Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Application of precision livestock farming: challenges and opportunities

  • Received : 2025.11.22
  • Accepted : 2026.02.20
  • Published : 2026.04.01
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Abstract

Sensor systems have increasingly been explored as tools to support precision livestock farming, particularly in monitoring cow health and improving decision-making. This systematic literature review aims to evaluate advancements in sensor systems for detecting health conditions in dairy cows especially on mastitis, fertility, locomotion, and metabolic disorders. Relevant articles published between 2014 and 2024 were identified from Scopus. Each article was categorized by health condition and assigned to one of four development levels: sensor technique (Level I), data interpretation (Level II), integration of information (Level III), and decision making (Level IV). Relevant information from the articles was systematically reviewed and discussed. We identified 132 articles published in the past 10 years, describing a total of 151 sensor systems. Most sensor systems were aimed at mastitis and reproduction, followed by locomotion and metabolic disorders. The far majority of the articles were at level II (data interpretation) presenting research on (novel) algorithms to detect disease. A large number of different statistical, machine-learning or deep-learning models were described and evaluated, among others random forests. Level II systems applied statistical analysis or machine-learning/deep-learning models (e.g., random forests, you only look once, support vector machine, or convolutional neural network). These algorithms used a wide range of sensor data. Only a few articles aimed at level III research, integration of information and decision support. The Level III sensor systems integrated information from the sensor with economic information and other information (i.e., medication dosage, cost per disease, and supplier selection) and simulated various treatment scenarios. This review highlights the need for sensor systems research to be driven by real-world requirements for on-farm decision making. To move from proof-of-concept toward practical, future research must integrate sensor outputs with herd records and financial models, validate systems across multiple farms and at higher data frequencies, and embed economic evaluation alongside sensitivity and specificity metrics. Addressing these technical, integration, and economic challenges is essential before sensor systems can fully support automated, value-driven health management on commercial dairy farms.

Keywords

Acknowledgement

This research is part of the Smart-In-Ag project, funded by the Wageningen Global Sustainability Fund. This specific research was possible by a grant of Indonesian Education Scholarship, Center for Higher Education Funding and Assessment, Ministry of Higher Education, Science, and Technology of Republic Indonesia and Indonesian Endowment Fund for Education.

References

  1. Berckmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci Tech 2014;33:189-96. https://doi.org/10.20506/rst.33.1.2273
  2. Norton T, Berckmans D. Developing precision livestock farming tools for precision dairy farming. Animal Front 2017;7:18-23. https://doi.org/10.2527/af.2017.0104
  3. Rutten CJ, Velthuis AGJ, Steeneveld W, Hogeveen H. Invited review: sensors to support health management on dairy farms. J Dairy Sci 2013;96:1928-52. https://doi.org/10.3168/jds.2012-6107
  4. Tangorra FM, Buoio E, Calcante A, Bassi A, Costa A. Internet of things (IoT): sensors application in dairy cattle farming. Animals 2024;14:3071. https://doi.org/10.3390/ani14213071
  5. Dolecheck K, Bewley J. Animal board invited review: dairy cow lameness expenditures, losses and total cost. Animal 2018;12:1462-74. https://doi.org/10.1017/S1751731118000575
  6. Rasmussen P, Barkema HW, Osei PP, et al. Global losses due to dairy cattle diseases: a comorbidity-adjusted economic analysis. J Dairy Sci 2024;107:6945-70. https://doi.org/10.3168/jds.2023-24626
  7. Berckmans D. General introduction to precision livestock farming. Animal Front 2017;7:6-11. https://doi.org/10.2527/af.2017.0102
  8. Cox S. Precision livestock farming. Wageningen Academic Publishers; 2003.
  9. Russell RA, Bewley JM. Characterization of kentucky dairy producer decision-making behavior. J Dairy Sci 2013;96:4751-8. https://doi.org/10.3168/jds.2012-6538
  10. Steeneveld W, Vernooij JCM, Hogeveen H. Effect of sensor systems for cow management on milk production, somatic cell count, and reproduction. J Dairy Sci 2015;98:3896-905. https://doi.org/10.3168/jds.2014-9101
  11. Krisnawijaya NNK, Tekinerdogan B, Catal C, van der Tol R. Data analytics platforms for agricultural systems: a systematic literature review. Comput Electron Agric 2022;195:106813. https://doi.org/10.1016/j.compag.2022.106813
  12. Tedeschi LO, Greenwood PL, Halachmi I. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. J Anim Sci 2021;99:skab038. https://doi.org/10.1093/jas/skab038
  13. Petticrew M, Roberts H. Systematic reviews in the social sciences: a practical guide. Blackwell; 2006. p. 323.
  14. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. https://doi.org/10.1136/bmj.n71
  15. Cabrera EV. DairyMGT: a suite of decision support systems in dairy farm management. In: Jao C editor. Decision support systems. IntechOpen; 2012.
  16. Laschinger J, Fuerst-Waltl B, Fuerst L, Linnenkohl S, Pesenhofer R, Kofler J. Evaluation of 'in-parlour scoring' (IPS) to detect lameness in dairy cows during milking. Animals 2024;14:2870. https://doi.org/10.3390/ani14192870
  17. Dutton-Regester KJ, Barnes TS, Wright JD, Rabiee AR. Lameness in dairy cows: farmer perceptions and automated detection technology. J Diary Res 2020;87:67-71. https://doi.org/10.1017/S0022029920000497
  18. James LM, Mayes MS, Siberski-Cooper CJ, Breitzman MW, Vandehaar MJ, Koltes JE. Association of milk metabolites with feed intake and traits impacting feed efficiency in lactating holstein dairy cows. Front Anim Sci 2024;5:1393996. https://doi.org/10.3389/fanim.2024.1393996
  19. Anglart D, Emanuelson U, Rönnegård L, Sandgren HC. Detecting and predicting changes in milk homogeneity using data from automatic milking systems. J Dairy Sci 2021;104:11009-17. https://doi.org/10.3168/jds.2021-20517
  20. He L, Chen B, Hu Y, Hu B, Li Y, Yang X. A sample-preparation-free, point-of-care testing system for in situ detection of bovine mastitis. Anal Bioanal Chem 2023;415:5499-509. https://doi.org/10.1007/s00216-023-04823-3
  21. Gayathri SL, Bhakat M, Mohanty TK. Thermographic assessment of mastitis progression in sahiwal cattle: insights into the patterns in the natural course of infection. Microb Pathog 2024;196:106964. https://doi.org/10.1016/j.micpath.2024.106964
  22. Sathiyabarathi M, Jeyakumar S, Manimaran A, et al. Infrared thermography: a potential noninvasive tool to monitor udder health status in dairy cows. Vet World 2016;9:1075-81. https://doi.org/10.14202/vetworld.2016.1075-1081
  23. Zhang Q, Yang Y, Liu G, Ning Y, Li J. Dairy cow mastitis detection by thermal infrared images based on CLE-UNet. Animals 2023;13:2211. https://doi.org/10.3390/ani13132211
  24. Fan X, Watters RD, Nydam DV, Virkler PD, Wieland M, Reed KF. Multivariable time series classification for clinical mastitis detection and prediction in automated milking systems. J Dairy Sci 2023;106:3448-64. https://doi.org/10.3168/jds.2022-22355
  25. Liebe DM, Steele NM, Petersson-Wolfe CS, De Vries A, White RR. Practical challenges and potential approaches to predicting low-incidence diseases on farm using individual cow data: a clinical mastitis example. J Dairy Sci 2022;105:2369-79. https://doi.org/10.3168/jds.2021-20306
  26. Saro J, Šubrt T, Brožová H, et al. A decision support system for herd health management for dairy farms. Czech J Anim Sci 2024;69:502-15. https://doi.org/10.17221/178/2024-CJAS
  27. Röttgen V, Schön PC, Becker F, et al. Automatic recording of individual oestrus vocalisation in group-housed dairy cattle: development of a cattle call monitor. Animal 2020;14:198-205. https://doi.org/10.1017/S1751731119001733
  28. Antanaitis R, Anskienė L, Palubinskas G, et al. Ruminating, eating, and locomotion behavior registered by innovative technologies around calving in dairy cows. Animals 2023;13:1257. https://doi.org/10.3390/ani13071257
  29. Mozūraitis R, Kutra J, Borg-Karlson AK, Būda V. Dynamics of putative sex pheromone components during heat periods in estrus-induced cows. J Dairy Sci 2017;100:7686-95. https://doi.org/10.3168/jds.2016-12376
  30. Eckelkamp EA, Bewley JM. On-farm use of disease alerts generated by precision dairy technology. J Dairy Sci 2020;103:1566-82. https://doi.org/10.3168/jds.2019-16888
  31. Ren Y, Duhatschek D, Bartolomeu CC, Erickson D, Giordano JO. An automated system for cattle reproductive management under the IoT framework. Part I: the e-Synch system and cow responses. Front Anim Sci 2023;4:1093851. https://doi.org/10.3389/fanim.2023.1093851
  32. Arago NM, Robles RR, Alvarez C, et al. Smart dairy cattle farming and in-heat detection through the internet of things (IoT). Int J Integr Eng 2022;14:157-72. https://doi.org/10.30880/ijie.2022.14.01.014
  33. Pfeiffer J, Spykman O, Gandorfer M. Sensor and video: two complementary approaches for evaluation of dairy cow behavior after calving sensor attachment. Animals 2021;11:1917. https://doi.org/10.3390/ani11071917
  34. Hut PR, Kuiper SEM, Nielen M, Hulsen JHJL, Stassen EN, Hostens MM. Sensor based time budgets in commercial dutch dairy herds vary over lactation cycles and within 24 hours. PLOS ONE 2022;17:e0264392. https://doi.org/10.1371/journal.pone.0264392
  35. Mayo LM, Silvia WJ, Ray DL, et al. Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows. J Dairy Sci 2019;102:2645-56. https://doi.org/10.3168/jds.2018-14738
  36. Pereira GM, Heins BJ, Endres MI. Estrous detection with an activity and rumination monitoring system in an organic grazing and a low-input conventional dairy herd. Anim Reprod Sci 2020;221:106553. https://doi.org/10.1016/j.anireprosci.2020.106553
  37. Schodl K, Stygar A, Steininger F, Egger-Danner C. Sensor data cleaning for applications in dairy herd management and breeding. Front Anim Sci 2024;5:1444948. https://doi.org/10.3389/fanim.2024.1444948
  38. Balasso P, Taccioli C, Serva L, Magrin L, Andrighetto I, Marchesini G. Uncovering patterns in dairy cow behaviour: a deep learning approach with tri-axial accelerometer data. Animals 2023;13:1886. https://doi.org/10.3390/ani13111886
  39. Beer G, Alsaaod M, Starke A, et al. Use of extended characteristics of locomotion and feeding behavior for automated identification of lame dairy cows. PLOS ONE 2016;11:e0155796. https://doi.org/10.1371/journal.pone.0155796
  40. Borghart GM, O'Grady LE, Somers JR. Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient irish dairy cows. Ir Vet J 2021;74:4. https://doi.org/10.1186/s13620-021-00182-6
  41. Dutta D, Natta D, Mandal S, Ghosh N. MOOnitor: An IoT based multi-sensory intelligent device for cattle activity monitoring. Sens Actuators A Phys 2022;333:113271. https://doi.org/10.1016/j.sna.2021.113271
  42. Zhang K, Han S, Wu J, et al. Early lameness detection in dairy cattle based on wearable gait analysis using semisupervised LSTM-autoencoder. Comput Electron Agric 2023;213:108252. https://doi.org/10.1016/j.compag.2023.108252
  43. Ferreira REP, de Luis Balaguer MA, et al. Multi-modal machine learning for the early detection of metabolic disorder in dairy cows using a cloud computing framework. Comput Electron Agric 2024;227. https://doi.org/10.1016/j.compag.2024.109563
  44. Heirbaut S, Jing XP, Stefańska B, et al. Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period. J Dairy Sci 2024;107:489-507. https://doi.org/10.3168/jds.2023-23693
  45. Taechachokevivat N, Kou B, Zhang T, et al. Evaluating the performance of herd-specific long short-term memory models to identify automated health alerts associated with a ketosis diagnosis in early-lactation cows. J Dairy Sci 2024;107:11489-501. https://doi.org/10.3168/jds.2023-24513
  46. Simoni A, König F, Weimar K, et al. Evaluation of sensor-based health monitoring in dairy cows: exploiting rumination times for health alerts around parturition. J Dairy Sci 2024;107:6052-64. https://doi.org/10.3168/jds.2023-24313
  47. Hogeveen H, Klaas IC, Dalen G, et al. Novel ways to use sensor data to improve mastitis management. J Dairy Sci 2021;104:11317-32. https://doi.org/10.3168/jds.2020-19097
  48. Edwardes F, van der Voort M, Hogeveen H. The economics of sensor-based management of dairy cow suboptimal mobility. J Dairy Sci 2022;105:9682-701. https://doi.org/10.3168/jds.2021-21726
  49. Steeneveld W, Hogeveen H. Characterization of dutch dairy farms using sensor systems for cow management. J Dairy Sci 2015;98:709-17. https://doi.org/10.3168/jds.2014-8595
  50. Groher T, Heitkämper K, Umstätter C. Digital technology adoption in livestock production with a special focus on ruminant farming. Animal 2020;14:2404-13. https://doi.org/10.1017/S1751731120001391
  51. Palma-Molina P, Hennessy T, O'Connor AH, et al. Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems. J Dairy Sci 2023;106:2498-509. https://doi.org/10.3168/jds.2021-21503
  52. Bianchi MC, Bava L, Sandrucci A, et al. Diffusion of precision livestock farming technologies in dairy cattle farms. Animal 2022;16:100650. https://doi.org/10.1016/j.animal.2022.100650
  53. Van der Voort M, Hogeveen H, Kamphuis C. Principles to determine the economic value of sensor technologies used on dairy farms. In Beede DK, editor. Large dairy herd management. American Dairy Science Association; 2017. pp. 1293-304.
  54. Jensen DB, Hogeveen H, De Vries A. Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis. J Dairy Sci 2016;99:7344-61. https://doi.org/10.3168/jds.2015-10060
  55. Lien CC, Wan YN, Ting CH. Online detection of dairy cow subclinical mastitis using electrical conductivity indices of milk. Eng Agric Environ Food 2016;9:201-7. https://doi.org/10.1016/j.eaef.2015.12.002
  56. Jensen DB, van der Voort M, Hogeveen H. Dynamic forecasting of individual cow milk yield in automatic milking systems. J Dairy Sci 2018;101:10428-39. https://doi.org/10.3168/jds.2017-14134
  57. Khatun M, Thomson PC, Kerrisk KL, et al. Development of a new clinical mastitis detection method for automatic milking systems. J Dairy Sci 2018;101:9385-95. https://doi.org/10.3168/jds.2017-14310
  58. Paudyal S, Maunsell FP, Richeson JT, Risco CA, Donovan DA, Pinedo PJ. Rumination time and monitoring of health disorders during early lactation. Animal 2018;12:1484-92. https://doi.org/10.1017/S1751731117002932
  59. Ebrahimi M, Mohammadi-Dehcheshmeh M, Ebrahimie E, Petrovski KR. Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: deep learning and gradient-boosted trees outperform other models. Comput Biol Med 2019;114:103456. https://doi.org/10.1016/j.compbiomed.2019.103456
  60. Khatun M, Bruckmaier RM, Thomson PC, House J, García SC. Suitability of somatic cell count, electrical conductivity, and lactate dehydrogenase activity in foremilk before versus after alveolar milk ejection for mastitis detection. J Dairy Sci 2019;102:9200-12. https://doi.org/10.3168/jds.2018-15752
  61. Post C, Rietz C, Büscher W, Müller U. Using sensor data to detect lameness and mastitis treatment events in dairy cows: a comparison of classification models. Sensors 2020;20:3863. https://doi.org/10.3390/s20143863
  62. Steele NM, Dicke A, De Vries A, et al. Identifying gramnegative and gram-positive clinical mastitis using daily milk component and behavioral sensor data. J Dairy Sci 2020;103:2602-14. https://doi.org/10.3168/jds.2019-16742
  63. Anglart D, Hallén-Sandgren C, Emanuelson U, Rönnegård L. Comparison of methods for predicting cow composite somatic cell counts. J Dairy Sci 2020;103:8433-42. https://doi.org/10.3168/jds.2020-18320
  64. Ebrahimie E, Mohammadi-Dehcheshmeh M, Laven R, Petrovski KR. Rule discovery in milk content towards mastitis diagnosis: dealing with farm heterogeneity over multiple years through classification based on associations. Animals 2021;11:1683. https://doi.org/10.3390/ani11061638
  65. Post C, Rietz C, Büscher W, Müller U. The importance of low daily risk for the prediction of treatment events of individual dairy cows with sensor systems. Sensors 2021;21:1389. https://doi.org/10.3390/s21041389
  66. Robles I, Nolan DT, Fendley CA, et al. Technical note: evaluation of a commercial on-farm milk leukocyte differential tester to identify subclinical mastitis cases in dairy cows. J Dairy Sci 2021;104:4942-9. https://doi.org/10.3168/jds.2020-19299
  67. Tsai IC, Mayo LM, Jones BW, Stone AE, Janse SA, Bewley JM. Precision dairy monitoring technologies use in disease detection: differences in behavioral and physiological variables measured with precision dairy monitoring technologies between cows with or without metritis, hyperketonemia, and hypocalcemia. Livest Sci 2021;244:104334. https://doi.org/10.1016/j.livsci.2020.104334
  68. Bausewein M, Mansfeld R, Doherr MG, Harms J, Sorge US. Sensitivity and specificity for the detection of clinical mastitis by automatic milking systems in bavarian dairy herds. Animals 2022;12:2131. https://doi.org/10.3390/ani12162131
  69. Bonestroo J, van der Voort M, Hogeveen H, Emanuelson U, Klaas IC, Fall N. Forecasting chronic mastitis using automatic milking system sensor data and gradient-boosting classifiers. Comput Electron Agric 2022;198:107002. https://doi.org/10.1016/j.compag.2022.107002
  70. Naqvi SA, King MTM, DeVries TJ, Barkema HW, Deardon R. Data considerations for developing deep learning models for dairy applications: a simulation study on mastitis detection. Comput Electron Agric 2022;196:106895. https://doi.org/10.1016/j.compag.2022.106895
  71. Naqvi SA, King MTM, Matson RD, DeVries TJ, Deardon R, Barkema HW. Mastitis detection with recurrent neural networks in farms using automated milking systems. Comput Electron Agric 2022;192:106618. https://doi.org/10.1016/j.compag.2021.106618
  72. Yang K, Fang D, Li Y, Guo W, Zhu X. A novel determination method of milk somatic cell count based on dielectric relaxation parameters. LWT-Food Sci Technol 2023;188:115424. https://doi.org/10.1016/j.lwt.2023.115424
  73. Cho SH, Lee M, Lee WH, Seo S, Lee DH. Mastitis classification in dairy cows using weakly supervised representation learning. Agriculture 2024;14:2084. https://doi.org/10.3390/agriculture14112084
  74. D'Anvers L, Adriaens I, Piepers S, Gote MJ, De Ketelaere B, Aernouts B. Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms. Prev Vet Med 2023;220:106033. https://doi.org/10.1016/j.prevetmed.2023.106033
  75. Faris DN, Gad AM, El-Tarabany MS, Ramadan SI, Afifi GG, Manaa EA. Machine learning algorithms for clinical mastitis prediction in a dairy herd use automated milking system data. J Adv Vete Res 2024;14:975-81.
  76. Jin C, Upton J, Bazargani MHZ, Mac Namee B. Using milk flow profiles for subclinical mastitis detection. Smart Agric Technol 2024;9:100537. https://doi.org/10.1016/j.atech.2024.100537
  77. Olofsson C, Toftaker I, Rachah A, Reksen O, Kielland C. Pathogen-specific patterns of milking traits in automatic milking systems. J Dairy Sci 2024;107:6035-51. https://doi.org/10.3168/jds.2023-23933
  78. Khatun M, Clark CEF, Lyons NA, Thomson PC, Kerrisk KL, Garciá SC. Early detection of clinical mastitis from electrical conductivity data in an automatic milking system. Ani Prod Sci 2017;57:1226-32. https://doi.org/10.1071/AN16707
  79. Ebrahimie E, Ebrahimi F, Ebrahimi M, Tomlinson S, Petrovski KR. A large-scale study of indicators of sub-clinical mastitis in dairy cattle by attribute weighting analysis of milk composition features: highlighting the predictive power of lactose and electrical conductivity. J Diary Res 2018;85:193-200. https://doi.org/10.1017/S0022029918000249
  80. Xudong Z, Xi K, Ningning F, Gang L. Automatic recognition of dairy cow mastitis from thermal images by a deep learning detector. Comput Electron Agric 2020;178:105754. https://doi.org/10.1016/j.compag.2020.105754
  81. Ramirez-Morales I, Aguilar L, Fernandez-Blanco E, Rivero D, Perez J, Pazos A. Detection of bovine mastitis in raw milk, using a low-cost NIR spectrometer and K-NN algorithm. Appl Sci 2021;11:10751. https://doi.org/10.3390/app112210751
  82. Wang Z, Wang S, Wang C, et al. A non-contact cow estrus monitoring method based on the thermal infrared images of cows. Agriculture 2023;13:385. https://doi.org/10.3390/agriculture13020385
  83. Wang Y, Chu M, Kang X, Liu G. A deep learning approach combining DeepLabV3+ and improved YOLOv5 to detect dairy cow mastitis. Comput Electron Agric 2024;216:108507. https://doi.org/10.1016/j.compag.2023.108507
  84. Khatun M, Thomson PC, Clark CEF, García SC. Prediction of quarter level subclinical mastitis by combining in-line and on-animal sensor data. Ani Prod Sci 2019;60:180-6. https://doi.org/10.1071/AN18578
  85. Feng Y, Niu H, Wang F, Ivey SJ, Wu JJ, Qi H, et al. Socialcattle: IoT-based mastitis detection and control through social cattle behavior sensing in smart farms. IEEE Internet Things J 2022;9:10130-8. https://doi.org/10.1109/JIOT.2021.3122341
  86. Grodkowski G, Szwaczkowski T, Koszela K, et al. Early detection of mastitis in cows using the system based on 3D motions detectors. Sci Rep 2022;12:21215. https://doi.org/10.1038/s41598-022-25275-2
  87. Hu T, Zhang J, Zhang X, Chen Y, Zhang R, Guo K. The development of smart dairy farm system and its application in nutritional grouping and mastitis prediction. Animals 2023;13:804. https://doi.org/10.3390/ani13050804
  88. Antanaitis R, Anskienė L, Palubinskas G, Rutkauskas A, Baumgartner W. The relationship between reticuloruminal temperature, reticuloruminal pH, cow activity, and clinical mastitis in dairy cows. Animals 2023;13:2134. https://doi.org/10.3390/ani13132134
  89. Kuma A, Sindhwani M, Sachdeva S. Detection of mastitis disease in cow with machine learning classifiers. Israa Univ J Appl Sci 2023;7:112-29. https://doi.org/10.52865/DQFZ6016
  90. Rodriguez Z, Kolar QK, Krogstad KC, et al. Evaluation of reticuloruminal temperature for the prediction of clinical mastitis in dairy cows challenged with streptococcus uberis. J Dairy Sci 2023;106:1360-9. https://doi.org/10.3168/jds.2022-22421
  91. Vidal G, Sharpnack J, Pinedo P, Tsai IC, Lee AR, Martínez-López B. Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle. Prev Vet Med 2023;215:105903. https://doi.org/10.1016/j.prevetmed.2023.105903
  92. Zhou X, Xu C, Zhao Z, Wang H, Chen M, Jia B. Prediction of health disorders in dairy cows monitored with collar based on Binary logistic analysis. Arq Bras Med Vet Zootec 2023;75:467-75. https://doi.org/10.1590/1678-4162-12880
  93. Khan MF, Thorup VM, Luo Z. Delineating mastitis cases in dairy cows: development of an IoT-enabled intelligent decision support system for dairy farms. IEEE Trans Ind Inf 2024;20:9508-17. https://doi.org/10.1109/TII.2024.3384594
  94. Nadeem G, Anis MI. Investigation of bovine disease and events through machine learning models. Pak J Agric Res 2024;37:102-14. https://doi.org/10.17582/JOURNAL.PJAR/2024/37.2.102.114
  95. Santos MGS, Antonacci N, Van Dorp C, Mion B, Tulpan D, Ribeiro ES. Use of rumination time in health risk assessment of prepartum dairy cows. J Dairy Sci 2024;107:8343-65. https://doi.org/10.3168/jds.2023-24610
  96. Tian H, Zhou X, Wang H, et al. The prediction of clinical mastitis in dairy cows based on milk yield, rumination time, and milk electrical conductivity using machine learning algorithms. Animals 2024;14:427. https://doi.org/10.3390/ani14030427
  97. Reith S, Pries M, Verhülsdonk C, Brandt H, Hoy S. Influence of estrus on dry matter intake, water intake and BW of dairy cows. Animal 2014;8:748-53. https://doi.org/10.1017/S1751731114000494
  98. Silper BF, Madureira AML, Polsky LB, et al. Daily lying behavior of lactating holstein cows during an estrus synchronization protocol and its associations with fertility. J Dairy Sci 2017;100:8484-95. https://doi.org/10.3168/jds.2016-12160
  99. Brassel J, Rohrssen F, Failing K, Wehrend A. Automated oestrus detection using multimetric behaviour recognition in seasonal-calving dairy cattle on pasture. New Zealand Vet J 2018;66:243-7. https://doi.org/10.1080/00480169.2018.1479316
  100. Hamilton AW, Davison C, Tachtatzis C, et al. Identification of the rumination in cattle using support vector machines with motion-sensitive bolus sensors. Sensors 2019;19:1165. https://doi.org/10.3390/s19051165
  101. Schweinzer V, Gusterer E, Kanz P, et al. Evaluation of an ear-attached accelerometer for detecting estrus events in indoor housed dairy cows. Theriogenology 2019;130:19-25. https://doi.org/10.1016/j.theriogenology.2019.02.038
  102. Lovarelli D, Tamburini A, Mattachini G, et al. Relating lying behavior with climate, body condition score, and milk production in dairy cows. Front Vet Sci 2020;7:565415. https://doi.org/10.3389/fvets.2020.565415
  103. Belaid MA, Rodriguez-Prado M, López-Suárez M, Rodríguez-Prado DV, Calsamiglia S. Prepartum behavior changes in dry Holstein cows at risk of postpartum diseases. J Dairy Sci 2021;104:4575-83. https://doi.org/10.3168/jds.2020-18792
  104. Lopreiato V, Ghaffari MH, Cattaneo L, et al. Suitability of rumination time during the first week after calving for detecting metabolic status and lactation performance in simmental dairy cows: a cluster-analytic approach. Ital J Anim Sci 2021;20:1909-23. https://doi.org/10.1080/1828051X.2021.1963862
  105. Macmillan K, Gobikrushanth M, Plastow G, Colazo MG. Natural versus induced estrus indicators of Holstein heifers determined by an automated activity monitoring system. Livest Sci 2021;249:104514. https://doi.org/10.1016/j.livsci.2021.104514
  106. Rachah A, Reksen O, Tafintseva V, et al. Exploring dry-film FTIR spectroscopy to characterize milk composition and subclinical ketosis throughout a cow's lactation. Foods 2021;10:2033. https://doi.org/10.3390/foods10092033
  107. Schilkowsky EM, Granados GE, Sitko EM, Masello M, Perez MM, Giordano JO. Evaluation and characterization of estrus alerts and behavioral parameters generated by an ear-attached accelerometer-based system for automated detection of estrus. J Dairy Sci 2021;104:6222-37. https://doi.org/10.3168/jds.2020-19667
  108. Fadul M, D'Andrea L, Alsaaod M, et al. Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving: a retrospective clinical study to early detect spontaneous disease appearance. PLOS ONE 2022;17:e0264834. https://doi.org/10.1371/journal.pone.0264834
  109. Higaki S, Matsui Y, Sasaki Y, et al. Prediction of 24-h and 6-h periods before calving using a multimodal tail-attached device equipped with a thermistor and 3-axis accelerometer through supervised machine learning. Animals 2022;12:2095. https://doi.org/10.3390/ani12162095
  110. Wang J, Zhang Y, Bell M, Liu G. Potential of an activity index combining acceleration and location for automated estrus detection in dairy cows. Inf Process Agric 2022;9:288-99. https://doi.org/10.1016/j.inpa.2021.04.003
  111. Wang J, Zhang Y, Wang J, Zhao K, Li X, Liu B. Using machine-learning technique for estrus onset detection in dairy cows from acceleration and location data acquired by a neck-tag. Biosyst Eng 2022;214:193-206. https://doi.org/10.1016/j.biosystemseng.2021.12.025
  112. Zhou X, Xu C, Wang H, et al. The early prediction of common disorders in dairy cows monitored by automatic systems with machine learning algorithms. Animals 2022;12:1251. https://doi.org/10.3390/ani12101251
  113. Arıkan İ, Ayav T, Seçkin AÇ, Soygazi F. Estrus detection and dairy cow identification with cascade deep learning for augmented reality-ready livestock farming. Sensors 2023;23:9795. https://doi.org/10.3390/s23249795
  114. Cook J. Association between prepartum alerts generated using a commercial monitoring system and health and production outcomes in multiparous dairy cows in five uk herds. Animals 2023;13:3235. https://doi.org/10.3390/ani13203235
  115. Jia Q, Yang J, Han S, Du Z, Liu J. CAMLLA-YOLOv8n: cow behavior recognition based on improved YOLOv8n. Animals 2024;14:3033. https://doi.org/10.3390/ani14203033
  116. Leliveld LMC, Lovarelli D, Finzi A, Riva E, Provolo G. Effects of cow reproductive status, parity and lactation stage on behaviour and heavy breathing indications of a commercial accelerometer during hot weather conditions. Int J Biometeorol 2023;67:1263-72. https://doi.org/10.1007/s00484-023-02496-2
  117. Lodkaew T, Pasupa K, Loo CK. CowXNet: an automated cow estrus detection system. Expert Sys Appl 2023;211:118550. https://doi.org/10.1016/j.eswa.2022.118550
  118. Zhou X, Xu C, Lopes MG, et al. Characterization of the physical and ruminal activities related to estrus in dairy cows raised on Chinese commercial farms. Vet Arh 2023;93:143-58. https://doi.org/10.24099/vet.arhiv.1533
  119. Khin MP, Tin P, Horii Y, Zin TT. Predictive modeling of cattle calving time emphasizing abnormal and normal cases by using posture analysis. Sci Rep 2024;14:31871. https://doi.org/10.1038/s41598-024-83279-6
  120. Li D, Dai B, Li Y, et al. IATEFF-YOLO: focus on cow mounting detection during nighttime. Biosyst Eng 2024;246:54-66. https://doi.org/10.1016/j.biosystemseng.2024.07.017
  121. Mladenova T, Valova I, Evstatiev B, et al. Evaluation of the efficiency of machine learning algorithms for identification of cattle behavior using accelerometer and gyroscope data. AgriEngineering 2024;6:2179-97. https://doi.org/10.3390/agriengineering6030128
  122. Rial C, Stangaferro ML, Thomas MJ, Giordano JO. Effect of automated health monitoring based on rumination, activity, and milk yield alerts versus visual observation on herd health monitoring and performance outcomes. J Dairy Sci 2024;107:11576-96. https://doi.org/10.3168/jds.2024-25256
  123. Shokrollahi B, Morammazi S, Dananiani K, Tarjoman H. Infrared thermometry for detecting estrus and pregnancy in Holstein cows. J Therm Biol 2024;125:103972. https://doi.org/10.1016/j.jtherbio.2024.103972
  124. Adriaens I, van den Brulle I, D'Anvers L, et al. Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage. J Dairy Sci 2021;104:405-18. https://doi.org/10.3168/jds.2020-19195
  125. Higaki S, Miura R, Suda T, et al. Estrous detection by continuous measurements of vaginal temperature and conductivity with supervised machine learning in cattle. Theriogenology 2019;123:90-9. https://doi.org/10.1016/j.theriogenology.2018.09.038
  126. Vicentini RR, Montanholi YR, Veroneze R, et al. Infrared thermography reveals surface body temperature changes during proestrus and estrus reproductive phases in Gyr heifers (Bos taurus indicus). J Therm Biol 2020;92:102662. https://doi.org/10.1016/j.jtherbio.2020.102662
  127. Huot F, Claveau S, Bunel A, Santschi DE, Gervais R, Paquet ÉR. Relationship between farm management strategies, reticuloruminal pH variations, and risks of subacute ruminal acidosis. J Dairy Sci 2023;106:2487-97. https://doi.org/10.3168/jds.2022-22509
  128. Risvanli A, Tanyeri B, Yildirim G, et al. Metrisor: a novel diagnostic method for metritis detection in cattle based on machine learning and sensors. Theriogenology 2024;223:115-21. https://doi.org/10.1016/j.theriogenology.2024.05.002
  129. Chin W, Steeneveld W, Nielen M, Christensen J, Klaas IC, Lam TJGM. The association between time-series milk β-hydroxybutyrate dynamics and early reproductive performance of dairy cows. J Dairy Sci 2024;107:6025-34. https://doi.org/10.3168/jds.2023-24184
  130. Benaissa S, Tuyttens FAM, Plets D, et al. Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors. Comput Electron Agric 2020;168:105153. https://doi.org/10.1016/j.compag.2019.105153
  131. Cairo FC, Pereira LGR, Campos MM, et al. Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers. Comput Electron Agric 2020;179:105855. https://doi.org/10.1016/j.compag.2020.105855
  132. Gusterer E, Kanz P, Krieger S, et al. Sensor technology to support herd health monitoring: using rumination duration and activity measures as unspecific variables for the early detection of dairy cows with health deviations. Theriogenology 2020;157:61-9. https://doi.org/10.1016/j.theriogenology.2020.07.028
  133. Frondelius L, Van Weyenberg S, Lindeberg H, Van Nuffel A, Maselyne J, Pastell M. Spatial behaviour of dairy cows is affected by lameness. Appl Anim Behav Sci 2022;256:105763. https://doi.org/10.1016/j.applanim.2022.105763
  134. Brouwers SP, Simmler M, Savary P, Scriba MF. Towards a novel method for detecting atypical lying down and standing up behaviors in dairy cows using accelerometers and machine learning. Smart Agric Technol 2023;4:100199. https://doi.org/10.1016/j.atech.2023.100199
  135. Ismail S, Diaz M, Carmona-Duarte C, Vilar JM, Ferrer MA. CowScreeningDB: a public benchmark database for lameness detection in dairy cows. Comput Electron Agric 2024;216:108500. https://doi.org/10.1016/j.compag.2023.108500
  136. Neupane R, Aryal A, Haeussermann A, Hartung E, Pinedo P, Paudyal S. Evaluating machine learning algorithms to predict lameness in dairy cattle. PLOS ONE 2024;19:e0301167. https://doi.org/10.1371/journal.pone.0301167
  137. Peng Y, Chen Y, Yang Y, et al. A multimodal classification method: cow behavior pattern classification with improved EdgeNeXt using an inertial measurement unit. Comput Electron Agric 2024;226:109453. https://doi.org/10.1016/j.compag.2024.109453
  138. Russel NS, Selvaraj A. Decoding cow behavior patterns from accelerometer data using deep learning. J Vet Behav 2024;74:68-78. https://doi.org/10.1016/j.jveb.2024.06.005
  139. Van Hertem T, Schlageter Tello A, Viazzi S, et al. Implementation of an automatic 3D vision monitor for dairy cow locomotion in a commercial farm. Biosyst Eng 2018;173:166-75. https://doi.org/10.1016/j.biosystemseng.2017.08.011
  140. Piette D, Norton T, Exadaktylos V, Berckmans D. Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance. Animal 2020;14:409-17. https://doi.org/10.1017/S1751731119001642
  141. Warner D, Vasseur E, Lefebvre DM, Lacroix R. A machine learning based decision aid for lameness in dairy herds using farm-based records. Comput Electron Agric 2020;169:105193. https://doi.org/10.1016/j.compag.2019.105193
  142. Brezov D, Hristov H, Dimov D, Alexiev K. Predicting the rectal temperature of dairy cows using infrared thermography and multimodal machine learning. Appl Sci 2023;13:11416. https://doi.org/10.3390/app132011416
  143. Hua Z, Wang Z, Xu X, Kong X, Song H. An effective Posec3D model for typical action recognition of dairy cows based on skeleton features. Comput Electron Agric 2023;212:108152. https://doi.org/10.1016/j.compag.2023.108152
  144. Mon SL, Onizuka T, Tin P, Aikawa M, Kobayashi I, Zin TT. Ai-enhanced real-time cattle identification system through tracking across various environments. Sci Rep 2024;14:17779. https://doi.org/10.1038/s41598-024-68418-3
  145. Garcia E, Klaas I, Amigo JM, Bro R, Enevoldsen C. Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis. J Dairy Sci 2014;97:7476-86. https://doi.org/10.3168/jds.2014-7982
  146. Grimm K, Haidn B, Erhard M, Tremblay M, Döpfer D. New insights into the association between lameness, behavior, and performance in Simmental cows. J Dairy Sci 2019;102:2453-68. https://doi.org/10.3168/jds.2018-15035
  147. Lemmens L, Schodl K, Fuerst-Waltl B, et al. The combined use of automated milking system and sensor data to improve detection of mild lameness in dairy cattle. Animals 2023;13:1180. https://doi.org/10.3390/ani13071180
  148. Jukna V, Meškinytė E, Urbonavičius G, et al. Association of lameness prevalence and severity in early-lactation cows with milk traits, metabolic profile, and dry period. Agriculture 2024;14:2030. https://doi.org/10.3390/agriculture14112030
  149. Michelena Á, Jove E, Fontenla-Romero Ó, Calvo-Rolle JL. Evaluation of one-class techniques for early estrus detection on galician intensive dairy cow farm based on behavioral data from activity collars. Adv Distrib Comput Artif Intell J 2024;13:e32508. https://doi.org/10.14201/adcaij.32508
  150. Swain S, Pattnayak BK, Mohanty MN, Jayasingh SK, Patra KJ, Panda C. Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning. Indones J Electrical Eng Comput Sci 2024;34:1192-203. https://doi.org/10.11591/ijeecs.v34.i2.pp1192-1203
  151. Antanaitis R, Džermeikaitė K, Januškevičius V, Šimonytė I, Baumgartner W. In-line registered milk fat-to-protein ratio for the assessment of metabolic status in dairy cows. Animals 2023;13:3293. https://doi.org/10.3390/ani13203293
  152. Giannuzzi D, Mota LFM, Pegolo S, et al. In-line nearinfrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle. Sci Rep 2022;12:8058. https://doi.org/10.1038/s41598-022-11799-0
  153. Satoła A, Bauer EA. Predicting subclinical ketosis in dairy cows using machine learning techniques. Animals 2021;11:2131. https://doi.org/10.3390/ani11072131
  154. Antanaitis R, Juozaitienė V, Džermeikaitė K, et al. Change in rumination behavior parameters around calving in cows with subclinical ketosis diagnosed during 30 days after calving. Animals 2023;13:595. https://doi.org/10.3390/ani13040595
  155. Rial C, Laplacette A, Caixeta L, Florentino C, Peña-Mosca F, Giordano JO. Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor. J Dairy Sci 2023;106:9323-44. https://doi.org/10.3168/jds.2022-23156
  156. Stangaferro ML, Wijma R, Caixeta LS, Al-Abri MA, Giordano JO. Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders. J Dairy Sci 2016;99:7395-410. https://doi.org/10.3168/jds.2016-10907
  157. Heirbaut S, Jensen DB, Jing XP, et al. Different reticuloruminal pH metrics of high-yielding dairy cattle during the transition period in relation to metabolic health, activity, and feed intake. J Dairy Sci 2022;105:6880-94. https://doi.org/10.3168/jds.2021-21751