy Model Species, Homo Sapiens","volume":"111","author":"Halsey","year":"2008","journal-title":"Zoology"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106036","DOI":"10.1016\/j.fishres.2021.106036","article-title":"Differences in the Behavioral Characteristics between Green and Loggerhead Turtles in a Setnet Bycatch Simulation","volume":"242","author":"Shiode","year":"2021","journal-title":"Fish. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Graves, A., Fern\u00e1ndez, S., Gomez, F., and Schmidhuber, J. (2006, January 25\u201329). Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. Proceedings of the 23rd International Conference on Machine Learning\u2014ICML \u201906, Pittsburgh, PA, USA.","DOI":"10.1145\/1143844.1143891"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103462","DOI":"10.1016\/j.bspc.2021.103462","article-title":"Classification of Epileptic Seizures from Electroencephalogram (EEG) Data Using Bidirectional Short-Term Memory (Bi-LSTM) Network Architecture","volume":"73","author":"Tuncer","year":"2022","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"102674","DOI":"10.1016\/j.trc.2020.102674","article-title":"Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-Wide Traffic State with Missing Values","volume":"118","author":"Cui","year":"2020","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103663","DOI":"10.1016\/j.bspc.2022.103663","article-title":"A Novel Bidirectional LSTM Network Based on Scale Factor for Atrial Fibrillation Signals Classification","volume":"76","author":"Feng","year":"2022","journal-title":"Biomed. Signal Processing Control"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on International Conference on Machine Learning (ICML\u201910), Haifa, Israel."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"7316954","DOI":"10.1155\/2018\/7316954","article-title":"Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors","volume":"2018","author":"Zhao","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110955","DOI":"10.1016\/j.measurement.2022.110955","article-title":"Classification of Hops Image Based on ResNet-ConvLSTM and Research of Intelligent Liquor Picking System","volume":"194","author":"Li","year":"2022","journal-title":"Measurement"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9110","DOI":"10.3168\/jds.2019-17478","article-title":"Hot Topic: Detecting Digital Dermatitis with Computer Vision","volume":"103","author":"Cernek","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"105141","DOI":"10.1016\/j.compag.2019.105141","article-title":"A Sensor-Based Solution to Monitor Grazing Cattle Drinking Behaviour and Water Intake","volume":"168","author":"Williams","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_32","first-page":"124","article-title":"Cattle Behaviour Classification from Collar, Halter, and Ear Tag Sensors","volume":"5","author":"Rahman","year":"2018","journal-title":"Inf. Processing Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.compag.2017.05.020","article-title":"Development of an Open-Source Algorithm Based on Inertial Measurement Units (IMU) of a Smartphone to Detect Cattle Grass Intake and Ruminating Behaviors","volume":"139","author":"Andriamandroso","year":"2017","journal-title":"Comput. Electron. Agric."}],"container-title":["Agriculture"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0472\/12\/8\/1237\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:10:56Z","timestamp":1760141456000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0472\/12\/8\/1237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,17]]},"references-count":33,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["agriculture12081237"],"URL":"https:\/\/doi.org\/10.3390\/agriculture12081237","relation":{},"ISSN":["2077-0472"],"issn-type":[{"value":"2077-0472","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,17]]}}}