{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:17:34Z","timestamp":1790219854941,"version":"4.1.0"},"reference-count":142,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.<\/jats:p>","DOI":"10.3390\/ani13050780","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T04:33:56Z","timestamp":1677040436000},"page":"780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":120,"title":["Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases"],"prefix":"10.3390","volume":"13","author":[{"given":"Karina","family":"D\u017eermeikait\u0117","sequence":"first","affiliation":[{"name":"Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dovil\u0117","family":"Ba\u010d\u0117ninait\u0117","sequence":"additional","affiliation":[{"name":"Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9993-2224","authenticated-orcid":false,"given":"Ram\u016bnas","family":"Antanaitis","sequence":"additional","affiliation":[{"name":"Large Animal Clinic, Veterinary Academy, Lithuanian University of Health Sciences, LT-47181 Kaunas, Lithuania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"127712","DOI":"10.1016\/j.jclepro.2021.127712","article-title":"Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring","volume":"312","author":"Zhang","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7395","DOI":"10.3168\/jds.2016-10907","article-title":"Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders","volume":"99","author":"Stangaferro","year":"2016","journal-title":"J. Dairy Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Henchion, M.M., Regan, \u00c1., Beecher, M., and MackenWalsh, \u00c1. (2022). Developing \u2018Smart\u2019 Dairy Farming Responsive to Farmers and Consumer-Citizens: A Review. Animals, 12.","DOI":"10.3390\/ani12030360"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"121409","DOI":"10.1016\/j.jclepro.2020.121409","article-title":"A review on dairy cattle farming: Is precision livestock farming the compromise for an environmental, economic and social sustainable production?","volume":"262","author":"Lovarelli","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13567-017-0418-5","article-title":"Advanced biosensors for detection of pathogens related to livestock and poultry","volume":"48","author":"Vidic","year":"2017","journal-title":"Vet. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Neethirajan, S. (2020). Transforming the Adaptation Physiology of Farm Animals through Sensors. Animals, 10.","DOI":"10.20944\/preprints202007.0417.v1"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"634338","DOI":"10.3389\/fvets.2021.634338","article-title":"A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle","volume":"8","author":"Stygar","year":"2021","journal-title":"Front. Vet. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.bios.2017.07.015","article-title":"Recent advancement in biosensors technology for animal and livestock health management","volume":"98","author":"Neethirajan","year":"2017","journal-title":"Biosens. Bioelectron."},{"key":"ref_9","first-page":"15","article-title":"Recent advances in wearable sensors for animal health management","volume":"12","author":"Neethirajan","year":"2017","journal-title":"Sens. BioSens. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Antanaitis, R., Juozaitien\u0117, V., Mala\u0161auskien\u0117, D., Televi\u010dius, M., Urbutis, M., and Baumgartner, W. (2021). Relation of Automated Body Condition Scoring System and Inline Biomarkers (Milk Yield, \u03b2-Hydroxybutyrate, Lactate Dehydrogenase and Progesterone in Milk) with Cow\u2019s Pregnancy Success. Sensors, 21.","DOI":"10.3390\/s21041414"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1390","DOI":"10.3168\/jds.S0022-0302(01)70170-1","article-title":"Body Condition Related to Ketosis and Reproductive Performance in Norwegian Dairy Cows","volume":"84","author":"Gillund","year":"2001","journal-title":"J. Dairy Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3269","DOI":"10.3168\/jds.2021-20389","article-title":"Genetic analyses of blood \u03b2-hydroxybutyrate predicted from milk infrared spectra and its association with longevity and female reproductive traits in Holstein cattle","volume":"105","author":"Lou","year":"2022","journal-title":"J. Dairy Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2788","DOI":"10.3168\/jds.2006-560","article-title":"The Effect of Subclinical Ketosis in Early Lactation on Reproductive Performance of Postpartum Dairy Cows","volume":"90","author":"Walsh","year":"2007","journal-title":"J. Dairy Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1016\/S0093-691X(02)01238-4","article-title":"Postpartum reproductive function: Association with energy, metabolic and endocrine status in high yielding dairy cows","volume":"59","author":"Reist","year":"2003","journal-title":"Theriogenology"},{"key":"ref_15","first-page":"369","article-title":"Changes in the inline lactate dehydrogenase according to the cow\u2019s production and reproduction status","volume":"88","author":"Rutkauskas","year":"2020","journal-title":"Acta Vet. Brno"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1672","DOI":"10.1017\/S1751731110000947","article-title":"Enzyme activity and acute phase proteins in milk utilized as indicators of acute clinical E. coli LPS-induced mastitis","volume":"4","author":"Larsen","year":"2010","journal-title":"Animal"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.theriogenology.2003.09.018","article-title":"Luteal function and conception in lactating cows and some factors influencing luteal function after first insemination","volume":"62","author":"Hommeida","year":"2004","journal-title":"Theriogenology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1007\/s11250-018-1629-0","article-title":"Diagnosis of bovine mastitis: From laboratory to farm","volume":"50","author":"Ashraf","year":"2018","journal-title":"Trop. Anim. Health Prod."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"100367","DOI":"10.1016\/j.sbsr.2020.100367","article-title":"The role of sensors, big data and machine learning in modern animal farming","volume":"29","author":"Neethirajan","year":"2020","journal-title":"Sens. Bio-Sens. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ali, A.S., Jacinto, J.G.P., M\u03cbnchemyer, W., Walte, A., Gentile, A., Formigoni, A., Mammi, L.M.E., Csaba Bajcsy, \u00c1., Abdu, M.S., and Kamel, M.M. (2022). Estrus Detection in a Dairy Herd Using an Electronic Nose by Direct Sampling on the Perineal Region. Vet. Sci., 9.","DOI":"10.3390\/vetsci9120688"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1017\/S1751731117001975","article-title":"Review: Behavioral signs of estrus and the potential of fully automated systems for detection of estrus in dairy cattle","volume":"12","author":"Reith","year":"2018","journal-title":"Anim. Int. J. Anim. Biosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.prevetmed.2018.12.014","article-title":"Predictive models for early lactation diseases in transition dairy cattle at dry-off","volume":"163","author":"Wisnieski","year":"2019","journal-title":"Prev. Vet. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5740","DOI":"10.3168\/jds.2019-17148","article-title":"Symposium review: The most important factors affecting adoption of precision dairy monitoring technologies","volume":"103","author":"Stone","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Evangelista, C., Basiric\u00f2, L., and Bernabucci, U. (2021). An Overview on the Use of Near Infrared Spectroscopy (NIRS) on Farms for the Management of Dairy Cows. Agriculture, 11.","DOI":"10.3390\/agriculture11040296"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"209","DOI":"10.15232\/aas.2018-01801","article-title":"Invited Review: Current state of wearable precision dairy technologies in disease detection","volume":"35","author":"Eckelkamp","year":"2019","journal-title":"Appl. Anim. Sci."},{"key":"ref_26","first-page":"539","article-title":"Wearable Wireless Temperature Sensor Nodes Appressed to Base of a Calf\u2019s Tail","volume":"26","author":"Nogami","year":"2014","journal-title":"Sens. Mater."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"861","DOI":"10.3390\/ani5030388","article-title":"Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior","volume":"5","author":"Zwertvaegher","year":"2015","journal-title":"Animals"},{"key":"ref_28","unstructured":"Badwolf (2022, November 14). CowAlert|Use Sensors to Manage Your Herd. IceRobotics. Available online: https:\/\/www.icerobotics.com\/cowalert\/."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1002\/elan.201600106","article-title":"\u2018SWEATCH\u2019: A Wearable Platform for Harvesting and Analysing Sweat Sodium Content","volume":"28","author":"Glennon","year":"2016","journal-title":"Electroanalysis"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1038\/529475a","article-title":"Technological leap for sweat sensing","volume":"529","author":"Heikenfeld","year":"2016","journal-title":"Nature"},{"key":"ref_31","unstructured":"(2022, November 14). U-Motion\u00ae\u2014Monitor Your Herd\u2019s Behavior. Available online: http:\/\/desamis.co.jp\/en\/."},{"key":"ref_32","unstructured":"(2022, December 03). Diagnostic Value of Milk Fat. BROLIS HerdLine. Available online: https:\/\/brolisherdline.com\/milk-fat\/."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Luo, T., Steeneveld, W., Nielen, M., Zanini, L., and Zecconi, A. (2023). Linear Mixed-Effects Model to Quantify the Association between Somatic Cell Count and Milk Production in Italian Dairy Herds. Animals, 13.","DOI":"10.1101\/2022.07.15.500192"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Neculai-Valeanu, A.-S., and Ariton, A.-M. (2022). Udder Health Monitoring for Prevention of Bovine Mastitis and Improvement of Milk Quality. Bioengineering, 9.","DOI":"10.3390\/bioengineering9110608"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"100646","DOI":"10.1016\/j.animal.2022.100646","article-title":"Accurate detection of dairy cow mastitis with deep learning technology: A new and comprehensive detection method based on infrared thermal images","volume":"16","author":"Wang","year":"2022","journal-title":"Animal"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100165","DOI":"10.1016\/j.vas.2021.100165","article-title":"Systemic inflammatory biomarkers and somatic cell count in dairy cows with subclinical mastitis","volume":"11","author":"Fernandes","year":"2021","journal-title":"Vet. Anim. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"562","DOI":"10.14202\/vetworld.2018.562-577","article-title":"Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview","volume":"11","author":"Alhussien","year":"2018","journal-title":"Vet. World"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104437","DOI":"10.1016\/j.landusepol.2019.104437","article-title":"Can technology help achieve sustainable intensification? Evidence from milk recording on Irish dairy farms","volume":"92","author":"Balaine","year":"2020","journal-title":"Land Use Policy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"186","DOI":"10.3389\/fbioe.2019.00186","article-title":"Biosensors for On-Farm Diagnosis of Mastitis","volume":"7","author":"Martins","year":"2019","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1080\/01652176.2019.1642546","article-title":"Technological interventions and advances in the diagnosis of intramammary infections in animals with emphasis on bovine population\u2014A review","volume":"39","author":"Chakraborty","year":"2019","journal-title":"Vet. Q"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1017\/S0022029920000084","article-title":"Biomarkers of fitness and welfare in dairy cattle: Healthy productivity","volume":"87","author":"Zachut","year":"2020","journal-title":"J. Dairy Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.3168\/jds.2012-6107","article-title":"Invited review: Sensors to support health management on dairy farms","volume":"96","author":"Rutten","year":"2013","journal-title":"J. Dairy Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"123893","DOI":"10.1016\/j.talanta.2022.123893","article-title":"Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows","volume":"253","author":"Muncan","year":"2023","journal-title":"Talanta"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.2460\/ajvr.70.10.1291","article-title":"Evaluation of breath biomarkers and serum haptoglobin concentration for diagnosis of bovine respiratory disease in heifers newly arrived at a feedlot","volume":"70","author":"Holland","year":"2009","journal-title":"Am. J. Vet. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1016\/j.trstmh.2009.02.004","article-title":"A pilot study of faecal volatile organic compounds in faeces from cholera patients in Bangladesh to determine their utility in disease diagnosis","volume":"103","author":"Garner","year":"2009","journal-title":"Trans. R. Soc. Trop. Med. Hyg."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Leopold, J.H., van Hooijdonk, R.T., Sterk, P.J., Abu-Hanna, A., Schultz, M.J., and Bos, L.D. (2014). Glucose prediction by analysis of exhaled metabolites\u2014A systematic review. BMC Anesthesiol., 14.","DOI":"10.1186\/1471-2253-14-46"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6553","DOI":"10.1021\/ac401573r","article-title":"Electrochemical tattoo biosensors for real-time noninvasive lactate monitoring in human perspiration","volume":"85","author":"Jia","year":"2013","journal-title":"Anal. Chem."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.tibtech.2014.04.005","article-title":"Non-invasive wearable electrochemical sensors: A review","volume":"32","author":"Bandodkar","year":"2014","journal-title":"Trends Biotechnol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mojsym, W., Wawrzykowski, J., Jamio\u0142, M., Chrobak, \u0141., and Kankofer, M. (2022). Comparative Analysis of Saliva and Plasma Proteins Patterns in Pregnant Cows\u2014Preliminary Studies. Animals, 12.","DOI":"10.3390\/ani12202850"},{"key":"ref_50","unstructured":"(2023, January 04). Global Survey of the Bovine Salivary Proteome: Integrating Multidimensional Prefractionation, Targeted, and Glycocapture Strategies. Journal of Proteome Research. Available online: https:\/\/pubs.acs.org\/doi\/10.1021\/pr200516d."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"962903","DOI":"10.1155\/2014\/962903","article-title":"Saliva-based biosensors: Noninvasive monitoring tool for clinical diagnostics","volume":"2014","author":"Malon","year":"2014","journal-title":"BioMed Res. Int."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"104541","DOI":"10.1016\/j.jprot.2022.104541","article-title":"Label-free plasma proteomics for the identification of the putative biomarkers of oral squamous cell carcinoma","volume":"259","author":"Gautam","year":"2022","journal-title":"J. Proteom."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"969","DOI":"10.3389\/fgene.2022.867909","article-title":"Comparative Proteome Profiling of Saliva Between Estrus and Non-Estrus Stages by Employing Label-Free Quantitation (LFQ) and Tandem Mass Tag (TMT)-LC-MS\/MS Analysis: An Approach for Estrus Biomarker Identification in Bubalus bubalis","volume":"13","author":"Singh","year":"2022","journal-title":"Front. Genet."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Contreras-Aguilar, M.D., Vallejo-Mateo, P.J., \u017delvyt\u0117, R., Tecles, F., and Rubio, C.P. (2020). Changes in Saliva Analytes Associated with Lameness in Cows: A Pilot Study. Animals, 10.","DOI":"10.3390\/ani10112078"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"12140","DOI":"10.3168\/jds.2019-17887","article-title":"Graduate Student Literature Review: Evaluating the appropriate use of wearable accelerometers in research to monitor lying behaviors of dairy cows","volume":"103","author":"Hendriks","year":"2020","journal-title":"J. Dairy Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Andriamandroso, A., Bindelle, J., Mercatoris, B., and Lebeau, F. (2016). A review on the use of sensors to monitor cattle jaw movements and behavior when grazing. Biotechnol. Agron. Soci\u00e9t\u00e9 Environ., 20.","DOI":"10.25518\/1780-4507.13058"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"105402","DOI":"10.1016\/j.applanim.2021.105402","article-title":"Noseband sensor validation and behavioural indicators for assessing beef cattle grazing on extensive pastures","volume":"242","author":"Raynor","year":"2021","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S0168-1591(00)00188-X","article-title":"Seasonal changes in circadian grazing patterns of Kerry cows (Bos taurus) in semi-feral conditions in Killarney National Park, Co. Kerry, Ireland","volume":"71","author":"Linnane","year":"2001","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Antanaitis, R., Juozaitien\u0117, V., Mala\u0161auskien\u0117, D., Televi\u010dius, M., Urbutis, M., Rutkaukas, A., \u0160ertvytyt\u0117, G., and Baumgartner, W. (2022). Identification of Changes in Rumination Behavior Registered with an Online Sensor System in Cows with Subclinical Mastitis. Vet. Sci., 9.","DOI":"10.20944\/preprints202207.0348.v1"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/1297-9716-42-15","article-title":"Variation of inflammatory dynamics and mediators in primiparous cows after intramammary challenge with Escherichia coli","volume":"42","author":"Pezeshki","year":"2011","journal-title":"Vet. Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.compag.2017.02.021","article-title":"System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows","volume":"136","author":"Zehner","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.anireprosci.2017.10.003","article-title":"Prediction of calving time in dairy cattle","volume":"187","author":"Fadul","year":"2017","journal-title":"Anim. Reprod. Sci."},{"key":"ref_63","unstructured":"(2022, November 23). Herd Monitoring Software|SMARTBOW. Available online: https:\/\/www.smartbow.com\/en\/Home.aspx."},{"key":"ref_64","unstructured":"(2022, December 03). Moonsyst. Available online: https:\/\/moonsyst.com\/home."},{"key":"ref_65","unstructured":"(2023, January 04). How It Works. Available online: https:\/\/smaxtec.com\/en\/function\/."},{"key":"ref_66","unstructured":"(2022, December 03). CattleEye|Autonomous Livestock Monitoring. Available online: https:\/\/cattleeye.com\/."},{"key":"ref_67","unstructured":"(2022, December 03). Technology. Cainthus. Available online: https:\/\/www.cainthus.com\/technology."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.theriogenology.2017.09.033","article-title":"Potential of connected devices to optimize cattle reproduction","volume":"112","year":"2018","journal-title":"Theriogenology"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1093\/tas\/txy004","article-title":"Using advanced technologies to quantify beef cattle behavior1","volume":"2","author":"Richeson","year":"2018","journal-title":"Transl. Anim. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s40317-020-00225-9","article-title":"Technologies for the automated collection of heat stress data in sheep","volume":"9","author":"Swinbourne","year":"2021","journal-title":"Anim. Biotelemetry"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Rivero, M.J., Grau-Campanario, P., Mullan, S., Held, S.D.E., Stokes, J.E., Lee, M.R.F., and Cardenas, L.M. (2021). Factors Affecting Site Use Preference of Grazing Cattle Studied from 2000 to 2020 through GPS Tracking: A Review. Sensors, 21.","DOI":"10.3390\/s21082696"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Cabezas, J., Yubero, R., Visitaci\u00f3n, B., Navarro-Garc\u00eda, J., Algar, M.J., Cano, E.L., and Ortega, F. (2022). Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection. Entropy, 24.","DOI":"10.3390\/e24030336"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.3390\/s130303711","article-title":"Assessing Herbivore Foraging Behavior with GPS Collars in a Semiarid Grassland","volume":"13","author":"Augustine","year":"2013","journal-title":"Sensors"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.applanim.2008.10.006","article-title":"Landscape nutritional patterns and cattle distribution in rangeland pastures","volume":"116","author":"Ganskopp","year":"2009","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"105383","DOI":"10.1016\/j.prevetmed.2021.105383","article-title":"Identification of discriminating behavioural and movement variables in lameness scores of dairy cows at pasture from accelerometer and GPS sensors using a Partial Least Squares Discriminant Analysis","volume":"193","author":"Riaboff","year":"2021","journal-title":"Prev. Vet. Med."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Riaboff, L., Couvreur, S., Madouasse, A., Roig-Pons, M., Aubin, S., Massabie, P., Chauvin, A., B\u00e9d\u00e8re, N., and Plantier, G. (2020). Use of Predicted Behavior from Accelerometer Data Combined with GPS Data to Explore the Relationship between Dairy Cow Behavior and Pasture Characteristics. Sensors, 20.","DOI":"10.3390\/s20174741"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1017\/S0022029920000667","article-title":"Sensing solutions for improving the performance, health and wellbeing of small ruminants","volume":"87","author":"Caja","year":"2020","journal-title":"J. Dairy Res."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.jtherbio.2018.08.021","article-title":"Monitoring foot surface temperature using infrared thermal imaging for assessment of hoof health status in cattle: A review","volume":"78","author":"LokeshBabu","year":"2018","journal-title":"J. Therm. Biol."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.compag.2016.01.027","article-title":"Infrared thermography in animal production: An overview","volume":"123","author":"McManus","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"14513","DOI":"10.3390\/s150614513","article-title":"The Role of Infrared Thermography as a Non-Invasive Tool for the Detection of Lameness in Cattle","volume":"15","author":"Alsaaod","year":"2015","journal-title":"Sensors"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kang, X., Zhang, X.D., and Liu, G. (2021). A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications. Sensors, 21.","DOI":"10.3390\/s21030753"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"687","DOI":"10.4141\/A03-012","article-title":"Daily variation in the udder surface temperature of dairy cows measured by infrared thermography: Potential for mastitis detection","volume":"83","author":"Berry","year":"2003","journal-title":"Can. J. Anim. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zaninelli, M., Redaelli, V., Luzi, F., Bronzo, V., Mitchell, M., Dell\u2019Orto, V., Bontempo, V., Cattaneo, D., and Savoini, G. (2018). First Evaluation of Infrared Thermography as a Tool for the Monitoring of Udder Health Status in Farms of Dairy Cows. Sensors, 18.","DOI":"10.3390\/s18030862"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.jtherbio.2016.10.003","article-title":"Mapping the body surface temperature of cattle by infrared thermography","volume":"62","author":"Salles","year":"2016","journal-title":"J. Therm. Biol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1016\/j.tvjl.2012.06.052","article-title":"An investigation into the use of infrared thermography (IRT) as a rapid diagnostic tool for foot lesions in dairy cattle","volume":"193","author":"Stokes","year":"2012","journal-title":"Vet. J."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Lowe, G., McCane, B., Sutherland, M., Waas, J., Schaefer, A., Cox, N., and Stewart, M. (2020). Automated Collection and Analysis of Infrared Thermograms for Measuring Eye and Cheek Temperatures in Calves. Animals, 10.","DOI":"10.3390\/ani10020292"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lee, M., and Seo, S. (2021). Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review. Animals, 11.","DOI":"10.3390\/ani11102779"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"179","DOI":"10.2527\/jas1982.541179x","article-title":"Effect of Water Temperature in Rumen Temperature, Digestion and Rumen Fermentation in Sheep","volume":"54","author":"Brod","year":"1982","journal-title":"J. Anim. Sci."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"3880","DOI":"10.3168\/jds.2008-1159","article-title":"Impact of Intake Water Temperatures on Reticular Temperatures of Lactating Dairy Cows","volume":"91","author":"Bewley","year":"2008","journal-title":"J. Dairy Sci."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Antanaitis, R., Anskien\u0117, L., Rapaliut\u0117, E., Bilskis, R., D\u017eermeikait\u0117, K., Ba\u010d\u0117ninait\u0117, D., Ju\u0161kien\u0117, V., Ju\u0161ka, R., and Me\u0161kinyt\u0117, E. (2022). Relationship between Reticulorumen Parameters Measured in Real Time and Methane Emission and Heat Stress Risk in Dairy Cows. Animals, 12.","DOI":"10.3390\/ani12233257"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Cantor, M.C., Costa, J.H.C., and Bewley, J.M. (2018). Impact of Observed and Controlled Water Intake on Reticulorumen Temperature in Lactating Dairy Cattle. Animals, 8.","DOI":"10.3390\/ani8110194"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Mullins, I.L., Truman, C.M., Campler, M.R., Bewley, J.M., and Costa, J.H.C. (2019). Validation of a Commercial Automated Body Condition Scoring System on a Commercial Dairy Farm. Animals, 9.","DOI":"10.3390\/ani9060287"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"106143","DOI":"10.1016\/j.compag.2021.106143","article-title":"Intelligent perception for cattle monitoring: A review for cattle identification, body condition score evaluation, and weight estimation","volume":"185","author":"Qiao","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"5769","DOI":"10.3168\/jds.2009-2431","article-title":"Invited review: Body condition score and its association with dairy cow productivity, health, and welfare","volume":"92","author":"Roche","year":"2009","journal-title":"J. Dairy Sci."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Albornoz, R.I., Giri, K., Hannah, M.C., and Wales, W.J. (2022). An Improved Approach to Automated Measurement of Body Condition Score in Dairy Cows Using a Three-Dimensional Camera System. Animals, 12.","DOI":"10.3390\/ani12010072"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"105821","DOI":"10.1016\/j.compag.2020.105821","article-title":"Accurate body measurement of live cattle using three depth cameras and non-rigid 3-D shape recovery","volume":"179","author":"Ruchay","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.compag.2014.12.020","article-title":"A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three-dimensional camera system","volume":"111","author":"Kuzuhara","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_98","first-page":"100298","article-title":"Making sense in the cloud: Farm advisory services in a smart farming future","volume":"90","author":"Eastwood","year":"2019","journal-title":"NJAS Wagening. J. Life Sci."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Antanaitis, R., Mala\u0161auskien\u0117, D., Televi\u010dius, M., Urbutis, M., Rutkauskas, A., \u0160ertvytyt\u0117, G., Anskien\u0117, L., and Baumgartner, W. (2022). Associations of Automatically Recorded Body Condition Scores with Measures of Production, Health, and Reproduction. Agriculture, 12.","DOI":"10.3390\/agriculture12111834"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"106675","DOI":"10.1016\/j.compag.2021.106675","article-title":"CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss","volume":"193","author":"Xu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.compag.2017.10.029","article-title":"Automatic individual identification of Holstein dairy cows using tailhead images","volume":"142","author":"Li","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.compag.2015.12.022","article-title":"Biometric cattle identification approach based on Weber\u2019s Local Descriptor and AdaBoost classifier","volume":"122","author":"Gaber","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_104","first-page":"69","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_107","unstructured":"(2022, December 03). Mask R-CNN | IEEE Conference Publication | IEEE Xplore. Available online: https:\/\/ieeexplore.ieee.org\/document\/8237584."},{"key":"ref_108","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Proceedings of the Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_109","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_110","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_111","doi-asserted-by":"crossref","unstructured":"Zhang, L., Gray, H., Ye, X., Collins, L., and Allinson, N. (2018). Automatic individual pig detection and tracking in surveillance videos. arXiv.","DOI":"10.3390\/s19051188"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2017.12.006","article-title":"Region detection of lesion area of knee based on colour edge detection and bilateral projection","volume":"173","author":"Guo","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_113","unstructured":"Yao, L., Hu, Z., Liu, C., Liu, H., Kuang, Y., and Gao, Y. (2019). Proceedings the ACM Turing Celebration Conference\u2014China, ACM."},{"key":"ref_114","first-page":"1","article-title":"Open Set Sheep Face Recognition Based on Euclidean Space Metric","volume":"2021","author":"Xue","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1007\/s10766-017-0550-x","article-title":"Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images","volume":"46","author":"Kumar","year":"2018","journal-title":"Int. J. Parallel Program."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"106254","DOI":"10.1016\/j.compag.2021.106254","article-title":"Automatic tracking of the dairy goat in the surveillance video","volume":"187","author":"Su","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1016\/j.snb.2018.11.109","article-title":"Volatile compounds monitoring as indicative of female cattle fertile period using electronic nose","volume":"282","author":"Manzoli","year":"2019","journal-title":"Sens. Actuators B Chem."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Nogami, H., Arai, S., Okada, H., Zhan, L., and Itoh, T. (2017). Minimized Bolus-Type Wireless Sensor Node with a Built-In Three-Axis Acceleration Meter for Monitoring a Cow\u2019s Rumen Conditions. Sensors, 17.","DOI":"10.3390\/s17040687"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1186\/2193-1801-3-760","article-title":"Calibration of an automated California mastitis test with focus on the device-dependent variation","volume":"3","author":"Neitzel","year":"2014","journal-title":"SpringerPlus"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1596","DOI":"10.3168\/jds.2009-2852","article-title":"Associations of elevated nonesterified fatty acids and beta-hydroxybutyrate concentrations with early lactation reproductive performance and milk production in transition dairy cattle in the northeastern United States","volume":"93","author":"Ospina","year":"2010","journal-title":"J. Dairy Sci."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"7991","DOI":"10.3390\/s100907991","article-title":"Sensors and Clinical Mastitis\u2014The Quest for the Perfect Alert","volume":"10","author":"Hogeveen","year":"2010","journal-title":"Sensors"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"86","DOI":"10.3758\/BF03200791","article-title":"Graze: A program to analyze recordings of the jaw movements of ruminants","volume":"32","author":"Rutter","year":"2000","journal-title":"Behav. Res. Methods Instrum. Comput."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.applanim.2011.04.005","article-title":"Impact of clinical acute mastitis on cow behaviour","volume":"132","author":"Siivonen","year":"2011","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"2571","DOI":"10.3168\/jds.2011-5135","article-title":"Short communication: The effects of experimentally induced Escherichia coli clinical mastitis on lying behavior of dairy cows","volume":"95","author":"Cyples","year":"2012","journal-title":"J. Dairy Sci."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"6994","DOI":"10.3168\/jds.2011-5247","article-title":"Behavioral changes in dairy cows with mastitis","volume":"95","author":"Gibbons","year":"2012","journal-title":"J. Dairy Sci."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.3168\/jds.2011-5064","article-title":"The effects of experimentally induced Escherichia coli mastitis and flunixin meglumine administration on activity measures, feed intake, and milk parameters","volume":"95","author":"Yeiser","year":"2012","journal-title":"J. Dairy Sci."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Bausewein, M., Mansfeld, R., Doherr, M.G., Harms, J., and Sorge, U.S. (2022). Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds. Animals, 12.","DOI":"10.3390\/ani12162131"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"2843","DOI":"10.3168\/jds.S0022-0302(05)72965-9","article-title":"Feeding Behavior Identifies Dairy Cows at Risk for Metritis","volume":"88","author":"Urton","year":"2005","journal-title":"J. Dairy Sci."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.3168\/jds.2006-807","article-title":"Von Prepartum Behavior and Dry Matter Intake Identify Dairy Cows at Risk for Metritis","volume":"90","author":"Huzzey","year":"2007","journal-title":"J. Dairy Sci."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"6812","DOI":"10.3168\/jds.2014-8947","article-title":"Characterization of peripartum rumination and activity of cows diagnosed with metabolic and uterine diseases","volume":"98","author":"Liboreiro","year":"2015","journal-title":"J. Dairy Sci."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Li, S., Wei, X., Song, J., Zhang, C., Zhang, Y., and Sun, Y. (2021). Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows. Animals, 11.","DOI":"10.3390\/ani11113224"},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Sturm, V., Efrosinin, D., \u00d6hlschuster, M., Gusterer, E., Drillich, M., and Iwersen, M. (2020). Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows. Sensors, 20.","DOI":"10.3390\/s20051484"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"4365","DOI":"10.3168\/jds.2009-2115","article-title":"Using gait score, walking speed, and lying behavior to detect hoof lesions in dairy cows","volume":"92","author":"Chapinal","year":"2009","journal-title":"J. Dairy Sci."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.applanim.2010.10.006","article-title":"Impact of lameness on gait characteristics and lying behaviour of zero grazed dairy cattle in early lactation","volume":"129","author":"Blackie","year":"2011","journal-title":"Appl. Anim. Behav. Sci."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3168\/jds.S0022-0302(04)73422-0","article-title":"Effect of Free Stall Surface on Daily Activity Patterns in Dairy Cows with Relevance to Lameness Prevalence","volume":"87","author":"Cook","year":"2004","journal-title":"J. Dairy Sci."},{"key":"ref_136","unstructured":"(2023, January 04). DeLaval Herd NavigatorTM\u2014DeLaval. Available online: https:\/\/www.delaval.com\/en-gb\/discover-our-farm-solutions\/delaval-delpro\/precision-analytics\/delaval-herd-navigator\/."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"475","DOI":"10.3390\/agriengineering4020031","article-title":"Automated Systems for Estrous and Calving Detection in Dairy Cattle","volume":"4","author":"Santos","year":"2022","journal-title":"AgriEngineering"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"3615","DOI":"10.3168\/jds.2018-15692","article-title":"Technical note: Validation of an automated in-line milk progesterone analysis system to diagnose pregnancy in dairy cattle","volume":"102","author":"Ambrose","year":"2019","journal-title":"J. Dairy Sci."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1007\/s11250-020-02230-9","article-title":"Early pregnancy diagnoses based on physiological indexes of dairy cattle: A review","volume":"52","author":"Wang","year":"2020","journal-title":"Trop. Anim. Health Prod."},{"key":"ref_140","unstructured":"Muasa, B.S. (2021). Monitoring the Reproductive Status of Dairy Cows Using Cow-Side Oestrus Detection Technologies. [Ph.D. Thesis, The University of Edinburgh]."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Zheng, S., Zhou, C., Jiang, X., Huang, J., and Xu, D. (2022). Progress on Infrared Imaging Technology in Animal Production: A Review. Sensors, 22.","DOI":"10.3390\/s22030705"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Maw, S.Z., Zin, T.T., Tin, P., Kobayashi, I., and Horii, Y. (2021). An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time. Sensors, 21.","DOI":"10.3390\/s21196490"}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/13\/5\/780\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:38:29Z","timestamp":1760121509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/13\/5\/780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":142,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["ani13050780"],"URL":"https:\/\/doi.org\/10.3390\/ani13050780","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}