Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T13:52:50.700Z Has data issue: false hasContentIssue false
  • English
  • Français

The effectiveness of a visual image analysis (VIA) system for monitoring the performance of growing/finishing pigs

Published online by Cambridge University Press:  18 August 2016

R. P. White ,
C. P. Schofield ,
D. M. Green ,
D. J. Parsons  and
C. T. Whittemore
R. P. White*
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS, UK
C. P. Schofield
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS, UK
D. M. Green
Affiliation:
University of Edinburgh School of Geosciences, Agriculture Building, West Mains Road, Edinburgh EH9 3JG, UK
D. J. Parsons
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Silsoe, Bedford MK45 4HS, UK
C. T. Whittemore
Affiliation:
University of Edinburgh School of Geosciences, Agriculture Building, West Mains Road, Edinburgh EH9 3JG, UK
*
† E-mail: Rodger.White@bbsrc.ac.uk
Get access

Abstract

A visual image analysis (VIA) system provided continuous, automatic collection of size and shape data for a total of 116 pigs slaughtered serially from 25 to 115 kg live weight. Males and females of three types of pigs (‘Meishan’ type, ‘Pietrain’ type, and ‘Landrace’ type) were selected to provide variation in both composition and conformation (the three types being, respectively, ‘fat’, ‘blocky’, and ‘lean’). Results below are presented in this order. Regression analysis was used to relate VIA size to platform weigher (FIRE) measurements of live weight. Residual maximum likelihood (REML) analysis showed that at the observed growth rate, a change in pig state could be detected by VIA after 8, 9, and 10 days respectively for the three types, and by the platform weigher system after 12, 4, and 13 days (in both cases with a confidence of 95%). Artificial neural network and canonical variates analysis were used to test the ability of VIA to distinguish between pig types and sexes. With cross validation, the canonical variates analysis correctly classified the three types in 72, 83, and 64% of observations, and the neural network in 81, 81, and 64% of observations. The VIA system is considered to be a valuable monitoring system which may play a rôle in the construction of integrated management systems (IMS).

Keywords

canonical analysisgrowthintegrated management systemspigsvisual image analysis
Type
Non-ruminant nutrition, behaviour and production
Information
Animal Science , Volume 78 , Issue 3 , June 2004 , pp. 409 - 418
Copyright
Copyright © British Society of Animal Science 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Chatfield, C. 1996. The analysis of time series: an introduction. Chapman and Hall, London.Google Scholar
Doeschl, A. B., Whittemore, C. T., Green, D. M., Fisher, A. V. and Schofield, C. P. 2003. Use of visual image analysis for the description of pig growth in size and shape. Proceedings of the British Society of Animal Science, 2003, p. 21 (abstr.).CrossRefGoogle Scholar
Fisher, A. V., Green, D. M., Whittemore, C. T., Wood, J. D. and Schofield, C. P. 2003. Growth of carcass components and its relations with conformation in pigs of three types. Meat Science 65: 639–650.CrossRefGoogle ScholarPubMed
Genstat 5 Committee. 1993. Genstat 5 release 3 reference manual. Oxford University Press, Oxford.Google Scholar
Green, D. M., Brotherstone, S., Schofield, C. P. and Whittemore, C. T. 2003. Food intake and live growth performance of pigs measured automatically and continuously from 25 to 115 kg live weight. Journal of the Science of Food and Agriculture 83: 1150–1155.CrossRefGoogle Scholar
Marchant, J. A., Schofield, C. P. and White, R. P. 1999. Pig growth and conformation monitoring using image analysis. Animal Science 68: 141–150.Google Scholar
Mardia, K. V., Kent, J. T. and Bibby, J. M. 1979. Multivariate analysis. Academic Press, London.Google Scholar
Schofield, C. P., Marchant, J. A., White, R. P., Brandl, N. and Wilson, M. 1999. Monitoring pig growth using a prototype imaging system. Journal of Agricultural Engineering Research 72: 205–210.Google Scholar
Verbyla, A. P. and Cullis, B. R. 1992. The analysis of multistratum and spatially correlated repeated measures data. Biometrics 48: 1015–1032.CrossRefGoogle Scholar
White, R. P., Parsons, D. J., Schofield, C. P., Green, D. M. and Whittemore, C. T. 2003. Use of visual image analysis for the management of pig growth in size and shape. Proceedings of the British Society of Animal Science, 2003, p. 101 (abstr.).CrossRefGoogle Scholar
Whittemore, C. T., Green, D. M. and Schofield, C. P. 2001. Nutrition management of growing pigs. In Integrated management systems for livestock (ed. Wathes, C. M. Frost|F., A. R. Gordon, and Wood, J. D.) British Society of Animal Science occasional publication no. 28, pp. 89–95.Google Scholar
Whittemore, C. T., Green, D. M., Wood, J. D., Fisher, A. V. and Schofield, C. P. 2003. Physical and chemical composition of the carcass of three different types of pigs grown from 25 to 115 kg live weight. Animal Science 77: 235–245.CrossRefGoogle Scholar
Whittemore, C. T. and Schofield, C. P. 2000. A case for size and shape scaling for understanding nutrient use in breeding sows and growing pigs. Livestock Production Science 65: 203–208.CrossRefGoogle Scholar