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Shape measurements of live pigs using 3-D image capture

Published online by Cambridge University Press:  09 March 2007

N. J. B. McFarlane ,
J. Wu ,
R. D. Tillett ,
C. P. Schofield ,
J. P. Siebert  and
X. Ju
N. J. B. McFarlane*
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Bedford MK45 4HS, UK
J. Wu
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Bedford MK45 4HS, UK
R. D. Tillett
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Bedford MK45 4HS, UK
C. P. Schofield
Affiliation:
BBSRC Silsoe Research Institute, Wrest Park, Bedford MK45 4HS, UK
J. P. Siebert
Affiliation:
Department of Computer Science, Boyd Orr Building, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
X. Ju
Affiliation:
Department of Computer Science, Boyd Orr Building, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
*
†E-mail: [email protected]
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Abstract

A photogrammetric stereo imaging system was used to capture 3-D models of live pigs, and quantitative shape measurements were extracted from cross sections of the models. Stereo images were captured of 32 pigs, divided into high-lysine and low-lysine diet groups, and 3-D models were built from the images. Each pig was imaged once per week for 14 weeks. After slaughter, 10 of the pigs were dissected for muscle and fat measurements. A sequence of algorithms was applied to the 3-D models: differential geometry to reveal surface curvature features and detect the spine; manual landmark placement; fitting a curve to the spine; determining the vertical axis of the body; placing a slice plane across the abdomen close to the P2 position; extracting a cross section; and fitting a shape model to the cross section. Differential geometry revealed many qualitative features of the musculature. The spine was a line of minimum curvature along the back. The high-lysine pigs had higher height-to-width ratios and flatter backs than the low-lysine pigs. The dissected total muscle mass had a -0·66 correlation with the flatness-of-back shape parameter, and a 0·64 correlation with weight.

Keywords

imagerymodelspigsshape
Type
Research Article
Information
Animal Science , Volume 81 , Issue 3 , December 2005 , pp. 383 - 391
Copyright
Copyright © British Society of Animal Science 2005

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References

Brandl, N. and Jorgensen, E. 1996. Determination of live weight of pigs from dimensions measured using image analysis. Computers and Electronics in Agriculture 15: 57–72.CrossRefGoogle Scholar
Brown, A. J. and Wood, J. D. 1979. Pig carcass evaluation-measurement of composition using a standard butchery method. Memorandum no. 42, ARC Meat Research Institute, Langford, Bristol, UK.Google Scholar
Coffey, M. P., Mottram, T. B. and McFarlane, N. 2003. Feasibility study on the automatic recording of condition score in dairy cows. Proceedings of the British Society of Animal Science, 2003, p. 131.CrossRefGoogle Scholar
Doeschl, A. B., Green, D. M., Whittemore, C. T., Schofield, C. P., Fisher, A. V. and Knap, P. W. 2004. The relationship between the body shape of living pigs and their carcass morphology and composition. Animal Science 79: 73–83.CrossRefGoogle Scholar
Doeschl-Wilson, A. B., Green, D. M., Fisher, A. V., Carroll, S. M., Schofield, C. P. and Whittemore, C. T. 2005. The relationship between body dimensions of living pigs and their carcass composition. Meat Science 70: 229–240.Google Scholar
Doeschl-Wilson, A. B., Whittemore, C. T., Knap, P. W. and Schofield, C. P. 2004. Using visual image analysis to describe pig growth in terms of size and shape. Animal Science 79: 415–427.CrossRefGoogle Scholar
French, A. P., Frost, A., Pridmore, T. P. and Tillett, R. D. 2003. An image analysis system to guide a sensor placement robot onto a feeding pig. Irish machine vision and image processing conference, Portrush, Co. Antrim, Northern Ireland, 3–5 09 2003, pp. 185–192.Google Scholar
Frost, A. R., French, A. P., Tillett, R. D., Pridmore, T. P. and Welch, S. K. 2004. A vision guided robot for tracking a live, loosely constrained pig. Computers and Electronics in Agriculture 44: 93–106.CrossRefGoogle Scholar
Geers, R., Goedseels, V., Parduyns, G., Stuyft van der, E., Boschaerts, L., Dely, J. and Neirynck, W. 1991. Prediction of SKG-II grading of carcass lean content by body measurements and ultrasound in vivo. Revue de l'Agriculture 44: 237–241.Google Scholar
Horgan, G. W., Murphy, S. V. and Simm, G. 1995. Automatic assessment of sheep carcasses by image analysis. Animal Science 60: 197–202.CrossRefGoogle Scholar
Jain, R., Kasturi, R. and Schunck, B. G. 1995. Machine vision. McGraw Hill, Singapore.Google Scholar
Ju, X., Boyling, T., Siebert, J. P., McFarlane, N., Wu, J. and Tillett, R. 2004. Integration of range images in a multi-view stereo system. Proceedings of the 17th international conference on pattern recognition, ICPR 2004, Cambridge, 23–26 08 2004, vol. IV, pp. 280–283.Google Scholar
Marchant, J. A. and Schofield, C. P. 1993. Extending the snake image processing algorithm for outlining pigs in scenes. Computers and Electronics in Agriculture 8: 261–275.Google Scholar
Patrikalakis, N. M. and Maekawa, T. 2002. Shape interrogation for computer aided design and manufacturing. Springer, Berlin.Google Scholar
Schofield, C. P. 1990. Evaluation of image analysis as a means of estimating the weight of pigs. Journal of Agricultural Engineering Research 47: 287–296.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
Schofield, C. P., Tillett, R. D., McFarlane, N. J. B., Mottram, T. T. and Frost, A. R. 2005. Emerging technology for assessing the composition of livestock. Proceedings of the second European conference on precision livestock farming, 2ECPLF 2005, Uppsala, Sweden, 9–12 06 2005. In press.Google Scholar
Siebert, J. P. and Marshall, S. J. 2000. Human body 3D imaging by speckle texture projection photogrammetry. Sensor Review 20: 218–226.CrossRefGoogle Scholar
Siebert, J. P. and Urquhart, C. W. 1990. Active stereo-texture enhanced reconstruction. Electronics Letters 26: 427–430.CrossRefGoogle Scholar
Siebert, J. P. and Urquhart, C. W. 1994. C3D: a novel vision-based 3D data acquisition system. Proceedings of the Mona Lisa European workshop, combined real and synthetic image processing for broadcast and video production, Hamburg, Germany, 11 1994, pp. 170–180.Google Scholar
Stanford, K., Richmond, R. J., Jones, S. D. M., Robertson, W. M., Price, M. A. and Gordon, A. J. 1998. Video image analysis for on-line classification of lamb carcasses. Animal Science 67: 311–316.Google Scholar
Stuyft van der, E., Goedseels, V. and Groof de., M. 1990. Three dimensional computer vision for in vivo measurements on pigs. Proceedings of the international conference on agricultural engineering, AgEng 90, Berlin, Germany, 24–26 10 1990, pp. 291–292.Google Scholar
Stuyft van der, E., Schofield, C. P., Randall, J. M., Wambacq, P. and Goedseels, V. 1991a. Development and application of computer vision systems for use in livestock production. Computers and Electronics in Agriculture 6: 243–265.CrossRefGoogle Scholar
Stuyft van der, E., Bael van, J., Goedseels, V. and Bosschaerts, L. 1991b. Design of a procedure yielding a standard posture in live pigs for computer vision-based exterior shape measurement. Proceedings of the first international seminar for the agricultural and bio-industries, Montpellier, France, 3–6 09, pp. 99–101.Google Scholar
Tillett, R. D., Frost, A. R. and Welch, S. K. 2002. Predicting sensor placement targets on pigs using image analysis. Biosystems Engineering 81: 453–463.Google Scholar
Tillett, R. D., McFarlane, N. J. B., Wu, J., Schofield, C. P., Ju, X. and Siebert, J. P. 2004a. Extracting morphological data from 3D images of pigs. Proceedings of the international conference on agricultural engineering, AgEng 2004, Leuven, Belgium, 12–16 09 2004, Part 1, pp. 492–493.Google Scholar
Tillett, R. D., McFarlane, N. J. B., Wu, J., Schofield, C. P., Ju, X. and Siebert, J. P. 2004b. A system for 3D imaging of pig shape for conformation assessment. Proceedings of the British Society of Animal Science, 2004, p. 20.Google Scholar
Trevelyan, J. P. 1992. Robots for shearing sheep: shear magic. Oxford University Press.Google Scholar
Whittemore, C. T. 1998. The science and practice of pig production, second edition. Blackwell Science, Oxford.Google Scholar
Wilson, R. J. 1985. Introduction to graph theory, third edition. Longman, Essex.Google Scholar
Wu, J., Tillett, R., McFarlane, N., Ju, X., Siebert, P. and Schofield, P. 2004. Extracting the three-dimensional shape of live pigs using stereo photogrammetry. Computers and Electronics in Agriculture 44: 203–212.CrossRefGoogle Scholar