Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Advancing precision livestock farming: integrating artificial intelligence and emerging technologies for sustainable livestock management

  • L. O. Tedeschi (Department of Animal Science, Texas A&M University) ;
  • Pablo Guarnido-Lopez (Department of Animal Science, Texas A&M University) ;
  • Hector M. Menendez III (Department of Animal Science, South Dakota State University) ;
  • Seongwon Seo (Chungnam National University)
  • Received : 2025.04.24
  • Accepted : 2025.07.17
  • Published : 2026.04.01
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Abstract

Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies, including red-green-blue and depth cameras, 3D imaging systems, and Internet of Things-enabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.

Keywords

Acknowledgement

The authors acknowledge partial support of the Texas A&M University Chancellor's Enhancing Development and Generating Excellence in Scholarship (EDGES) Fellowship, the United States Department of Agriculture - National Institute of Food and Agriculture (USDA-NIFA) Hatch Fund (09123): Development of Mathematical Nutrition Models to Assist with Smart Farming and Sustainable Production, and the National Animal Nutrition Program (NANP; https://animalnutrition.org), which is a National Research Support Project (NRSP-9) supported by agInnovation, the State Agricultural Experiment Stations, the Natural Resources Conservation Service, and Hatch Funds provided by the National Institute of Food and Agriculture, U.S. Department of Agriculture, Washington, DC.

References

  1. Berckmans D. Automatic on-line monitoring of animals by precision livestock farming. In: Geers R, Madec F, editors. Livestock production and society. International Society for Animal Hygiene (ISAH); 2004. pp. 27-30.
  2. Berckmans D. General introduction to precision livestock farming. Anim Front 2017;7:6-11. https://doi.org/10.2527/af.2017.0102
  3. Halachmi I, Guarino M. Editorial: precision livestock farming: a 'per animal' approach using advanced monitoring technologies. Animal 2016;10:1482-3. https://doi.org/10.1017/S1751731116001142
  4. Kleen JL, Guatteo R. Precision livestock farming: what does it contain and what are the perspectives? Animals 2023;13:779. https://doi.org/10.3390/ani13050779
  5. Berckmans D. Advances in precision livestock farming. Burleigh Dodds Science Publishing; 2021.
  6. Guarnido-Lopez P, Pi Y, Tao J, Mendes EDM, Tedeschi LO. Computer vision algorithms to help decision-making in cattle production. Anim Front 2024;14:11-22. https://doi.org/10.1093/af/vfae028
  7. González LA, Kyriazakis I, Tedeschi LO. Review: precision nutrition of ruminants: approaches, challenges and potential gains. Animal 2018;12:s246-61. https://doi.org/10.1017/S1751731118002288
  8. Han CS, Kaur U, Bai H, et al. Invited review: sensor technologies for real-time monitoring of the rumen environment. J Dairy Sci 2022;105:6379-404. https://doi.org/10.3168/jds.2021-20576
  9. Romano E, Brambilla M, Cutini M, et al. Increased cattle feeding precision from automatic feeding systems: considerations on technology spread and farm level perceived advantages in Italy. Animals 2023;13:3382. https://doi.org/10.3390/ani13213382
  10. Tedeschi LO, Fonseca MA, Muir JP, et al. A glimpse of the future in animal nutrition science. 2. Current and future solutions. Rev Bras Zootec 2017;46:452-69. https://doi.org/10.1590/s1806-92902017000500012
  11. Saar M, Edan Y, Godo A, Lepar J, Parmet Y, Halachmi I. A machine vision system to predict individual cow feed intake of different feeds in a cowshed. Animal 2022;16:100432. https://doi.org/10.1016/j.animal.2021.100432
  12. Wang S, Jiang H, Qiao Y, Jiang S, Lin H, Sun Q. The research progress of vision-based artificial intelligence in smart pig farming. Sensors 2022;22:6541. https://doi.org/10.3390/s22176541
  13. Tedeschi LO, Muir JP, Riley DG, Fox DG. The role of ruminant animals in sustainable livestock intensification programs. Int J Sustain Dev World Ecol 2015;22:452-65. https://doi.org/10.1080/13504509.2015.1075441
  14. Morrone S, Dimauro C, Gambella F, Cappai MG. Industry 4.0 and precision livestock farming (PLF): an up to date overview across animal productions. Sensors 2022;22:4319. https://doi.org/10.3390/s22124319
  15. Lamanna M, Bovo M, Cavallini D. Wearable collar technologies for dairy cows: a systematized review of the current applications and future innovations in precision livestock farming. Animals 2025;15:458. https://doi.org/10.3390/ani15030458
  16. Tekin K, Yurdakök Dikmen B, Kanca H, Guatteo R. Precision livestock farming technologies: novel direction of information flow. Ankara Üniv Vet Fak Derg 2021;68:193-212. https://doi.org/10.33988/auvfd.837485
  17. Abi-Rizk H, Jouan-Rimbaud Bouveresse D, Chamberland J, Cordella CBY. Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review. Anal Methods 2023;15:5410-40. https://doi.org/10.1039/D3AY01132A
  18. Dijkstra J, van Gastelen S, Dieho K, Nichols K, Bannink A. Review: rumen sensors: data and interpretation for key rumen metabolic processes. Animal 2020;14:s176-86. https://doi.org/10.1017/S1751731119003112
  19. Wu D, Yin X, Jiang B, Jiang M, Li Z, Song H. Detection of the respiratory rate of standing cows by combining the Deeplab V3+ semantic segmentation model with the phase-based video magnification algorithm. Biosyst Eng 2020;192:72-89. https://doi.org/10.1016/j.biosystemseng.2020.01.012
  20. Yaseer A, Chen H. A review of sensors and machine learning in animal farming. In: Proceedings of the 2021 IEEE 11th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER); 2021 Jul 27-31; Jiaxing, China. IEEE; 2021. pp. 747-52.
  21. Qiao Y, Guo Y, He D. Cattle body detection based on YOLOv5-ASFF for precision livestock farming. Comput Electron Agric 2023;204:107579. https://doi.org/10.1016/j.compag.2022.107579
  22. Korkmaz A, Agdas MT, Kosunalp S, Iliev T, Stoyanov I. Detection of threats to farm animals using deep learning models: a comparative study. Appl Sci 2024;14:6098. https://doi.org/10.3390/app14146098
  23. Rohan A, Rafaq MS, Hasan MJ, Asghar F, Bashir AK, Dottorini T. Application of deep learning for livestock behaviour recognition: a systematic literature review. Comput Electron Agric 2024;224:109115. https://doi.org/10.1016/j.compag.2024.109115
  24. Ramirez BC, Xin H, Halbur PG, et al. At the intersection of industry, academia, and government: how do we facilitate productive precision livestock farming in practice? Animals 2019;9:635. https://doi.org/10.3390/ani9090635
  25. Mallinger K, Corpaci L, Neubauer T, Tikász IE, Banhazi T. Unsupervised and supervised machine learning approach to assess user readiness levels for precision livestock farming technology adoption in the pig and poultry industries. Comput Electron Agric 2023;213:108239. https://doi.org/10.1016/j.compag.2023.108239
  26. Silvi R, Pereira LGR, Paiva CAV, et al. Adoption of precision technologies by Brazilian dairy farms: the farmer's perception. Animals 2021;11:3488. https://doi.org/10.3390/ani11123488
  27. Tedeschi LO, Mendes EDM. 13 Precision livestock farming tools for climate-smart feedyard operations. J Anim Sci 2023;101:326-7. https://doi.org/10.1093/jas/skad281.390
  28. Adams JM, Fernandes Jr. J, Fernandes M, Reis R, Tedeschi LO. PSII-19 a satellite-based decision-support tool to optimize profitability and environmental stewardship of cow-calf operations. J Anim Sci 2023;101:481. https://doi.org/10.1093/jas/skad281.571
  29. Fernandes MH, Fernandes J, Reis RA, Tedeschi LO. PSXII-9 application of machine learning algorithms to estimate tropical pasture biomass based on satellite images. J Anim Sci 2022;100:284-5. https://doi.org/10.1093/jas/skac247.518
  30. Fernandes MHMR, Tedeschi LO. 2 Satellite-based decision support tools to assist grazing cattle production. J Anim Sci 2023;101:74-5. https://doi.org/10.1093/jas/skad281.091
  31. Fernandes MHMR, de Souza Fernandes Junior J, Adams JM, Lee M, Reis RA, Tedeschi LO. Using sentinel-2 satellite images and machine learning algorithms to predict tropical pasture forage mass, crude protein, and fiber content. Sci Rep 2024;14:8704. https://doi.org/10.1038/s41598-024-59160-x
  32. Fernandes MHMR, Tedeschi LO. ASAS-NANP symposim: mathematical moedling in animal nutrition: application of modeling innovations to support satellite remote sensing for sustainable grazing cattle management. J Anim Sci 2025;103:skaf137. https://doi.org/10.1093/jas/skaf137
  33. Kamilaris A, Prenafeta-Boldú FX. A review of the use of convolutional neural networks in agriculture. J Agric Sci 2018;156:312-22. https://doi.org/10.1017/S0021859618000436
  34. Meng H, Zhang L, Yang F, et al. Livestock biometrics identification using computer vision approaches: a review. Agriculture 2025;15:102. https://doi.org/10.3390/agriculture15010102
  35. Hostiou N, Fagon J, Chauvat S, et al. Impact of precision livestock farming on work and human-animal interactions on dairy farms. A review. Biotechnol Agron Soc Environ 2017;21:268-75. https://doi.org/10.25518/1780-4507.13706
  36. Bezen R, Edan Y, Halachmi I. Computer vision system for measuring individual cow feed intake using RGB-D camera and deep learning algorithms. Comput Electron Agric 2020;172:105345. https://doi.org/10.1016/j.compag.2020.105345
  37. Guarnido Lopez PP, Ramirez-Agudelo F, Ribeira-Ferreiro E, Aouda MB. 301 Determining individual dry matter intake of Charolais bulls from ingestion time recorded with Rgb-D cameras. J Anim Sci 2023;101:247-8. https://doi.org/10.1093/jas/skad281.297
  38. Lowe G, Sutherland M, Waas J, Schaefer A, Cox N, Stewart M. Infrared thermography: a non-invasive method of measuring respiration rate in calves. Animals 2019;9:535. https://doi.org/10.3390/ani9080535
  39. Davison C, Bowen JM, Michie C, et al. Predicting feed intake using modelling based on feeding behaviour in finishing beef steers. Animal 2021;15:100231. https://doi.org/10.1016/j.animal.2021.100231
  40. Shangru L, Chengrui Z, Ruixue W, et al. Establishment of a feed intake prediction model based on eating time, ruminating time and dietary composition. Comput Electron Agric 2022;202:107296. https://doi.org/10.1016/j.compag.2022.107296
  41. Ding Y, Hua L, Li S. Research on computer vision enhancement in intelligent robot based on machine learning and deep learning. Neural Comput Appl 2022;34:2623-35. https://doi.org/10.1007/s00521-021-05898-8
  42. Nasirahmadi A, Edwards SA, Sturm B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livest Sci 2017;202:25-38. https://doi.org/10.1016/j.livsci.2017.05.014
  43. Rahman A, Smith DV, Little B, Ingham AB, Greenwood PL, Bishop-Hurley GJ. Cattle behaviour classification from collar, halter, and ear tag sensors. Inf Process Agric 2018;5:124-33. https://doi.org/10.1016/j.inpa.2017.10.001
  44. Gebreyesus G, Milkevych V, Lassen J, Sahana G. Supervised learning techniques for dairy cattle body weight prediction from 3D digital images. Front Genet 2023;13:947176. https://doi.org/10.3389/fgene.2022.947176
  45. Wang J, Dai B, Li Y, He Y, Sun Y, Shen W. An intelligent edge-IoT platform with deep learning for body condition scoring of dairy cow. IEEE Internet Things J 2024;11:17453-67. https://doi.or/10.1109/JIOT.2024.3357862
  46. Gjergji M, Weber VM, Silva LOC, et al. Deep learning techniques for beef cattle body weight prediction. In: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN); 2020 Jul 19-24; Glasgow, UK. IEEE; 2020. pp. 1-8.
  47. Ji W, Luo Y, Liao Y, et al. UAV assisted livestock distribution monitoring and quantification: a low-cost and high-precision solution. Animals 2023;13:3069. https://doi.org/10.3390/ani13193069
  48. Liu H, Reibman AR, Boerman JP. Feature extraction using multi-view video analytics for dairy cattle body weight estimation. Smart Agric Technol 2023;6:100359. https://doi.org/10.1016/j.atech.2023.100359
  49. Xu B, Wang W, Falzon G, et al. Automated cattle counting using Mask R-CNN in quadcopter vision system. Comput Electron Agric 2020;171:105300. https://doi.org/10.1016/j.compag.2020.105300
  50. Crowe MA, Hostens M, Opsomer G. Reproductive management in dairy cows - the future. Ir Vet J 2018;71:1. https://doi.org/10.1186/s13620-017-0112-y
  51. Das S, Shaji A, Nain D, Singha S, Karunakaran M, Baithalu RK. Precision technologies for the management of reproduction in dairy cows. Trop Anim Health Prod 2023;55:286. https://doi.org/10.1007/s11250-023-03704-2
  52. Roche JF, Mackey D, Diskin MD. Reproductive management of postpartum cows. Anim Prod Sci 2000;60-61:703-12. https://doi.org/10.1016/S0378-4320(00)00107-X
  53. Fricke PM, Carvalho PD, Giordano JO, Valenza A, Lopes Jr. G, Amundson MC. Expression and detection of estrus in dairy cows: the role of new technologies. Animal 2014;8:134-43. https://doi.org/10.1017/S1751731114000299
  54. Valenza A, Giordano JO, Lopes Jr. G, Vincenti L, Amundson MC, Fricke PM. Assessment of an accelerometer system for detection of estrus and treatment with gonadotropin-releasing hormone at the time of insemination in lactating dairy cows. J Dairy Sci 2012;95:7115-27. https://doi.org/10.3168/jds.2012-5639
  55. Madureira AML, Silper BF, Burnett TA, et al. Factors affecting expression of estrus measured by activity monitors and conception risk of lactating dairy cows. J Dairy Sci 2015;98:7003-14. https://doi.org/10.3168/jds.2015-9672
  56. Saint-Dizier M, Chastant-Maillard S. Methods and on-farm devices to predict calving time in cattle. Vet J 2015;205:349-56. https://doi.org/10.1016/j.tvjl.2015.05.006
  57. Reith S, Hoy S. Relationship between daily rumination time and estrus of dairy cows. J Dairy Sci 2012;95:6416-20. https://doi.org/10.3168/jds.2012-5316
  58. Pahl C, Hartung E, Mahlkow-Nerge K, Haeussermann A. Feeding characteristics and rumination time of dairy cows around estrus. J Dairy Sci 2015;98:148-54. https://doi.org/10.3168/jds.2014-8025
  59. Brehme U, Stollberg U, Holz R, Schleusener T. ALT pedometer: new sensor-aided measurement system for improvement in oestrus detection. Comput Electron Agric 2008;62:73-80. https://doi.org/10.1016/j.compag.2007.08.014
  60. Jung DH, Kim NY, Moon SH, et al. Deep learning-based cattle vocal classification model and real-time livestock monitoring system with noise filtering. Animals 2021;11:357. https://doi.org/10.3390/ani11020357
  61. Sharma S, Kadyan V. Detection of estrus through automated classification approaches using vocalization pattern in Murrah buffaloes. In: Proceedings of the 2023 3rd International Conference on Artificial Intelligence and Signal Processing (AISP); 2023 Mar 18-20; Vijayawada, India. IEEE; 2023. pp. 1-6.
  62. Wang Y, Kang X, He Z, Feng Y, Liu G. Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images. animal 2022;16:100646. https://doi.org/10.1016/j.animal.2022.100646
  63. Sejian V, Devaraj C, Shashank CG, et al. Heat stress in ruminants and its mitigation through nutritional additives. In: Mahesh MS, Yata VK, editors. Feed additives and supplements for ruminants. Springer Nature; 2024. pp. 367-98.
  64. Michelena Á, Aveleira-Mata J, Jove E, et al. A novel intelligent approach for man-in-the-middle attacks detection over Internet of things environments based on message queuing telemetry transport. Expert Syst 2024;41:e13263. https://doi.org/10.1111/exsy.13263
  65. Mylostyvyi R, Sejian V, Souza-Junior JBF, et al. Digitalisation opportunities for livestock welfare monitoring with a focus on heat stress. Multidiscip Rev 2024;7:2024300. https://doi.org/10.31893/multirev.2024300
  66. Rebez EB, Sejian V, Silpa MV, et al. Applications of artificial intelligence for heat stress management in ruminant livestock. Sensors 2024;24:5890. https://doi.org/10.3390/s24185890
  67. Ghassemi Nejad J, Ju MS, Jo JH, et al. Advances in methane emission estimation in livestock: a review of data collection methods, model development and the role of AI technologies. Animals 2024;14:435. https://doi.org/10.3390/ani14030435
  68. Kasimanickam R, Ferreira JCP, Kastelic J, Kasimanickam V. Application of genomic selection in beef cattle disease prevention. Animals 2025;15:277. https://doi.org/10.3390/ani15020277
  69. Hajnal É, Kovács L, Vakulya G. Dairy cattle rumen bolus developments with special regard to the applicable artificial intelligence (AI) methods. Sensors 2022;22:6812. https://doi.org/10.3390/s22186812
  70. Vargas-Bello-Pérez E, Espinoza-Sandoval OR, Gonzalez Ronquillo M, et al. The role of artificial intelligence in Latin American ruminant production systems. Anim Front 2024;14:23-32. https://doi.org/10.1093/af/vfae034
  71. Phillips N, Mottram T, Poppi D, Mayer D, McGowan MR. Continuous monitoring of ruminal pH using wireless telemetry. Anim Prod Sci 2009;50:72-7. https://doi.org/10.1071/AN09027
  72. Campuzano S, Pedrero M, Yáñez-Sedeño P, Pingarrón JM. Antifouling (bio)materials for electrochemical (bio)sensing. Int J Mol Sci 2019;20:423. https://doi.org/10.3390/ijms20020423
  73. Neubauer V, Humer E, Kröger I, Braid T, Wagner M, Zebeli Q. Differences between pH of indwelling sensors and the pH of fluid and solid phase in the rumen of dairy cows fed varying concentrate levels. J Anim Physiol Anim Nutr 2018;102:343-9. https://doi.org/10.1111/jpn.12675
  74. Crossland WL, Cagle CM, Sawyer JE, Callaway TR, Tedeschi LO. Evaluation of active dried yeast in the diets of feedlot steers. II. Effects on rumen pH and liver health of feedlot steers. J Anim Sci 2019;97:1347-63. https://doi.org/10.1093/jas/skz008
  75. Crossland WL, Jobe JT, Ribeiro FRB, Sawyer JE, Callaway TR, Tedeschi LO. Evaluation of active dried yeast in the diets of feedlot steers—I: effects on feeding performance traits, the composition of growth, and carcass characteristics. J Anim Sci 2019;97:1335-46. https://doi.org/10.1093/jas/skz007
  76. Cusack PMV, Dell'Osa D, Wilkes G, Grandini D, Tedeschi LO. Ruminal pH and its relationship with dry matter intake, growth rate, and feed conversion ratio in commercial Australian feedlot cattle fed for 148days. Aust Vet J 2021;99:319-25. https://doi.org/10.1111/avj.13069
  77. Dias Batista LF, Rivera ME, Tedeschi LO. Investigation of virginiamycin to improve health of growing and finishing steers: II. animal growth and development, and intake dynamics. Appl Anim Sci 2024;40:487-503. https://doi.org/10.15232/aas.2023-02510
  78. Rivera ME, Dias Batista LF, Tedeschi LO. Investigation of virginiamycin to improve health of growing and finishing steers: I. effects on ruminal acidosis and liver health. Appl Anim Sci 2024;40:329-46. https://doi.org/10.15232/aas.2023-02511
  79. Tarafdar A, Sheikh A, Majumder P, et al. Enhancing intrusion detection using wireless sensor networks: a novel ahp-madm aggregated multiple type 3 fuzzy logic-based k-barriers prediction system. Peer Peer Netw Appl 2024;17:1732-49. https://doi.org/10.1007/s12083-024-01688-w
  80. Tedeschi LO, Greenwood PL, Halachmi I. Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming. J Anim Sci 2021;99:1-11. https://doi.org/10.1093/jas/skab038
  81. Gupta S, Nirmala TV, Reddy AD, Kumar D. Smart livestock farming: monitoring and health management through IoT. In: Gupta S, Hasan W, Singh S, Kumar D, Ansari MJ, Nisar S, editors. Agriculture 4.0. CRC Press; 2024.
  82. Fuentes-Peñailillo F, Gutter K, Vega R, Silva GC. Transformative technologies in digital agriculture: leveraging Internet of things, remote sensing, and artificial intelligence for smart crop management. J Sens Actuator Netw 2024;13:39. https://doi.org/10.3390/jsan13040039
  83. Vlaicu PA, Gras MA, Untea AE, Lefter NA, Rotar MC. Advancing livestock technology: intelligent systemization for enhanced productivity, welfare, and sustainability. AgriEngineering 2024;6:1479-96. https://doi.org/10.3390/agriengineering6020084
  84. Alonso RS, Sittón-Candanedo I, García Ó, Prieto J, Rodríguez-González S. An intelligent edge-IoT platform for monitoring livestock and crops in a dairy farming scenario. Ad Hoc Netw 2020;98:102047. https://doi.org/10.1016/j.adhoc.2019.102047
  85. Caria M, Schudrowitz J, Jukan A, Kemper N. Smart farm computing systems for animal welfare monitoring. In: Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); 2017 May 22-26; Opatija, Croatia. IEEE; 2017. pp. 152-7.
  86. National Academies of Sciences, Engineering, and Medicine. Nutrient requirements of beef cattle. 8th ed. The National Academies Press; 2016.
  87. National Academies of Sciences, Engineering, and Medicine. Nutrient requirements of dairy cattle. 8th ed. The National Academies Press; 2021.
  88. Tedeschi LO, Fox DG. The ruminant nutrition system: an applied model for predicting nutrient requirements and feed utilization in ruminants. 3rd ed. Kendall Hunt; 2020.
  89. Tedeschi LO, Fox DG. The ruminant nutrition system: tables of equations and coding. 1st ed. Kendall Hunt; 2020.
  90. Pomar C, Pomar J, Babot D, Dubeau F. Effet d'une alimentation multiphase quotidienne sur les performances zootechniques, la composition corporelle et les rejets d'azote et de phosphore du porc charcutier. J Recherche Porcine 2007;39:23-30.
  91. Pomar C, Pomar J, Dubeau F, Joannopoulos E, Dussault JP. The impact of daily multiphase feeding on animal performance, body composition, nitrogen and phosphorus excretions, and feed costs in growing-finishing pigs. Animal 2014;8:704-13. https://doi.org/10.1017/S1751731114000408
  92. Awasthi TR, Morshed A, Swain DL. A machine learning approach to simulate cattle growth at pasture using remotely collected walk-over weights. Agric Syst 2025;226:104332. https://doi.org/10.1016/j.agsy.2025.104332
  93. Tedeschi LO. Review: the prevailing mathematical modeling classifications and paradigms to support the advancement of sustainable animal production. animal 2023;17:100813. https://doi.org/10.1016/j.animal.2023.100813
  94. Allen MS. Do more mechanistic models increase accuracy of prediction of metabolisable protein supply in ruminants? Anim Prod Sci 2019;59:1991-8. https://doi.org/10.1071/AN19337
  95. Neethirajan S, Kemp B. Digital twins in livestock farming. Animals [Preprint]. 2021 [cited 2025 Jan 1]. https://doi.org/10.20944/preprints202101.0620.v1
  96. Neethirajan S. Twin farms nexus: digital twins for sustainable animal farming. Arch Anim Poult Sci 2024;2:555595. https://doi.org/10.19080/AAPS.2024.02.555595
  97. Tedeschi LO. ASAS-NANP symposium: mathematical modeling in animal nutrition: the progression of data analytics and artificial intelligence in support of sustainable development in animal science. J Anim Sci 2022;100:1-11. https://doi.org/10.1093/jas/skac111
  98. Monteiro HF, Figueiredo CC, Mion B, et al. An artificial intelligence approach of feature engineering and ensemble methods depicts the rumen microbiome contribution to feed efficiency in dairy cows. Anim Microbiome 2024;6:5. https://doi.org/10.1186/s42523-024-00289-5
  99. Fontanesi L. Metabolomics and livestock genomics: insights into a phenotyping frontier and its applications in animal breeding. Anim Front 2016;6:73-9. https://doi.org/10.2527/af.2016-0011
  100. de Novais FJ, Yu H, Cesar ASM, et al. Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. Front Genet 2022;13:948240. https://doi.org/10.3389/fgene.2022.948240
  101. Lee M, Kim DH, Seo S, Tedeschi LO. Development of machine learning models for estimating metabolizable protein supply from feed in lactating dairy cows. Animals 2025;15:687. https://doi.org/10.3390/ani15050687
  102. Eastwood CR, Chapman DF, Paine MS. Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia. Agric Syst 2012;108:10-8. https://doi.org/10.1016/j.agsy.2011.12.005
  103. Groenendaal H, Galligan DT, Mulder HA. An economic spreadsheet model to determine optimal breeding and replacement decisions for dairy cattle. J Dairy Sci 2004;87:2146-57. https://doi.org/10.3168/jds.S0022-0302(04)70034-X
  104. Wolfert S, Ge L, Verdouw C, Bogaardt MJ. Big data in smart farming: a review. Agric Syst 2017;153:69-80. https://doi.org/10.1016/j.agsy.2017.01.023
  105. Zuidhof MJ. Precision livestock feeding: matching nutrient supply with nutrient requirements of individual animals. J Appl Poult Res 2020;29:11-4. https://doi.org/10.1016/j.japr.2019.12.009
  106. Ghazal S, Munir A, Qureshi WS. Computer vision in smart agriculture and precision farming: techniques and applications. Artif Intell Agric 2024;13:64-83. https://doi.org/10.1016/j.aiia.2024.06.004
  107. Nyamuryekung'e S. Transforming ranching: precision livestock management in the Internet of things era. Rangelands 2024;46:13-22. https://doi.org/10.1016/j.rala.2023.10.002
  108. Jiang B, Tang W, Cui L, Deng X. Precision livestock farming research: a global scientometric review. Animals 2023;13:2096. https://doi.org/10.3390/ani13132096
  109. Balkrishna A, Pathak R, Kumar S, Arya V, Singh SK. A comprehensive analysis of the advances in Indian digital agricultural architecture. Smart Agric Technol 2023;5:100318. https://doi.org/10.1016/j.atech.2023.100318
  110. Raihan A, Ridwan M, Rahman MS. An exploration of the latest developments, obstacles, and potential future pathways for climate-smart agriculture. Clim Smart Agric 2024;1:100020. https://doi.org/10.1016/j.csag.2024.100020
  111. Pomar C, Remus A. Review: fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming. animal 2023;17:100763. https://doi.org/10.1016/j.animal.2023.100763
  112. Kling-Eveillard F, Allain C, Boivin X, et al. Farmers' representations of the effects of precision livestock farming on human-animal relationships. Livest Sci 2020;238:104057. https://doi.org/10.1016/j.livsci.2020.104057
  113. Tedeschi LO. 6 Transforming animal agriculture through hybrid modeling and quantum computing. J Anim Sci 2024;102:70-1. https://doi.org/10.1093/jas/skae234.078
  114. Nastasijevic I, Kundacina I, Jaric S, Pavlovic Z, Radovic M, Radonic V. Recent advances in biosensor technologies for meat production chain. Foods 2025;14:744. https://doi.org/10.3390/foods14050744
  115. Yang Y, Lin M, Lin Y, Zhang C, Wu C. A survey of blockchain applications for management in agriculture and livestock Internet of things. Future Internet 2025;17:40. https://doi.org/10.3390/fi17010040
  116. Cui S, Lyu S, Ma Y, Wang K. Improved informer PV power short-term prediction model based on weather typing and AHA-VMD-MPE. Energy 2024;307:132766. https://doi.org/10.1016/j.energy.2024.132766
  117. Gupta GK, Singh A, Manikandan SV, Ehtesham A. Digital diagnostics: the potential of large language models in recognizing symptoms of common illnesses. AI 2025;6:13. https://doi.org/10.3390/ai6010013
  118. Al Mamun MA, Sarker MR, Sarkar MAR, et al. Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm. Sci Rep 2024;14:566. https://doi.org/10.1038/s41598-023-51111-2
  119. Babina T, Fedyk A, He A, Hodson J. Artificial intelligence, firm growth, and product innovation. J Financ Econ 2024;151:103745. https://doi.org/10.1016/j.jfineco.2023.103745
  120. de Oliveira FM, Ferraz GAS, André ALG, Santana LS, Norton T, Ferraz PFP. Digital and precision technologies in dairy cattle farming: a bibliometric analysis. Animals 2024;14:1832. https://doi.org/10.3390/ani14121832
  121. Bao Y, Llagostera P, Plà-Aragonès LM. Is deep learning useful for decision making in pig production? Internet Things 2024;26:101229. https://doi.org/10.1016/j.iot.2024.101229
  122. Neethirajan S. From predictive analytics to emotional recognition-the evolving landscape of cognitive computing in animal welfare. Int J Cogn Comput Eng 2024;5:123-31. https://doi.org/10.1016/j.ijcce.2024.02.003
  123. Wang H, Liu J, Dong Z, Song J, Zhu Z. Artificial intelligence-based metabolic energy prediction model for animal feed proportioning optimization. Ital J Anim Sci 2023;22:942-52. https://doi.org/10.1080/1828051X.2023.2236132
  124. Gao G, Wang C, Wang J, et al. CNN-Bi-LSTM: a complex environment-oriented cattle behavior classification network based on the fusion of CNN and Bi-LSTM. Sensors 2023;23:7714. https://doi.org/10.3390/s23187714
  125. Li G, Shi G, Zhu C. Dynamic serpentine convolution with attention mechanism enhancement for beef cattle behavior recognition. Animals 2024;14:466. https://doi.org/10.3390/ani14030466
  126. Qiao Y, Guo Y, Yu K, He D. C3D-ConvLSTM based cow behaviour classification using video data for precision livestock farming. Comput Electron Agric 2022;193:106650. https://doi.org/10.1016/j.compag.2021.106650
  127. Akinsulie OC, Idris I, Aliyu VA, et al. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Front Vet Sci 2024;11:1347550. https://doi.org/10.3389/fvets.2024.1347550
  128. Hoorn QA, Rabaglino MB, Amaral TF, et al. Machine learning to identify endometrial biomarkers predictive of pregnancy success following artificial insemination in dairy cows. Biol Reprod 2024;111:54-62. https://doi.org/10.1093/biolre/ioae052
  129. Tzanidakis C, Simitzis P, Panagakis P. Precision livestock farming (PLF) systems: improving sustainability and efficiency of animal production. In: García Márquez FP, Lev B, editors. Sustainability: cases and studies in using operations research and management science methods. Springer; 2023. pp. 285-337.
  130. Tedeschi LO. Quantifying ruminal health: a statistical review and application of area and time under the curve in animal science. Ecol Inform 2025;90:103271. https://doi.org/10.1016/j.ecoinf.2025.103271
  131. Tedeschi LO. ASAS-NANP symposium: mathematical modeling in animal nutrition: synthetic database generation for non-normal multivariate distributions: a rank-based method with application to ruminant methane emissions. J Anim Sci 2025;103:skaf136. https://doi.org/10.1093/jas/skaf136