Prediction of welfare outcomes for broiler chickens using Bayesian regression on continuous optical flow data

Journal of the Royal Society Interface

Volumn 9, Issue 77, 2012, Pages 3436-3443

  Roberts, Stephen J ^a   Cain, Russell ^b   Dawkins, Marian Stamp ^b  

^a Department of Engineering Science ^*  (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Animal welfare; Bayesian multivariate modelling; Optical flow; Variational Bayes inference

Indexed keywords

AGRICULTURE; ANIMALS; FORECASTING;

ANIMAL WELFARE; BAYESIAN MODELLING; BAYESIAN REGRESSION; BROILER CHICKENS; CLINICAL SYMPTOMS; DESCRIPTORS; KEY VARIABLES; LABOUR-INTENSIVE; MOTION DATA; MULTIVARIATE FORECASTING; PREDICTIVE POWER; VARIATIONAL BAYES;

OPTICAL FLOWS;

ACCURACY; ANIMAL WELFARE; ARTICLE; BAYES THEOREM; CHICKEN; DATA ANALYSIS; HERD; MATHEMATICAL MODEL; MOTION ANALYSIS SYSTEM; MULTIVARIATE ANALYSIS; NONHUMAN; OPTICS; PREDICTIVE VALUE; VIDEORECORDING;

EID: 84868539192     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2012.0594     Document Type: Article

References (17)

1. Koolhaas, J. M. 2008 Coping style and immunity in animals: making sense of individual variation. Brain Behav. Immun. 22, 662-667. (doi:10.1016/j.bbi. 2007.11.006)
2. Urton, G., von Keyserlingk, M. A. G. & Weary, D. M. 2005 Feeding behavior identifies dairy cows at risk for metritis. J. Dairy Sci. 88, 2843-2849. (doi:10.3168/jds. S0022-0302(05)72965-9)
3. Littin, K., Acevedo, A., Browne, W., Edgar, J., Mendl, M., Owen, D.,Sherwin, C.,Wurbel, H.&Nicol, C. 2008Towards humane end points: behavioural changes precede clinical signs of disease in a Huntingtons disease model. Proc. R. Soc. B 275, 1865-1874. (doi:10.1098/rspb.2008.0388)
4. Beattie, V. E., Breuer, K., O'Connell, N. E., Sneddon, I. A., Mercer, J. T., Rance, K. A., Sutcliffe, M. E. M. & Edwards, S. A. 2005 Factors identifying pigs predisposed to tail biting. Anim. Sci. 80, 307-312. (doi:10.1079/ASC40040307)
5. Toscano, M. J., Sait, L., Jorgensen, F., Nicol, C. J., Powers, C., Smith, A. L., Bailey, M. & Humphrey, T. J. 2010 Sub-clinical infection with salmonella in chickens differentially affects behaviour and welfare in three inbred strains. Br. Poult. Sci. 51, 703-713. (doi:10.1080/00071668.2010.528748)
6. Lee, H.-J., Roberts, S. J., Drake, K. A. & Dawkins, M. S. 2011 Prediction of feather damage in laying hens using optical flow and Markov models. J. R. Soc. Interface 8, 489-499. (doi:10.1098/rsif.2010.0268)
7. Dawkins, M. S., Lee, H.-J., Waitt, C. D. & Roberts, S. J. 2009 Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait. Appl. Anim. Behav. Sci. 119, 203-209. (doi:10.1016/j. applanim.2009.04.009)
8. Kestin, S. C., Knowles, T. G., Tinch, A. E. & Gregory, N. 1992 Prevalance of leg weakness in broiler chickens and its relationship with genotype. Vet. Rec. 131, 190-194. (doi:10.1136/vr.131.9.190)
9. Beauchemin, S. & Barron, J. 1995 The computation of optical flow. ACM Comput. Surv. 27, 433-467. (doi:10.1145/212094.212141)
10. Fleet, D. J. & Weiss, Y. 2005 Optical flow estimation. In Handbook of mathematical models for computer vision (eds N. Paragios, Y. Chen & O. Faugeras), pp. 239-258. New York, NY: Springer.
11. Haslam, S. M., Brown, S. N., Wilkins, L. J., Kestin, S. C., Warriss, P. D. & Nicol, C. J. 2006 Preliminary study to examine the possibility of using foot burn or hockburn to assess aspects of housing conditions in broiler chickens. Br. Poult. Sci. 47, 13-18. (doi:10.1080/00071660500475046)
12. Kjaer, J. B., Su, G., Nielsen, B. L. & Särenson, P. 2006 Foot pad dermatitis and hock burn in broiler chickens and degree of inheritance. Poult. Sci. 85, 1342-1348.
13. Hepworth, P. J., Nefedov, A. V., Muchnik, I. B. & Morgan, K. L. 2010 Early warning indicators for hock burn in broiler flocks. Avian Pathol. 39, 405-409. (doi:10.1080/03079457.2010.510500)
14. Bishop, C. M. 2006 Pattern recognition and machine learning. New York, NY: Springer.
15. Fox, C. W. & Roberts, S. J. 2012 A tutorial on variational Bayesian inference. Artif. Intell. Rev. 38, 1-11. (doi:10.1007/s10462-011-9236-8)
16. Neal, R. 1998 Assessing relevance determination methods using DELVE. In Neural networks and machine learning (ed. C. Bishop), pp. 97-129. Berlin, Germany: Springer-Verlag.
17. Penny, W. & Roberts, S. 2002 Bayesian multivariate autoregressive models with structured priors. IEE Proc. Vision, Image Signal Proc. 149, 33-41. (doi:10.1049/ip-vis:20020149)