Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-10T13:27:20.776Z Has data issue: false hasContentIssue false

Automatic classification of measures of lying to assess the lameness of broilers

Published online by Cambridge University Press:  03 January 2023

A Aydin*
Affiliation:
Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Canakkale Onsekiz Mart University, 17020 Canakkale, Turkey
C Bahr
Affiliation:
Division Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
D Berckmans
Affiliation:
Division Measure, Model & Manage Bioresponses, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, B-3001 Heverlee, Belgium
*
* Contact for correspondence and requests for reprints: araydin@comu.edu.tr
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Leg disorders are a major cause of poor welfare in broilers. Previous studies have shown that at slaughter age at least 90% of chickens experienced some degree of gait problems and approximately 30% were seriously lame. In this study, a new and non-invasive technique was developed to automatically assess the lameness of the birds. For this purpose, video surveillance images of broilers with five different pre-defined gait scores were recorded as they walked along a test corridor. Afterwards, the image-processing algorithm was applied to detect the number of lying events (NOL) and latency to lie down (LTL) of broiler chickens. Then, the results of the algorithm were compared with visually assessed manual labelling data (reference method) and the relation between these measures and lameness was investigated. Eighty-three percent of NOL were correctly classified by the automatic monitoring system when compared to manual labelling using a data set collected from 250 broiler chickens. The results also showed a positive significant correlation between NOL and gait score and a significant negative correlation between LTL and gait-score level of broilers. Since strong correlations were found, on the one hand, between two measures and gait-score level of broiler chickens and, on the other, between the results of algorithm and manual labelling, the results suggest this automatic monitoring system may have the potential to be used as a tool for assessing lameness of broiler chickens.

Type
Research Article
Copyright
© 2015 Universities Federation for Animal Welfare

References

Aydin, A, Cangar, O, Eren Ozcan, S, Bahr, C and Berckmans, D 2010 Application of a fully automatic analysis tool to assess the activity of broiler chickens with different gait scores. Computers and Electronics in Agriculture 73: 194199. http://dx.doi.org/10.1016/j.compag.2010.05.004CrossRefGoogle Scholar
Bauer, M, Heissenhuber, K, Damme, K and Köbler, M 1996 Welche roilerherkunft eignet sich? DGS Magazin 44: 2226. [Title translation: What is the origin of the broiler?]Google Scholar
Berg, C and Sanotra, GS 2003 Can a modified latency-to-lie test be used to validate gait-scoring results in commercial broiler flocks? Animal Welfare 12: 655659Google Scholar
Bessei, W 2006 Welfare of broilers: a review. Worlds Poultry Science Journal 62: 455466. http://dx.doi.org/10.1079/WPS2005108CrossRefGoogle Scholar
Birchfield, S 1998 Elliptical head tracking using intensity gradients and color histograms. Proceedings of the IEEE conference on comput-er vision and pattern recognition (CVPR) pp 232–237. 23-25 June 1998, Santa Barbara, CA, USAGoogle Scholar
Bradshaw, RH, Kirkden, RD and Broom, DM 2002 A review of the aetiology and pathology of leg weakness in broilers in relation to welfare. Avian and Poultry Biology Reviews 13: 45103. http://dx.doi.org/10.3184/147020602783698421CrossRefGoogle Scholar
Cangar, O, Leroy, T, Guarino, M, Vranken, E, Fallon, R, Lenehan, J, Meed, J and Berckmans, D 2008 Automatic real-time monitoring of locomotion and posture behaviour of preg-nant cows prior to calving using online image analysis. Computers and Electronics in Agriculture 64: 5360. http://dx.doi.org/10.1016/j.compag.2008.05.014CrossRefGoogle Scholar
Caplen, G, Hothersall, B, Murrell, JC, Nicol, CJ and Waterman Pearson, AE 2012 Kinematic analysis quantifies gait abnormalities associated with lameness in broiler chickens and identifies evolutionary gait differences. PLoS One 7(7): e40800. http://dx.doi.org/10.1371/journal.pone.0040800CrossRefGoogle Scholar
Cook, ME 2000 Skeletal deformities and their causes: Introduction. Poultry Science 79(7): 982-984. http://dx.doi.org/10.1093/ps/79.7.982CrossRefGoogle Scholar
Corr, SA, Gentle, MJ, McCorquodale, CC and Bennett, D 2003 The effect of morphology on walking ability in the modern broiler: a gait analysis study. Animal Welfare 12(2): 159171Google Scholar
Dawkins, MS, Cain, R, Merelie, K and Roberts, SJ 2013 In search of the behavioural correlates of optical flow patterns in the automated assessment of broiler chicken welfare. Applied Animal Behaviour Science 145(1): 4450. http://dx.doi.org/10.1016/j.applan-im.2013.02.001CrossRefGoogle Scholar
Dawkins, MS, Cain, R and Roberts, SJ 2012 Optical flow, flock behaviour and chicken welfare. Animal Behaviour 84: 219223. http://dx.doi.org/10.1016/j.anbehav.2012.04.036CrossRefGoogle Scholar
Dawkins, MS, Lee, HJ, Waitt, CD and Roberts, SJ 2009 Optical flow patterns in broiler chicken flocks as automated measures of behaviour and gait. Applied Animal Behaviour Science 119(3-4): 203209. http://dx.doi.org/10.1016/j.applan-im.2009.04.009CrossRefGoogle Scholar
Kestin, SC, Gordon, S, Su, G and Sørensen, P 2001 Relationships in broiler chickens between lameness, live weight, growth rate and age. The Veterinary Record 148(7):195-197. http://dx.doi.org/10.1136/vr.148.7.195CrossRefGoogle Scholar
Kestin, SC, Knowles, TG, Tinch, AE and Gregory, NG 1992 Prevalence of leg weakness in broiler chickens and its relationship with genotype. The Veterinary Record 131: 190194. http://dx.doi.org/10.1136/vr.131.9.190CrossRefGoogle ScholarPubMed
Knowles, TG, Kestin, SC, Haslam, SM, Brown, SN, Green, LE, Butterworth, A, Pope, SJ, Pfeiffer, D and Nicol, CJ 2008 Leg disorders in broiler chickens: prevalence, risk factors and pre-vention. PLoS One 3: e1545.CrossRefGoogle Scholar
Leroy, T, Vranken, E, Van Brecht, A, Struelens, E, Sonck, B and Berckmans, D 2006 A computer vision method for online behavioral quantification of individually caged poultry. Transactions of the ASABE 49(3): 795802. http://dx.doi.org/10.13031/2013.20462CrossRefGoogle Scholar
McGeown, D, Danbury, TC, Waterman-Pearson, AE and Kestin, SC 1999 Effect of carprofen on lameness in broiler chick-ens. The Veterinary Record 144(24): 668671. http://dx.doi.org/10.1136/vr.144.24.668CrossRefGoogle Scholar
McKay, JC, Barton, NF, Koerhuis, ANM and McAdam, J 2000 The challenge of genetic change in the broiler chicken. The Challenge of Genetic Change in Animal Production pp 1–7. 26-27 October 1999, Edinburgh, UKCrossRefGoogle Scholar
Naas, IA, Almeida Paz, ICL and Baracho, MS 2010 Assessing locomotion deficiency in broiler chicken. Scientia Agricola 67: 129135CrossRefGoogle Scholar
Rushen, J, Chapinal, N and de Passillé, AM 2012 Automated monitoring of behavioural-based animal welfare indicators. Animal Welfare 21: 339350. http://dx.doi.org/10.7120/09627286.21.3.339CrossRefGoogle Scholar
Swayne, DE and Halvorson, DA 2003 Influenza. In: Calnek, BW, Barnes, HJ, Beard, CW, McDougald, LR and Saif, YM (eds) Diseases of Poultry, Eleventh Edition pp 147. Iowa State University Press: Ames, IA, USAGoogle Scholar
Thorp, BH and Duff, SRI 1988 Effect of exercise on the vascu-lar pattern in the bone extremities of broiler fowl. Research Veterinary Science 45: 7277CrossRefGoogle Scholar
Weeks, CA, Danbury, TD, Davies, HC, Hunt, P and Kestin, SC 2000 The behaviour of broiler chickens and its modification by lameness. Applied Animal Behaviour Science 67: 111125. http://dx.doi.org/10.1016/S0168-1591(99)00102-1CrossRefGoogle ScholarPubMed
Weeks, CA, Knowles, TG, Gordon, RG, Kerr, AE, Peyton, ST and Tilbrook, NT 2002 New method for objectively assessing lameness in broiler chickens. Veterinary Record 151: 762764Google ScholarPubMed
Vestergaard, KS and Sanotra, GS 1999 Relationships between leg disorders and changes in the behaviour of broiler chickens. Veterinary Record 144: 205209. http://dx.doi.org/10.1136/vr.144.8.205CrossRefGoogle ScholarPubMed
Young, PC 1984 Recursive Estimation and Time-Series Analysis. Springer-Verlag: New York, USA. http://dx.doi.org/10.1007/978-3-642-82336-7CrossRefGoogle Scholar