Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Autoři: Kaitlin Wurtz aff001;  Irene Camerlink aff002;  Richard B. D’Eath aff003;  Alberto Peña Fernández aff004;  Tomas Norton aff004;  Juan Steibel aff001;  Janice Siegford aff001
Působiště autorů: Department of Animal Science, Michigan State University, East Lansing, Michigan, United States of America aff001;  Department of Farm Animals and Veterinary Public Health, Institute of Animal Welfare Science, University of Veterinary Medicine Vienna, Vienna, Austria aff002;  Animal Behaviour & Welfare, Animal and Veterinary Sciences, Scotland’s Rural College (SRUC), Edinburgh, United Kingdom aff003;  M3-BIORES– Measure, Model & Manage Bioresponses, KU Leuven, Leuven, Belgium aff004;  Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

Klíčová slova:

Algorithms – Animal behavior – Birds – Cameras – Computer vision – Livestock – Poultry – Swine


1. Weary DM, Huzzey JM, Von Keyserlingk MAG. Board-invited review: Using behavior to predict and identify ill health in animals. J Anim Sci. 2009;87(2):770–7. doi: 10.2527/jas.2008-1297 18952731

2. Dawkins MS. Using behaviour to assess animal welfare. Anim Welf. 2004;13(1):3–7.

3. Andreasen SN, Sandøe P, Forkman B. Can animal-based welfare assessment be simplified? A comparison of the Welfare Quality® protocol for dairy cattle and the simpler and less timeconsuming protocol developed by the Danish Cattle Federation. Anim Welf. 2014;23(1):81–94.

4. Pandolfi F, Stoddart K, Wainwright N, Kyriazakis I, Edwards SA. The “Real Welfare” scheme: Benchmarking welfare outcomes for commercially farmed pigs. Animal. 2017;11(10):1816–24. doi: 10.1017/S1751731117000246 28249629

5. D’Eath RB, Turner SP, Kurt E, Evans G, Thölking L, Looft H, et al. Pigs’ aggressive temperament affects pre-slaughter mixing aggression, stress and meat quality. Animal. 2010;4(4):604–16. doi: 10.1017/S1751731109991406 22444048

6. Turner SP. Breeding against harmful social behaviours in pigs and chickens: State of the art and the way forward. Appl Anim Behav Sci. 2011;134(1–2):1–9.

7. Desire S, Turner SP, D’Eath RB, Lewis CRG, Roehe R. Prediction of reduction in aggressive behaviour of growing pigs using skin lesion traits as selection criteria. Animal. 2016;10(8):1243–53. doi: 10.1017/S1751731116000112 26857289

8. D’Eath RB, Jack M, Futro A, Talbot D, Zhu Q, Barclay D, et al. Automatic early warning of tail biting in pigs : 3D cameras can detect lowered tail posture before an outbreak. PLoS One. 2018;13(4):1–18.

9. 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(1):124–33.

10. Banhazi TM, Babinszky L, Halas V, Tscharke M. Precision Livestock Farming: Precision feeding technologies and sustainable livestock production. Int J Agric Biol Eng. 2012;5(4):54–61.

11. Fournel S, Rousseau AN, Laberge B. Rethinking environment control strategy of confined animal housing systems through precision livestock farming. Biosyst Eng. 2017;155:96–123.

12. Berckmans D. General introduction to precision livestock farming. Anim Front. 2017;7(1):6–11.

13. Nóbrega L, Gonçalves P, Pedreiras P, Pereira J. An IoT-based solution for intelligent farming. Sensors. 2019;19(3):603.

14. Shi X, An X, Zhao Q, Liu H, Xia L, Sun X, et al. State-of-the-art internet of things in protected agriculture. Sensors. 2019;19(8):1833.

15. Nasirahmadi A, Hensel O, Edwards SA, Sturm B. A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method. Animal. 2017;11(1):131–9. doi: 10.1017/S1751731116001208 27353419

16. Franco NH, Gerós A, Oliveira L, Olsson IAS, Aguiar P. ThermoLabAnimal—A high-throughput analysis software for non-invasive thermal assessment of laboratory mice. Physiol Behav. 2019;207:113–21. doi: 10.1016/j.physbeh.2019.05.004 31078672

17. Noldus LPJJ, Spink AJ, Tegelenbosch RAJ. EthoVision: A versatile video tracking system for automation of behavioral experiments. Behav Res Methods, Instruments, Comput. 2001;33(3):398–414.

18. Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi: 10.1371/journal.pmed.1000097 19621072

19. Perner P. Motion tracking of animals for behavior analysis. 2001. 779–786 p.

20. Stajnko D, Brus M, Hočevar M. Estimation of bull live weight through thermographically measured body dimensions. Comput Electron Agric. 2008;1:233–40.

21. Pezzuolo A, Guarino M, Sartori L, Marinello F. A Feasibility Study on the Use of a Structured Light Measurements of Dairy Cows in Free-Stall Barns. Sensors. 2018;18(2):673.

22. Lee J, Jin L, Park D, Chung Y. Automatic recognition of aggressive behavior in pigs using a Kinect depth sensor. Sensors. 2016;16(5):631.

23. Gronskyte R, Clemmensen LH, Hviid MS, Kulahci M. Pig herd monitoring and undesirable tripping and stepping prevention. Comput Electron Agric. 2015;119:51–60.

24. Kim J, Chung Y, Choi Y, Sa J, Kim H, Chung Y, et al. Depth-based detection of standing-pigs in moving noise environments. Sensors. 2017;17(12):2757.

25. Kim J, Choi Y, Ju M, Sa J, Chung Y, Park D, et al. Lying-pig detection using depth information. In: ICACS ‘17. 2017. p. 40–3.

26. Nir O, Parmet Y, Werner D, Adin G, Halachmi I. 3D Computer-vision system for automatically estimating heifer height and body mass. Biosyst Eng [Internet]. 2018;173:4–10. Available from:

27. Le Cozler Y, Allain C, Xavier C, Depuille L, Caillot A, Delouard JM, et al. Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation. Comput Electron Agric [Internet]. 2019;165:104977. Available from:

28. Le Cozler Y, Allain C, Caillot A, Delouard JM, Delattre L, Luginbuhl T, et al. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Comput Electron Agric [Internet]. 2019;157:447–53. Available from:

29. Mcfarlane NJB, Schofield CP. Segmentation and tracking of piglets in images. Mach Vis Appl. 1995;8:187–93.

30. Shao J, Xin H, Harmon JD. Comparison of image feature extraction for classification of swine thermal comfort behavior. Comput Electron Agric. 1998;19(3):223–32.

31. Xin H. Assessing swine thermal comfort by image analysis of postural behaviors. J Anim Sci. 1999;77(Suppl. 2):1–9.

32. Xin H. Real-time assessment of swine thermal comfort by computer vision. In: Proceedings of the World Congress of Computers in Agriculture and Natural Resources. 2002. p. 362–9.

33. Shao J, Xin H, Harmon JD. Neural network analysis of postural behavior of young swine to determine the IR thermal comfort state. Trans ASAE. 1997;40(3):755–60.

34. Shao B, Xin H. A real-time computer vision assessment and control of thermal comfort for group-housed pigs. Comput Electron Agric. 2008;62(1):15–21.

35. Baek H, Chung Y, Ju M, Chung Y, Park D. Segmentation of group-housed pigs using concave points and edge information. In: 19th International Conference on Advanced Communication Technology (ICACT). 2017. p. 563–5.

36. Ju M, Chung Y, Baek H, Chung Y, Park D, Park B. Segmentation methods for a group-housed pig monitoring system. J Theor Appl Inf Technol. 2017;95(17):4321–9.

37. Ju M, Seo J, Chung Y, Park D, Kim H. Touching-pigs segmentation using concave points in continuous video frames. In: Proceedings of the International Conference on Algorithms, Computing and Systems. 2017. p. 14–8.

38. Oczak M, Maschat K, Berckmans D, Vranken E, Baumgartner J. Automatic estimation of number of piglets in a pen during farrowing, using image analysis. Biosyst Eng. 2016;151:81–9.

39. Meyer F. Topographic distance and watershed lines. Signal Processing. 1994;38:113–25.

40. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9(1):62–6.

41. Nilsson M, Ardö H, Åström K, Herlin A, Bergsten C, Guzhva O. Learning based image segmentation of pigs in a pen. In: Visual observation and analysis of vertebrate and insect behavior—Workshop at the 22nd International Conference on Pattern Recognition (ICPR 2014). 2014. p. 24–8.

42. Nilsson M, Herlin AH, Guzhva O, Åström K, Ardö H, Bergsten C. Continuous surveillance of pigs in a pen using learning-based segmentation in computer vision. In: Precision livestock farming applications: Making sense of sensors to support farm managment. 2015. p. 25–35.

43. Nilsson M, Herlin AH, Ardö H, Guzhva O, Åström K, Bergsten C. Development of automatic surveillance of animal behaviour and welfare using image analysis and machine learned segmentation technique. Animal. 2015;9(11):1859–65. doi: 10.1017/S1751731115001342 26189971

44. Ma C, Zhu W, Li H, Li X. Pig target extraction based on adaptive elliptic block and wavelet edge detection. In: Proceedings of the 8th International Conference on Signal Processing Systems. 2016. p. 11–5.

45. Buayai P, Kantanukul T, Leung CK, Saikaew KR. Boundary detection of pigs in pens based on adaptive thresholding using an integral image and adaptive partitioning. C J Nat Sci. 2017;16(2):145–55.

46. Khoramshahi E, Hietaoja J, Valros A, Yun J, Pastell M. Real-time recognition of sows in video: A supervised approach. Inf Process Agric. 2014;1(1):73–81.

47. Tu GJ, Karstoft H, Pedersen LJ, Jørgensen E. Segmentation of sows in farrowing pens. IET Image Process. 2013;8(1):56–68.

48. Bloemen H, Aerts J, Berckmans D, Goedseels V. Image analysis to measure activity index of animals. Equine Vet J. 1997;29(S23):16–9.

49. Guo Y, Zhu W, Jiao P, Chen J. Foreground detection of group-housed pigs based on the combination of Mixture of Gaussians using prediction mechanism and threshold segmentation. Biosyst Eng. 2014;125:98–104.

50. Guo Y, Zhu W, Jiao P, Ma C, Yang J. Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation. Biosyst Eng. 2015;135:54–60.

51. Zhu W, Pu X, Li X, Zhu X. Automated detection of sick pigs based on machine vision. In: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems. 2009. p. 790–4.

52. Lind NM, Vinther M, Hemmingsen RP, Hansen AK. Validation of a digital video tracking system for recording pig locomotor behaviour. J Neurosci Methods. 2005;143:123–32. doi: 10.1016/j.jneumeth.2004.09.019 15814144

53. Ott S, Moons CPH, Kashiha MA, Bahr C, Tuyttens FAM, Berckmans D, et al. Automated video analysis of pig activity at pen level highly correlates to human observations of behavioural activities. Livest Sci. 2014;160:132–7.

54. Costa A, Ismayilova G, Borgonovo F, Leroy T, Berckmans D, Guarino M. The use of image analysis as a new approach to assess behavior classification in a pig barn. Acta Vet Brno. 2013;82(1):25–30.

55. Costa A, Ismayilova G, Borgonovo F, Viazzi S, Berckmans D, Guarino M. Image-processing technique to measure pig activity in response to climatic variation in a pig barn. Anim Prod Sci. 2014;54(8):1075–83.

56. Kashiha MA, Bahr C, Ott S, Moons CPH, Niewold TA, Tuyttens F, et al. Automatic monitoring of pig activity using image analysis. In: International Conference on Advanced Concepts for Intelligent Vision Systems. 2013. p. 555–63.

57. Chung Y, Kim H, Lee H, Park D, Jeon T, Chang H-H. A cost-effective pigsty monitoring system based on a video sensor. KSII Trans Internet Inf Syst. 2014;8(4):1481–98.

58. Martínez-Avilés M, Fernández-Carrión E, López García-Baones JM, Sánchez-Vizcaíno JM. Early detection of infection in pigs through an online monitoring system. Transbound Emerg Dis. 2017;64(2):364–73. doi: 10.1111/tbed.12372 25955521

59. Gronskyte R, Clemmensen LH, Hviid MS, Kulahci M. Monitoring pig movement at the slaughterhouse using optical flow and modified angular histograms. Biosyst Eng. 2016;141:19–30.

60. Fernández-Carrión E, Martínez-Avilés M, Ivorra B, Martínez-López B, Ramos ÁM, Sánchez-Vizcaíno JM. Motion-based video monitoring for early detection of livestock diseases: The case of African swine fever. PLoS One. 2017;12(9):e0183793. doi: 10.1371/journal.pone.0183793 28877181

61. Kulikov VA, Khotskin NV., Nikitin SV., Lankin VS, Kulikov AV., Trapezov. Application of 3-D imaging sensor for tracking minipigs in the open field test. J Neurosci Methods. 2014;235:219–25. doi: 10.1016/j.jneumeth.2014.07.012 25066208

62. Matthews SG, Miller AL, Plötz T, Kyriazakis I. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci Rep. 2017;7(1):17582. doi: 10.1038/s41598-017-17451-6 29242594

63. Kuhn HW. The Hungarian method for the assignment problem. Nav Res Logist Q. 1955;2(1–2):83–97.

64. Mittek M, Psota ET, Pérez LC, Schmidt T, Mote B. Health monitoring of group-housed pigs using depth-enabled multi-object tracking. Proc Int Conf Pattern Recognit, Work Vis Obs Anal Vertebr Insect Behav. 2016.

65. Kongsro J. Development of a computer vision system to monitor pig locomotion. Open J Anim Sci. 2013;3(3):254–60.

66. Weixing Z, Jin Z. Identification of abnormal gait of pigs based on video analysis. 2010 Third Int Symp Knowl Acquis Model. 2010;394–7.

67. Stavrakakis S, Li W, Guy JH, Morgan G, Ushaw G, Johnson GR, et al. Validity of the Microsoft Kinect sensor for assessment of normal walking patterns in pigs. Comput Electron Agric. 2015;117:1–7.

68. Zhu W, Zhu Y, Li X, Yuan D. The posture recognition of pigs based on Zernike moments and support vector machines. In: 2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). 2015. p. 480–4.

69. Zhu Q, Ren J, Barclay D, McCormack S, Thomson W. Automatic animal detection from Kinect sensed images for livestock monitoring and assessment. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing. IEEE; 2015. p. 1154–7.

70. Lao F, Brown-Brandl T, Stinn J. P, Liu K, Teng G, Xin H. Automatic recognition of lactating sow behaviors through depth image processing. Comput Electron Agric. 2016;125:56–62.

71. Zheng C, Zhu X, Yang X, Wang L, Tu S, Xue Y. Automatic recognition of lactating sow postures from depth images by deep learning detector. Comput Electron Agric. 2018;147:51–63.

72. Weixing Z, Zhilei W. Detection of porcine respiration based on machine vision. In: 3rd International Symposium on Knowledge Acquisition and Modeling. 2010. p. 398–401.

73. Šustr P, Špinka M, Cloutier S, Newberry RC. Computer-aided method for calculating animal configurations during social interactions from two-dimensional coordinates of color-marked body parts. Behav Res Methods, Instruments, Comput. 2001;33(3):364–70.

74. Nasirahmadi A, Edwards S, Richter U, Sturm B. Automatic detection of changes in pig group lying behaviour using image analysis. In: 2015 ASABE Annual International Meeting. 2015.

75. Nasirahmadi A, Edwards SA, Matheson SM, Sturm B. Using automated image analysis in pig behavioural research: Assessment of the in fl uence of enrichment substrate provision on lying behaviour. Appl Anim Behav Sci. 2017;196:30–5.

76. Nasirahmadi A, Hensel O, Edwards SA, Sturm B. Automatic detection of mounting behaviours among pigs using image analysis. Comput Electron Agric. 2016;124:295–302.

77. Kashiha M, Bahr C, Haredasht SA, Ott S, Moons CPH, Niewold TA, et al. The automatic monitoring of pigs water use by cameras. Comput Electron Agric. 2013;90:164–9.

78. Oczak M, Viazzi S, Ismayilova G, Sonoda LT, Roulston N, Fels M, et al. Classification of aggressive behaviour in pigs by activity index and multilayer feed forward neural network. Biosyst Eng. 2014;119:89–97.

79. Viazzi S, Ismayilova G, Oczak M, Sonoda LT, Fels M, Guarino M, et al. Image feature extraction for classification of aggressive interactions among pigs. Comput Electron Agric. 2014;104:57–62.

80. Chen C, Zhu W, Ma C, Guo Y, Huang W, Ruan C. Image motion feature extraction for recognition of aggressive behaviors among group-housed pigs. Comput Electron Agric. 2017;142:380–7.

81. Zuo S, Jin L, Chung Y, Park D. An index algorithm for tracking pigs in pigsty. In: Proceedings of the ICITMS, Hong Kong, China. 2014. p. 797–803.

82. Zelek JS, Bullock D. Real-time automated concurrent visual tracking of many animals and subsequent behavioural compilation. In: Proceedings Eighth IEEE International Conference on Computer Vision. 2001. p. 751.

83. Ahrendt P, Gregersen T, Karstoft H. Development of a real-time computer vision system for tracking loose-housed pigs. Comput Electron Agric. 2011;76(2):169–74.

84. Kashiha M, Bahr C, Ott S, Moons CPH, Niewold TA, Ödberg FO, et al. Automatic identification of marked pigs in a pen using image pattern recognition. Comput Electron Agric. 2013;93:111–20.

85. Navarro-Jover JM, Alcañiz-Raya M, Gómez V, Balasch S, Moreno JR, Grau-Colomer V, et al. An automatic colour-based computer vision algorithm. Spanish J Agric Res. 2009;7(3):535–49.

86. Huang W, Zhu W, Ma C, Guo Y, Chen C. Identification of group-housed pigs based on Gabor and Local Binary Pattern features. Biosyst Eng. 2018;166:90–100.

87. Zhu W, Guo Y, Jiao P, Ma C, Chen C. Recognition and drinking behaviour analysis of individual pigs based on machine vision. Livest Sci. 2017;205:129–36.

88. Yu S, Chen Z, Ou J, Zhou Y. Tracking algorithm based on multi-feature detection and target association of pigs on large-scale pig farms. J Inf Comput Sci. 2015;12(10):3837–44.

89. Leroy T, Vranken E, Struelens E, Sonck B, Berckmans D. Computer vision based recognition of behavior phenotypes of laying hens. In: 2005 ASAE Annual Meeting. 2005.

90. Leroy T, Vranken E, Van Brecht A, Struelens E, Sonck B, Berckmans D. A computer vision method for on-line behavioral quantification of individually caged poultry. Trans ASABE. 2006;49(3):795–802.

91. Cronin GM, Borg SS, Dunn MT. Using video image analysis to count hens in cages and reduce egg breakage on collection belts. Aust J Exp Agric. 2008;48(7):768–72.

92. Kashiha MA, Green AR, Sales TG, Bahr C, Berckmans D, Gates RS. Performance of an image analysis processing system for hen tracking in an environmental preference chamber. Poult Sci. 2014;93(10):2439–48. doi: 10.3382/ps.2014-04078 25071227

93. Zhang G, Jayas DS, White NDG. Separation of touching grain kernels in an image by ellipse fitting algorithm. Biosyst Eng. 2005;92(2):135–42.

94. Nakarmi AD, Tang L, Xin H. Automated tracking and behavior quantification of laying hens using 3D computer vision and radio frequency identification technologies. Trans ASABE. 2014;57(5):1455–72.

95. Wang C, Chen H, Zhang X, Meng C. Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine. J Anim Sci Biotechnol. 2016;7(1):60.

96. Sergeant D, Boyle R, Forbes M. Computer visual tracking of poultry. Comput Electron Agric. 1998;21:1–18.

97. Fujii T, Yokoi H, Tada T, Suzuki K, Tsukamoto K. Poultry tracking system with camera using particle filters. In: IEEE International Conference on Robotics and Biomimetics. IEEE; 2008. p. 1888–93.

98. Kristensen HH, Cornou C. Automatic detection of deviations in activity levels in groups of broiler chickens—A pilot study. Biosyst Eng. 2011;109(4):369–76.

99. Kashiha M, Pluk A, Bahr C, Vranken E, Berckmans D. Development of an early warning system for a broiler house using computer vision. Biosyst Eng. 2013;116(1):36–45.

100. Peña Fernández A, Norton T, Exadaktylos V, Vranken E, Berckmans D. Analysis of behavioural patterns in broilers using camera-based technology. In: International Conference in Agricultural Engineering CIGR AgEng 2016. 2016.

101. Aydin A, Cangar O, Eren Ozcan S, Bahr C, Berckmans D. Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores. Comput Electron Agric. 2010;73(2):194–9.

102. Aydin A, Pluk A, Leroy T, Berckmans D, Bahr C. Automatic identification of activity and spatial use of broiler chickens with different gait scores. Trans ASABE. 2013;56(3):1123–32.

103. Aydin A. Using 3D vision camera system to automatically assess the level of inactivity in broiler chickens. Comput Electron Agric. 2017;135:4–10.

104. Kestin SC, Knowles TG, Tinch AE, Gregory NG. Prevalence of leg weakness in broiler chickens and its relationship with genotype. Vet Rec. 1992;131(9):190–4. doi: 10.1136/vr.131.9.190 1441174

105. Youssef A, Exadaktylos V, Berckmans DA. Towards real-time control of chicken activity in a ventilated chamber. Biosyst Eng. 2015;135:31–43.

106. Dawkins MS, Lee H, Waitt CD, Roberts SJ. Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait. Appl Anim Behav Sci. 2009;119:203–9.

107. Dawkins MS, Cain R, Roberts SJ. Optical flow, flock behaviour and chicken welfare. Anim Behav. 2012;84(1):219–23.

108. Colles FM, Cain RJ, Nickson T, Smith AL, Roberts SJ, Maiden MCJ, et al. Monitoring chicken flock behaviour provides early warning of infection by human pathogen Campylobacter. Proc R Soc B Biol Sci. 2016;283(1822):1–6.

109. Pereira DF, Miyamoto BCB, Maia GDN, Tatiana Sales G, Magalhães MM, Gates RS. Machine vision to identify broiler breeder behavior. Comput Electron Agric. 2013;99:194–9.

110. Zhuang X, Bi M, Guo J, Wu S, Zhang T. Development of an early warning algorithm to detect sick broilers. Comput Electron Agric. 2018;144:102–13.

111. Cangar Ö, Leroy T, Guarino M, Vranken E, Fallon R, Lenehan J, et al. Model-based calving monitor using real time image analysis. 2007. 291–298 p.

112. Cangar Ö, Leroy T, Guarino M, Vranken E, Fallon R, Lenehan J, et al. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online. Comput Electron Agric. 2008;64(1):53–60.

113. Van Hertem T, Alchanatis V, Antler A, Maltz E, Halachmi I, Schlageter-Tello A, et al. Comparison of segmentation algorithms for cow contour extraction from natural barn background in side view images. Comput Electron Agric. 2013;91:65–74.

114. Tsai D-M, Huang C-Y. A motion and image analysis method for automatic detection of estrus and mating behavior in cattle. Comput Electron Agric. 2014;104:25–31.

115. Ahn S-J, Ko D-M, Choi K-S. Cow behavior recognition using motion history image feature. International Conference Image Analysis and Recognition. 2017. p. 626–33.

116. Song X, Leroy T, Vranken E, Maertens W, Sonck B, Berckmans D. Automatic detection of lameness in dairy cattle—Vision-based trackway analysis in cow’s locomotion. Comput Electron Agric. 2008;64:39–44.

117. Poursaberi A, Bahr C, Pluk A, Van Nuffel A, Berckmans D. Real-time automatic lameness detection based on back posture extraction in dairy cattle: Shape analysis of cow with image processing techniques. Comput Electron Agric. 2010;74(1):110–9.

118. Pluk A, Bahr C, Poursaberi A, Maertens W, Van Nuffel A, Berckmans D. Automatic measurement of touch and release angles of the fetlock joint for lameness detection in dairy cattle using vision techniques. J Dairy Sci. 2012;95(4):1738–48. doi: 10.3168/jds.2011-4547 22459822

119. Viazzi S, Bahr C, Van Hertem T, Romanini CEB, Pluk A, Halachmi I. Analysis of individual classification of lameness using automatic measurement of back posture in dairy cattle. J Dairy Sci. 2013;96(1):257–66. doi: 10.3168/jds.2012-5806 23164234

120. Viazzi S, Bahr C, Van Hertem T, Schlageter-Tello A, Romanini CEB, Halachmi I, et al. Comparison of a three-dimensional and two-dimensional camera system for automated measurement of back posture in dairy cows. Comput Electron Agric. 2014;100:139–47.

121. Van Hertem T, Bahr C, Viazzi S, Steensels M, Romanini CEB, Lokhorst C, et al. On farm implementation of a fully automatic computer vision system for monitoring gait related measures in dairy cows. In: 2014 ASABE and CSBE/SCGAB Annual International Meeting Montreal, Quebec Canada. 2014.

122. Van Hertem T, Viazzi S, Steensels M, Maltz E, Antler A, Alchanatis V, et al. Automatic lameness detection based on consecutive 3D-video recordings. Biosyst Eng. 2014;119:108–16.

123. Hansen MF, Smith ML, Smith LN, Abdul Jabbar K, Forbes D. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput Ind. 2018;98:14–22. doi: 10.1016/j.compind.2018.02.011 29997403

124. Souza SRL, Nääs IA, Moura DJ. Computational vision use for evaluation of confined dairy cows behavior. Livest Environ VIII, 31 August–4 Sept 2008, Iguassu Falls, Brazil. 2009;136.

125. Porto SMC, Arcidiacono C, Anguzza U, Cascone G. A computer vision-based system for the automatic detection of lying behaviour of dairy cows in free-stall barns. Biosyst Eng. 2013;115(2):184–94.

126. Porto SMC, Arcidiacono C, Anguzza U, Cascone G. The automatic detection of dairy cow feeding and standing behaviours in free-stall barns by a computer vision-based system. Biosyst Eng. 2015;133:46–55.

127. Zin TT, Kobayashi I, Tin P, Hama H. A general video surveillance framework for animal behavior analysis. 2016 Third Int Conf Comput Meas Control Sens Netw. 2016;130–3.

128. Norton T, Berckmans D. Developing precision livestock farming tools for precision dairy farming. Anim Front. 2017;7(1):18–23.

129. Fontana I, Tullo E, Gottardo D, Bahr C, Viazzi S, Sloth KH, et al. Validation of a commercial system for the continuous and automated monitoring of dairy cows activity. In: European Conference on Precision Livestock Farming. 2015. p. 93–102.

130. Tullo E, Fontana I, Gottardo D, Sloth KH, Guarino M. Technical note: Validation of a commercial system for the continuous and automated monitoring of dairy cow activity. J Dairy Sci. 2016;99(9):7489–94. doi: 10.3168/jds.2016-11014 27344390

131. Weintraub PG. The Importance of Publishing Negative Results. J insect Sci. 2016;16(1):1–2.

132. Jukan A, Masip-Bruin X, Amla N. Smart computing and sensing technologies for animal welfare: A systematic review. ACM Comput Surv. 2017;50(1).

133. Hansen MF, Smith ML, Smith LN, Salter MG, Baxter EM, Farish M, et al. Towards on-farm pig face recognition using convolutional neural networks. Comput Ind. 2018;98:145–52.

134. Neethirajan S. Recent advances in wearable sensors for animal health management. Sens Bio-Sensing Res. 2017;12:15–29.

135. Chung Y, Oh S, Lee J, Park D, Chang H-H, Kim S. Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems. Sensors. 2013;13(10):12929–42. doi: 10.3390/s131012929 24072029

136. Berkmans D. Precision livestock farming technologies for welfare management in intensive livestock systems. Rev Sci Tech. 2014;33(1):189–96. doi: 10.20506/rst.33.1.2273 25000791

137. Rizwan M, Carroll BT, Anderson D V., Daley W, Harbert S, Britton DF, et al. Identifying rale sounds in chickens using audio signals for early disease detection in poultry. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE; 2016. p. 55–9.

138. Carpentier L, Berckmans D, Youssef A, Berckmans D, Van Waterschoot T, Johnston D, et al. Automatic cough detection for bovine respiratory disease in a calf house. Biosyst Eng. 2018;173:45–56.

139. Berkmans D, Hemeryck M, Berckmans D, Vranken E, Van Waterschoot T. Animal Sound … talks! Real-time sound analysis for health monitoring in livestock. Int Symp Anim Environ Welf. 2015;(October 2015):215–22.

140. Abeni F, Petrera F, Galli A. A survey of Italian dairy farmers’ propensity for precision livestock farming tools. Animals. 2019;9(5):202.

141. Van Hertem T, Rooijakkers L, Berckmans D, Peña Fernández A, Norton T, Berckmans D, et al. Appropriate data visualisation is key to Precision Livestock Farming acceptance. Comput Electron Agric [Internet]. 2017;138:1–10. Available from:

142. Richens IF, Houdmont J, Wapenaar W, Shortall O, Kaler J, O’Connor H, et al. Application of multiple behaviour change models to identify determinants of farmers’ biosecurity attitudes and behaviours. Prev Vet Med [Internet]. 2018;155:61–74. Available from: 29786526

143. Bahlo C, Dahlhaus P, Thompson H, Trotter M. The role of interoperable data standards in precision livestock farming in extensive livestock systems: A review. Comput Electron Agric [Internet]. 2019;156:459–66. Available from:

144. Rowe E, Dawkins MS, Gebhardt-Henrich SG. A systematic review of precision livestock farming in the poultry sector: Is technology focussed on improving bird welfare? Animals. 2019;9(9):614.

145. Norton T, Chen C, Larsen ML V, Berckmans D. Review: Precision livestock farming: building ‘digital representations’ to bring the animals closer to the farmer. Animal. 2019;1–9.

146. Li N, Ren Z, Li D, Zeng L. Review: Automated techniques for monitoring the behaviour and welfare of broilers and laying hens: towards the goal of precision livestock farming. Animal. 2019;1–9.

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2019 Číslo 12
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