Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems

Computational and Mathematical Methods in Medicine

Volumn 2012, Issue , 2012, Pages

  Shahinfar, Saleh ^a   Mehrabani Yeganeh, Hassan ^a   Lucas, Caro ^a   Kalhor, Ahmad ^a   Kazemian, Majid ^a   Weigel, Kent A ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DAIRIES; FORECASTING; FUZZY INFERENCE; FUZZY LOGIC; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; SOFT COMPUTING; TREES (MATHEMATICS);

ANALYTICAL METHOD; BEST LINEAR UNBIASED PREDICTIONS; HYBRID INTELLIGENT SYSTEM; INTELLIGENT LEARNING; LOCAL LINEAR MODEL TREE; NEUROFUZZY SYSTEM; PERFORMANCE DATA; SOFTCOMPUTING TECHNIQUES;

FUZZY NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CATTLE FARMING; CORRELATION ANALYSIS; DAIRY CATTLE; FUZZY LOGIC; FUZZY SYSTEM; MILK YIELD; PERCEPTRON; ALGORITHM; ANIMAL; ANIMAL MODEL; ARTIFICIAL INTELLIGENCE; BREEDING; CATTLE; COMPUTER SIMULATION; DAIRYING; FEMALE; LACTATION; MALE; METHODOLOGY; MILK; STATISTICAL MODEL;

ANIMALIA; BOS; HUMAN HERPESVIRUS 4;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BREEDING; CATTLE; COMPUTER SIMULATION; DAIRYING; FEMALE; FUZZY LOGIC; LACTATION; LINEAR MODELS; MALE; MILK; MODELS, ANIMAL; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER);

EID: 84866994735     PISSN: 1748670X     EISSN: 17486718     Source Type: Journal    
DOI: 10.1155/2012/127130     Document Type: Article

References (35)

1. McQueen R. J., Garner S. R., Nevill-Manning C. G., Witten I. H., Applying machine learning to agricultural data. Computers and Electronics in Agriculture 1995 12 4 275 293 2-s2.0-41149116073
2. Kim T., Heald C. W., Inducing inference rules for the classification of bovine mastitis. Computers and Electronics in Agriculture 1999 23 1 27 42 2-s2.0-0032846871 10.1016/S0168-1699(99)00003-4 (Pubitemid 29461766)
3. Mitchell R. S., Sherlock R. A., Smith L. A., An investigation into the use of machine learning for determining oestrus in cows. Computers and Electronics in Agriculture 1996 15 3 195 213 2-s2.0-0042538910 10.1016/0168-1699(96)00016-6 (Pubitemid 126394969)
4. Pietersma D., Lacroix R., Lefebvre D., Wade K. M., Induction and evaluation of decision trees for lactation curve analysis. Computers and Electronics in Agriculture 2003 38 1 19 32 2-s2.0-0037238954 10.1016/S0168-1699(02)00105-9 (Pubitemid 36122029)
5. Allore H. G., Jones L. R., Merrill W. G., Oltenacu P. A., A decision support system for evaluating mastitis information. Journal of Dairy Science 1995 78 6 1382 1398 2-s2.0-0029315114
6. Domecq J. J., Nebel R. L., McGilliard M. L., Pasquino A. T., Expert system for evaluation of reproductive performance and management. Journal of Dairy Science 1991 74 10 3446 3453 2-s2.0-0026231510
7. Caraviello D. Z., Weigel K. A., Craven M., Gianola D., Cook N. B., Nordlund K. V., Fricke P. M., Wiltbank M. C., Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms. Journal of Dairy Science 2006 89 12 4703 4722 2-s2.0-33846281930 (Pubitemid 46118372)
8. Sanzogni L., Kerr D., Milk production estimates using feed forward artificial neural networks. Computers and Electronics in Agriculture 2001 32 1 21 30 2-s2.0-0034991828 10.1016/S0168-1699(01)00151-X (Pubitemid 32500927)
9. Sharma R. K., Sharma A. K., Sharma A., Neuro-computing paradigms with application to dairy production. Lecture Compendium, National Training Programme on InFormation Resources on Genetics and Documentation Techniques For Livestock Improvement 2004 Karnal, India Centre For Advanced StudiesAG&B, Dairy Cattle Breeding Division, NDRI 173 178
10. Shahinfar S., Intelligent decision making for progeny testing in dairy cattle using neuro-fuzzy models [M.S. thesis] 2007 Tehran, Iran University of Tehran
11. Heald C. W., Kim T., Sischo W. M., Cooper J. B., Wolfgang D. R., A computerized mastitis decision aid using farm-based records: an artificial neural network approach. Journal of Dairy Science 2000 83 4 711 720 2-s2.0-0034169904
12. Nielen M., Schukken Y. H., Brand A., Haring S., Ferwerda-van Zonneveld R. T., Comparison of analysis techniques for on-line detection of clinical mastitis. Journal of Dairy Science 1995 78 5 1050 1061 2-s2.0-0029300082
13. Kim S., Cho S. I., Neural network modeling and fuzzy control simulation for bread-baking process. Transactions of the ASAE 1997 40 671 676 (Pubitemid 27324435)
14. Ribeiro R. S. F., Yoder R. E., Wilkerson J. B., Russell B. D., A fuzzy logic base control system optimized via neural networks. ASAE Paper 1998 98:2169
15. Lacroix R., Huijbers J., Tiemessen R., Lefebvre D., Marchand D., Wade K. M., Fuzzy set-based analytical tools for dairy herd improvement. Applied Engineering in Agriculture 1998 14 1 79 85 2-s2.0-0031696281 (Pubitemid 128640618)
16. De Mol R. M., Woldt W. E., Application of fuzzy logic in automated cow status monitoring. Journal of Dairy Science 2001 84 2 400 410 2-s2.0-0035259684 (Pubitemid 33609622)
17. Kamphuis C., Sherlock R., Jago J., Mein G., Hogeveen H., Automatic detection of clinical mastitis is improved by in-line monitoring of somatic cell count. Journal of Dairy Science 2008 91 12 4560 4570 2-s2.0-57749109878 10.3168/jds.2008-1160 (Pubitemid 352847746)
18. Rekaya R., Weigel K. A., Gianola D., Comparison between traditional and fuzzy logic definitions of herd-year-season groups in Holsteins. Journal of Animal Science 1999 77, supplement 1 33
19. Takagi H., Hyashi I., NN-Driven fuzzy reasoning. International Journal of Approximate Reasoning 1991 5 91 212
20. Lacroix R., Strasser M., Kok R., Wade K. M., Performance analysis of a fuzzy decision-support system for culling of dairy cows. Canadian Agricultural Engineering 1998 40 2 139 151 2-s2.0-0032035553
21. Salehi F., Lacroix R., Wade K. M., Improving dairy yield predictions through combined record classifiers and specialized artificial neural networks. Computers and Electronics in Agriculture 1998 20 3 199 213 2-s2.0-0032143484 10.1016/S0168-1699(98)00018-0 (Pubitemid 28417811)
22. Lacroix R., Wade K. M., Kok R., Hayes J. F., Prediction of cow performance with a connectionist model. Transactions of the American Society of Agricultural Engineers 1995 38 5 1573 1579 2-s2.0-0029368068
23. Salehi F., Lacroix R., Wade K. M., Effects of learning parameters and data presentation on the performance of backpropagation networks for milk yield prediction. Transactions of the American Society of Agricultural Engineers 1998 41 1 253 259 2-s2.0-0031956801 (Pubitemid 28153782)
24. Groeneveld E., PEST Users' Manual 1990 Neustadt, Germany Institute of Animal Husbandry and Animal Behavior Federal Research Center
25. Sharma A. K., Sharma R. K., Kasana H. S., Prediction of first lactation 305-day milk yield in Karan Fries dairy cattle using ANN modeling. Applied Soft Computing Journal 2007 7 3 1112 1120 2-s2.0-34047269620 10.1016/j.asoc.2006.07. 002 (Pubitemid 46551934)
26. Sayeed M. S., Whittaker A. D., Kehtarnavaz N. D., Snack quality evaluation method based on image features and neural network prediction. Transactions of the American Society of Agricultural Engineers 1995 38 4 1239 1245 2-s2.0-0029338784
27. Ruan R., Almaer S., Zou C., Chen P. L., Spectrum analysis of mixing power curves for neural network prediction of dough rheological properties. Transactions of the American Society of Agricultural Engineers 1997 40 3 677 681 2-s2.0-0031147678 (Pubitemid 27324436)
28. Parmar R. S., McClendon R. W., Hoogenboom G., Blankenship P. D., Cole R. J., Dorner J. W., Estimation of aflatoxin contamination in preharvest peanuts using neural networks. Transactions of the American Society of Agricultural Engineers 1997 40 3 809 813 2-s2.0-0031147928 (Pubitemid 27324454)
29. Hagan M. T., Demuth H. B., Beale M. H., Neural Network Design 1996 Boston, Mass, USA PWS Publishing
30. Mitchell T. M., Machine Learning 1997 New York, NY, USA McGraw-Hill
31. Salehi F., Lacroix R., Wade K. M., Development of neuro-fuzzifiers for qualitative analyses of milk yield. Computers and Electronics in Agriculture 2000 28 3 171 186 2-s2.0-0034283040 10.1016/S0168-1699(00)00128-9
32. Nelles O., Nonlinear System Identification 2001 Berlin, Germany Springer
33. Nelles O., Nonlinear system identification with local linear neuro-fuzzy models [Ph.D. thesis] 1999 Aachen, Germany TU Darmstadt, Shaker
34. Hestenes M. R., Stiefel E., Method of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards 1952 6, article 49
35. Levenberg K., A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 1944 2 164 168