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Using artificial intelligence to design and implement a morphological assessment system in beef cattle

Published online by Cambridge University Press:  18 August 2016

F. Goyache ,
J. J. del Coz ,
J. R. Quevedo ,
S. López ,
J. Alonso ,
J. Ranilla ,
O. Luaces ,
I. Alvarez  and
A. Bahamonde
F. Goyache*
Affiliation:
SERIDA-CENSYRA-Somió, C/ Camino de los Claveles 604, E-33203 Gijón (Asturias), Spain
J. J. del Coz
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
J. R. Quevedo
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
S. López
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
J. Alonso
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
J. Ranilla
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
O. Luaces
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
I. Alvarez
Affiliation:
SERIDA-CENSYRA-Somió, C/ Camino de los Claveles 604, E-33203 Gijón (Asturias), Spain
A. Bahamonde
Affiliation:
Centro de Inteligencia Artificia, Universidad de Oviedo in Gijón, Campus de Viesques. E-33271 Gijón (Asturias), Spain
*
† E-mail:[email protected]
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Abstract

In this paper a methodology is developed to improve the design and implementation of a linear morphological system in beef cattle using artificial intelligence. The proposed process involves an iterative mechanism where type traits are successively defined and computationally represented using knowledge engineering methodologies, scored by a set of trained human experts and finally, analysed by means of four reputed machine learning algorithms. The results thus achieved serve as feed back to the next iteration in order to improve the accuracy and efficacy of the proposed assessment system. A sample of 260 conformation records of the Asturiana de los Valles beef cattle breed is shown to illustrate the methodology. Three sources of inconsistency were detected: (a) the existence of different interpretations of the trait’s definition, increasing the subjectivity of the assessment; (b) the narrow range of variation of some of the anatomical traits assessed; (c) the inclusion of some complex traits in the assessment system. In this sense, the reopening of the evaluated Asturiana de los Valles assessment system is recommended. In spite of the difficulty of collecting data from live animals, further implications of the artificial intelligence systems on morphological assessment are pointed out.

Keywords

artificial intelligencebeef cattlelinear typemachine learning.
Type
Breeding and genetics
Information
Animal Science , Volume 73 , Issue 1 , August 2001 , pp. 49 - 60
Copyright
Copyright © British Society of Animal Science 2001

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References

Associazione Nazionale Allevatori Bovini Italiani Carne. 1997. La razza Chianina. Tipolitografía Grifo, Perugia.Google Scholar
Bahamonde, A., de la Cal, E. A., Ranilla, J. and Alonso, J. 1997. Self-organizing symbolic learned rules. Lecture Notes in Computer Science, LNCS no. 1240, pp. 536–545. SpringerVerlag, Berlin.Google Scholar
Berg, R. and Butterfield, R. 1979. Nuevos conceptos sobre desarrollo de ganado vacuno. Editorial Acribia, Zaragoza, Spain.Google Scholar
Brotherstone, S. 1994. Genetic and phenotypic correlation between linear type traits and production traits in Holstein-Friesian dairy cattle. Animal Production 59: 183–187.Google Scholar
Brotherstone, S. and Hill, W. G. 1991. Dairy herd life in relation to linear type traits and production. I. Phenotypic and genetic analyses in pedigree type classified herds. Animal Production 53: 279–287.Google Scholar
Cover, T. M. and Hart, P. E. 1967. Nearest neighbour pattern classification. IEEE Transactions on Information Theory 13: 21–27.Google Scholar
de la Fuente, L. F., Fernández, G., and San Primitivo, F. 1996. A linear evaluation system for udder traits of dairy ewes. Livestock Production Science 45: 171–178.Google Scholar
del Coz, J. J. and Bahamonde, A. 1999. Mapas autoorganizados con atributos discretos. Proceedings of the VIII Conferencia de la Asociación Española para la Inteligencia Artificial, Murcia, Spain, vol. I, pp. 117–124.Google Scholar
del Coz, J. J., Luaces, O., Quevedo, J. R., Alonso, J. and Bahamonde, A. 1999. Self-organizing cases to find paradigms. Lecture Notes in Computer Sciences, LNCS no. 1606, pp. 527-536, Springer-Verlag, Berlín.Google Scholar
Fernández, G., Alvarez, P., San Primitivo, F. and de la Fuente, L. F. 1995. Factors affecting variation of udder traits of dairy ewes. Journal of Dairy Science 78: 842–849.Google Scholar
Goyache, F., Villa, A., Baro, J. A. and Alonso, L. 1999. Aplicación de un sistema de calificación morfológica continua en la raza Asturiana de los Valles. Federación Española de Asociaciones de Ganado Selecto (FEAGAS) 16: 8–17.Google Scholar
International Committee on Animal Recording. 1995. Recording guidelines: appendices to the international agreement of recording practices. Section 5: Conformation recording. Rome, Italy/RVN, Arnhem, the Netherlands.Google Scholar
Linko, S. 1998. Expert systems — what can they do for the food industry? Trends in Food Science and Technology 9: 3–12.Google Scholar
López, S., Goyache, F., Quevedo, J. R., Alonso, J., Ranilla, J., Luaces, O., Bahamonde, A. and del Coz, J. J. 2000. Un sistema inteligente para calificar morfológicamente bovinos de la raza Asturiana de los Valles. Revista Iberoamericana de la Inteligencia Artificial 10: 5–17.Google Scholar
Luaces, O., del Coz, J. J., Quevedo, J. R., Alonso, J., Ranilla, J. and Bahamonde, A. 1999. Autonomous clustering for machine learning. Lecture Notes in Computer Sciences, LNCS no. 1606, pp. 497–506. Springer-Verlag, Berlin.Google Scholar
Michalski, R. S., Bratko, I., and Kubat, M. 1998. Machine learning and data mining, methods and applications. John Wiley and Sons Ltd, Chichester.Google Scholar
Nilsson, N. J. 1998. Artificial intelligence: a new synthesis. Morgan Kaufmann, San Francisco, CA.Google Scholar
Pietersma, D., Lacroix, R., Lefebvre, D., Block, E. and Wade, K. M. 1999. Example-based knowledge acquisition for automated interpretation of milk recording data. Proceedings of the second European conference of the European Federation for Information Technology in Agriculture, Food and Environment, Bonn, Germany, pp. 669–677.Google Scholar
Quevedo, J. R. and Bahamonde, A. 1999. Aprendizaje de funciones usando inducción sobre clasificaciones discretas. Proceedings of the VIII conferencia de la Asociación Española para la Inteligencia Artificial, Murcia, Spain, vol. I, pp. 64–71.Google Scholar
Quinlan, J. R. 1993a. C4·5: pograms for machine learning. Morgan Kaufmann, San Mateo, CA.Google Scholar
Quinlan, J. R. 1993b. Combining instance-based and model-based learning. Proceedings of the tenth international machine learning conference. Morgan Kaufmann, Amherst, MA.Google Scholar
Quinlan, J. R. 2000. Cubist release 1·08, http:// www.rulequest.com/cubist-info.htmlGoogle Scholar
Rich, E., and Knigh, K. 1991. Artificial intelligence, second edition. McGraw-Hill, New York, NY. Google Scholar
Shi, M. J., Laloè, D., Menissier, F. and Renand, G. 1993. Estimation of genetic parameters of preweaning performance in the French Limousine cattle breed. Genetics, Selection, Evolution 25: 177–189.Google Scholar
Short, T. H. and Lawlor, T. J. 1992. Genetic parameters of conformation traits, milk yield and life in Holsteins. Journal of Dairy Science 71: 1987–1998.Google Scholar
Vallejo, M., Gutiérrez, J. P., Cima, M., Cañón, J., Alonso, L., Revuelta, J. R. and Goyache, F. 1993. Características de las canales de las razas bovinas asturianas. III. Valoración cuantitativa y predicción tisular de canales en la raza Asturiana de los Valles. Archivos de Zootecnia 42: 29–40.Google Scholar
Vukasinovic, N., Moll, J. and Künzi, N. 1997. Factor analysis for evaluating relationships between herd’s life and type traits in Swiss Brown cattle. Livestock Production Science 49: 227–234.Google Scholar
Wang, Y. and Witten, I. H. 1997. Inducing model trees for predicting continuous classes. Proceedings of the European conference on machine learning. University of Economics, Prague.Google Scholar
Yang, X. Z., Lacroix, R. and Wade, K. M. 1999. Neural detection of mastitis from dairy herd improvement records. Transactions of the American Society of Agricultural Engineers 42: 1063–1071.Google Scholar