{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:47:04Z","timestamp":1792039624108,"version":"4.4.0"},"reference-count":80,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Korea Smart Farm R&amp;D Foundation","award":["RS-2025-02314059"],"award-info":[{"award-number":["RS-2025-02314059"]}]},{"name":"Korea Institute of Planning and Evaluation of Technology in Food, Agriculture and Forestry","award":["RS-2025-02314059"],"award-info":[{"award-number":["RS-2025-02314059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Continuous environmental, production, locational, image, and acoustic data have increasingly been collected from laying-hen farms through precision livestock farming (PLF) systems. However, sensor-derived classifications have often been validated against human-assigned behavioral or appearance labels rather than independently measured physiological states. In this narrative review, the methodological requirements for using physiological indicators as reference labels in PLF models for laying hens were examined, considering blood biochemistry, corticosterone concentration, the heterophil-to-lymphocyte ratio, body temperature, and immune or disease indicators alongside structured farm-level data and unstructured video, thermal, and acoustic data. Direct sensor-to-reference comparison studies numbered only seven and predominantly used rectal or cloacal temperature, peripheral temperature, clinical trajectories, or disease outcomes. No study identified in this review developed and externally validated a composite label combining blood biochemistry, the heterophil-to-lymphocyte ratio, corticosterone, and immune or pathogen indicators in laying hens. Major barriers were considered to include sensor\u2013biomarker time-scale mismatches, sampling-induced label noise, and inconsistent units of analysis across individual, flock, and farm levels. It is suggested that future studies incorporate individual identification, biomarker-specific temporal alignment, multimodal composite labeling, and independent farm-level validation, so that PLF for laying hens can progress from sensor-centered detection toward biologically validated decision support linked to management actions.<\/jats:p>","DOI":"10.3390\/ani16193155","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:16:52Z","timestamp":1791472612000},"page":"3155","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrating Physiological Biomarkers into Precision Livestock Farming for Laying Hens: Methodological Challenges and Future Research Directions"],"prefix":"10.3390","volume":"16","author":[{"given":"Leecheon","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Animal Science, Jeonbuk National University, Jeonju 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1659-4427","authenticated-orcid":false,"given":"Darae","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Animal Biotechnology, Jeonbuk National University, Jeonju 54896, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2527\/af.2017.0102","article-title":"General introduction to precision livestock farming","volume":"7","author":"Berckmans","year":"2017","journal-title":"Anim. Front."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1017\/S175173111900199X","article-title":"Precision livestock farming: Building \u2018digital representations\u2019 to bring the animals closer to the farmer","volume":"13","author":"Norton","year":"2019","journal-title":"Animal"},{"key":"ref_3","first-page":"184","article-title":"A review on computer vision systems in monitoring of poultry: A welfare perspective","volume":"4","author":"Okinda","year":"2020","journal-title":"Artif. Intell. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dabbou, S., Mugnai, C., Soglia, D., Tarantola, M., Macchi, E., Sartore, S., Bergagna, S., Pagliasso, G., and Schiavone, A. (2022). Multifactorial evaluation of regrouping effects on performance and welfare in two Italian dual-purpose chicken breeds: Bianca Di Saluzzo and Bionda Piemontese. Animals, 12.","DOI":"10.3390\/ani12182355"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1093\/ps\/85.4.761","article-title":"Physiological stress in laying hens","volume":"85","author":"Mumma","year":"2006","journal-title":"Poult. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.physbeh.2009.04.012","article-title":"Effects of chronic and repeated corticosterone administration in rearing chickens on physiology, the onset of lay and egg production of hens","volume":"98","author":"Shini","year":"2009","journal-title":"Physiol. Behav."},{"key":"ref_7","first-page":"37","article-title":"Temperature measurement method for commercially farmed layer hens based on multi-source image and environmental data fusion","volume":"56","author":"Song","year":"2025","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"EFSA Panel on Animal Health and Welfare (AHAW), Nielsen, S.S., Alvarez, J., Bicout, D.J., Calistri, P., Canali, E., Drewe, J.A., Garin-Bastuji, B., Gonzales Rojas, J.L., and Gort\u00e1zar Schmidt, C. (2023). Welfare of laying hens on farm. EFSA J., 21, e07789.","DOI":"10.2903\/j.efsa.2023.7789"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1238","DOI":"10.3382\/ps\/pew100","article-title":"Welfare indicators in laying hens in relation to nest exclusion","volume":"95","author":"Alm","year":"2016","journal-title":"Poult. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1111\/j.1939-165X.2002.tb00295.x","article-title":"Clinical chemistry of companion avian species: A review","volume":"31","author":"Harr","year":"2002","journal-title":"Vet. Clin. Pathol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2615","DOI":"10.3382\/ps.2014-04193","article-title":"Corticosterone metabolites in laying hen droppings\u2014Effects of fiber enrichment, genotype, and daily variations","volume":"93","author":"Alm","year":"2014","journal-title":"Poult. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4685","DOI":"10.1016\/j.psj.2020.06.065","article-title":"Corticosterone in feathers of laying hens: An assay validation for evidence-based assessment of animal welfare","volume":"99","author":"Lindenwald","year":"2020","journal-title":"Poult. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"101494","DOI":"10.1016\/j.psj.2021.101494","article-title":"Research Note: It\u2019s not just stress\u2014Fecal contamination of plumage may affect feather corticosterone concentration","volume":"100","author":"Bartels","year":"2021","journal-title":"Poult. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Buharideen, S.M., Hassan, M.S., Najimudeen, S.M., Niu, D., Czub, M., Gomis, S., and Abdul-Careem, M.F. (2021). Immune responses in laying hens after an infectious bronchitis vaccination of pullets: A comparison of two vaccination strategies. Vaccines, 9.","DOI":"10.3390\/vaccines9050531"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101868","DOI":"10.1016\/j.psj.2022.101868","article-title":"Comparison of stress biomarkers in laying hens raised under a long-term multiple stress condition","volume":"101","author":"Lee","year":"2022","journal-title":"Poult. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"9937941","DOI":"10.1155\/tbed\/9937941","article-title":"Application of a Quantitative Real-Time PCR Assay for Early Detection of Salmonella enterica Serovar Enteritidis on Poultry Farms During an Outbreak in New South Wales, Australia (2018\u20132020)","volume":"2025","author":"Onizawa","year":"2025","journal-title":"Transbound. Emerg. Dis."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1093\/condor\/104.1.129","article-title":"Corticosterone responses in wild birds: The importance of rapid initial sampling","volume":"104","author":"Romero","year":"2002","journal-title":"Condor"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2208","DOI":"10.3382\/ps\/pew137","article-title":"Biology of stress in poultry with emphasis on glucocorticoids and the heterophil to lymphocyte ratio","volume":"95","author":"Scanes","year":"2016","journal-title":"Poult. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3381","DOI":"10.3382\/ps\/pey204","article-title":"An evaluation of methods for measuring stress in broiler chickens","volume":"97","author":"Weimer","year":"2018","journal-title":"Poult. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kang, H., Brocklehurst, S., Haskell, M., Jarvis, S., and Sandilands, V. (2025). Do Activity Sensors Identify Physiological, Clinical and Behavioural Changes in Laying Hens Exposed to a Vaccine Challenge?. Animals, 15.","DOI":"10.3390\/ani15020205"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1111\/j.1557-9263.2011.00338.x","article-title":"Collecting, processing, and storing avian blood: A review","volume":"82","author":"Owen","year":"2011","journal-title":"J. Field Ornithol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2095","DOI":"10.5433\/1679-0359.2023v44n6p2095","article-title":"Light intensity, blood fraction, fasting and storage time affect blood biochemical metabolites in broiler chickens","volume":"44","author":"Wachholz","year":"2023","journal-title":"Semin. Cienc. Agrar."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Caulfield, M.P., and Padula, M.P. (2020). HPLC MS-MS analysis shows measurement of corticosterone in egg albumen is not a valid indicator of chicken welfare. Animals, 10.","DOI":"10.3390\/ani10050821"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Andreasson, U., Perret-Liaudet, A., van Waalwijk van Doorn, L.J., Blennow, K., Chiasserini, D., Engelborghs, S., Fladby, T., Genc, S., Kruse, N., and Kuiperij, H.B. (2015). A practical guide to immunoassay method validation. Front. Neurol., 6.","DOI":"10.3389\/fneur.2015.00179"},{"key":"ref_25","unstructured":"U.S. Food and Drug Administration (FDA) (2018). Bioanalytical Method Validation: Guidance for Industry, U.S. Department of Health and Human Services."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1887","DOI":"10.1111\/jvim.14574","article-title":"Methods and processes of developing the Strengthening the Reporting of Observational Studies in Epidemiology\u2013Veterinary (STROBE-Vet) statement","volume":"30","author":"Sargeant","year":"2016","journal-title":"J. Vet. Intern. Med."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"105291","DOI":"10.1016\/j.compag.2020.105291","article-title":"Smart poultry management: Smart sensors, big data, and the internet of things","volume":"170","author":"Astill","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Olejnik, K., Popiela, E., and Opali\u0144ski, S. (2022). Emerging precision management methods in poultry sector. Agriculture, 12.","DOI":"10.3390\/agriculture12050718"},{"key":"ref_30","first-page":"1455","article-title":"Automated tracking and behavior quantification of laying hens using 3D computer vision and radio frequency identification technologies","volume":"57","author":"Nakarmi","year":"2014","journal-title":"Trans. ASABE"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2060","DOI":"10.1017\/S1751731118003440","article-title":"An ultra-high frequency radio frequency identification system for studying individual feeding and drinking behaviors of group-housed broilers","volume":"13","author":"Li","year":"2019","journal-title":"Animal"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105627","DOI":"10.1016\/j.psj.2025.105627","article-title":"Development and computer-assisted validation of a radio frequency identification system for tracking individual chicken visits to functional areas","volume":"104","author":"Alindekon","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1017\/S1751731119002155","article-title":"Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: Towards the goal of precision livestock farming","volume":"14","author":"Li","year":"2020","journal-title":"Animal"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Manikandan, V., and Neethirajan, S. (2025). Ai-powered vocalization analysis in poultry: Systematic review of health, behavior, and welfare monitoring. Sensors, 25.","DOI":"10.3390\/s25134058"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101503","DOI":"10.1016\/j.animal.2025.101503","article-title":"Multiobject tracking in livestock\u2014From farm animal management to state-of-the-art methods","volume":"19","author":"Nidhi","year":"2025","journal-title":"Animal"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106695","DOI":"10.1016\/j.compag.2022.106695","article-title":"ChickenNet\u2014An end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision","volume":"194","author":"Lamping","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sozzi, M., Pillan, G., Ciarelli, C., Marinello, F., Pirrone, F., Bordignon, F., Bordignon, A., Xiccato, G., and Trocino, A. (2023). Measuring comfort behaviours in laying hens using deep-learning tools. Animals, 13.","DOI":"10.3390\/ani13010033"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"905","DOI":"10.3390\/agriengineering5020056","article-title":"A novel YOLOv6 object detector for monitoring piling behavior of cage-free laying hens","volume":"5","author":"Bist","year":"2023","journal-title":"AgriEngineering"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105697","DOI":"10.1016\/j.psj.2025.105697","article-title":"Decoding Laying Hen Behavior and Physiological Status through Acoustic Biomarkers: Temporal Patterns, Rooster-Hen Vocalization Identification in Group Housing, and Environmental Adaptation","volume":"104","author":"Lin","year":"2025","journal-title":"Poult. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Essien, D., and Neethirajan, S. (2025). Multimodal AI systems for enhanced laying hen welfare assessment and productivity optimization. Smart Agric. Technol., 101564.","DOI":"10.1016\/j.atech.2025.101564"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"102637","DOI":"10.1016\/j.psj.2023.102637","article-title":"Tracking floor eggs with machine vision in cage-free hen houses","volume":"102","author":"Subedi","year":"2023","journal-title":"Poult. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"104289","DOI":"10.1016\/j.psj.2024.104289","article-title":"Tracking dustbathing behavior of cage-free laying hens with machine vision technologies","volume":"103","author":"Paneru","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"104193","DOI":"10.1016\/j.psj.2024.104193","article-title":"Monitoring activity index and behaviors of cage-free hens with advanced deep learning technologies","volume":"103","author":"Yang","year":"2024","journal-title":"Poult. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1093\/japr\/15.2.274","article-title":"Assessing feather cover of laying hens by infrared thermography","volume":"15","author":"Cook","year":"2006","journal-title":"J. Appl. Poult. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"295","DOI":"10.3382\/ps.2012-02629","article-title":"Use of infrared thermography to assess laying-hen feather coverage","volume":"92","author":"Zhao","year":"2013","journal-title":"Poult. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102022","DOI":"10.1016\/j.psj.2022.102022","article-title":"Is infrared thermography an appropriate method for early detection and objective quantification of plumage damage in white and brown feathered laying hens?","volume":"101","author":"Schreiter","year":"2022","journal-title":"Poult. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.physbeh.2015.09.032","article-title":"Skin temperature reveals the intensity of acute stress","volume":"152","author":"Herborn","year":"2015","journal-title":"Physiol. Behav."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1594","DOI":"10.1017\/S1751731116000616","article-title":"Heritability of body surface temperature in hens estimated by infrared thermography at normal or hot temperatures and genetic correlations with egg and feather quality","volume":"10","author":"Loyau","year":"2016","journal-title":"Animal"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.5713\/ab.20.0647","article-title":"Changes in facial surface temperature of laying hens under different thermal conditions","volume":"34","author":"Kim","year":"2021","journal-title":"Anim. Biosci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Solis, I.L., de Oliveira-Boreli, F.P., de Sousa, R.V., Martello, L.S., and Pereira, D.F. (2024). Using thermal signature to evaluate heat stress levels in laying hens with a machine-learning-based classifier. Animals, 14.","DOI":"10.3390\/ani14131996"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101056","DOI":"10.1016\/j.atech.2025.101056","article-title":"Decoding vocal indicators of stress in laying hens: A CNN-MFCC deep learning framework","volume":"11","author":"Neethirajan","year":"2025","journal-title":"Smart Agric. Technol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2469","DOI":"10.1016\/j.psj.2019.11.062","article-title":"Effects of heat stress on pullet cloacal and body temperature","volume":"99","author":"Albino","year":"2020","journal-title":"Poult. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.physbeh.2018.04.004","article-title":"Surface temperature elevated by chronic and intermittent stress","volume":"191","author":"Herborn","year":"2018","journal-title":"Physiol. Behav."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Welch, M., Sibanda, T.Z., De Souza Vilela, J., Kolakshyapati, M., Schneider, D., and Ruhnke, I. (2023). An initial study on the use of machine learning and radio frequency identification data for predicting health outcomes in free-range laying hens. Animals, 13.","DOI":"10.3390\/ani13071202"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100607","DOI":"10.1016\/j.japr.2025.100607","article-title":"Circadian rhythm of locomotor activity and body temperature in free-range laying hens as measured by triaxial accelerometers and subcutaneous biologgers","volume":"34","author":"Abecia","year":"2025","journal-title":"J. Appl. Poult. Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Willems, S., Nijs, H., Sleeckx, N., and Norton, T. (2025). Monitoring Night-Time Activity Patterns of Laying Hens in Response to Poultry Red Mite Infestations Using Night-Vision Cameras. Animals, 15.","DOI":"10.3390\/ani15192928"},{"key":"ref_57","first-page":"120","article-title":"Russell and Burch\u2019s 3Rs then and now: The need for clarity in definition and purpose","volume":"54","author":"Tannenbaum","year":"2015","journal-title":"J. Am. Assoc. Lab. Anim. Sci."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3007","DOI":"10.1242\/jeb.147058","article-title":"Considering aspects of the 3Rs principles within experimental animal biology","volume":"220","author":"Sneddon","year":"2017","journal-title":"J. Exp. Biol."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"187","DOI":"10.2307\/1942661","article-title":"Pseudoreplication and the design of ecological field experiments","volume":"54","author":"Hurlbert","year":"1984","journal-title":"Ecol. Monogr."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"351","DOI":"10.2307\/2087176","article-title":"Ecological correlations and the behavior of individuals","volume":"15","author":"Robinson","year":"1950","journal-title":"Am. Sociol. Rev."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lazic, S.E., Clarke-Williams, C.J., and Munaf\u00f2, M.R. (2018). What exactly is \u2018N\u2019 in cell culture and animal experiments?. PLoS Biol., 16.","DOI":"10.1371\/journal.pbio.2005282"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"G\u00f3mez, Y., Stygar, A.H., Boumans, I.J., Bokkers, E.A., Pedersen, L.J., Niemi, J.K., Pastell, M., Manteca, X., and Llonch, P. (2021). A systematic review on validated precision livestock farming technologies for pig production and its potential to assess animal welfare. Front. Vet. Sci., 8.","DOI":"10.3389\/fvets.2021.660565"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+ AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.compag.2017.11.032","article-title":"Development of an early warning algorithm to detect sick broilers","volume":"144","author":"Zhuang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.biosystemseng.2013.06.004","article-title":"Development of an early warning system for a broiler house using computer vision","volume":"116","author":"Kashiha","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"285","DOI":"10.3382\/ps.2012-02589","article-title":"Genetic variations alter production and behavioral responses following heat stress in 2 strains of laying hens","volume":"92","author":"Mack","year":"2013","journal-title":"Poult. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"586","DOI":"10.3382\/ps\/pev028","article-title":"Robustness to chronic heat stress in laying hens: A meta-analysis","volume":"94","author":"Moreri","year":"2015","journal-title":"Poult. Sci."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kang, S., Kim, D.-H., Lee, S., Lee, T., Lee, K.-W., Chang, H.-H., Moon, B., Ayasan, T., and Choi, Y.-H. (2020). An acute, rather than progressive, increase in temperature-humidity index has severe effects on mortality in laying hens. Front. Vet. Sci., 7.","DOI":"10.3389\/fvets.2020.568093"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3382\/ps.2011-01497","article-title":"Skin surface temperature of broiler chickens is correlated to body core temperature and is indicative of their thermoregulatory status","volume":"91","author":"Giloh","year":"2012","journal-title":"Poult. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liakos, K.G., Busato, P., Moshou, D., Pearson, S., and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18.","DOI":"10.3390\/s18082674"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Li, G., Huang, Y., Chen, Z., Chesser, G.D., Purswell, J.L., Linhoss, J., and Zhao, Y. (2021). Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review. Sensors, 21.","DOI":"10.3390\/s21041492"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"4037","DOI":"10.1109\/TPAMI.2020.2992393","article-title":"Self-supervised visual feature learning with deep neural networks: A survey","volume":"43","author":"Jing","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s10462-024-10859-3","article-title":"Breaking the data barrier: A review of deep learning techniques for democratizing AI with small datasets","volume":"57","author":"Rather","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Saito, T., and Rehmsmeier, M. (2015). The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Carslake, C., Occhiuto, F., V\u00e1zquez-Diosdado, J.A., and Kaler, J. (2022). Repeatability and predictability of calf feeding behaviors\u2014Quantifying between- and within-individual variation for precision livestock farming. Front. Vet. Sci., 9.","DOI":"10.3389\/fvets.2022.827124"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"skab206","DOI":"10.1093\/jas\/skab206","article-title":"Disentangling data dependency using cross-validation strategies to evaluate prediction quality of cattle grazing activities using machine learning algorithms and wearable sensor data","volume":"99","author":"Bresolin","year":"2021","journal-title":"J. Anim. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Wang, J., Yu, Z., Chebel, R.C., and Yu, H. (2025). Impact of cross-validation designs on cattle behavior prediction using machine learning and deep learning models with tri-axial accelerometer data. Smart Agric. Technol., 101483.","DOI":"10.1016\/j.atech.2025.101483"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Mao, A., Huang, E., Gan, H., and Liu, K. (2022). FedAAR: A novel federated learning framework for animal activity recognition with wearable sensors. Animals, 12.","DOI":"10.20944\/preprints202206.0306.v1"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"100488","DOI":"10.1016\/j.eij.2024.100488","article-title":"A federated learning model for intelligent cattle health monitoring system using body area sensors and IoT","volume":"27","author":"Arshad","year":"2024","journal-title":"Egypt. Inform. J."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ganesan, L.P., and Krishnan, S. (2025). Blockchain-enabled, nature-inspired federated learning for cattle health monitoring. Informatics, 12.","DOI":"10.3390\/informatics12030057"}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/19\/3155\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:13:24Z","timestamp":1792037604000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/16\/19\/3155"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":80,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["ani16193155"],"URL":"https:\/\/doi.org\/10.3390\/ani16193155","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]}}}