Detection of clinical mastitis with sensor data from automatic milking systems is improved by using decision-tree induction

Journal of Dairy Science

Volumn 93, Issue 8, 2010, Pages 3616-3627

  Kamphuis, C ^a   Mollenhorst, H ^a   Heesterbeek, J A P ^a   Hogeveen, H ^a,b  

^a UTRECHT UNIVERSITY   (Netherlands)

^b WAGENINGEN UNIVERSITY   (Netherlands)

Author keywords

Clinical mastitis detection; Decision tree induction; Robotic milking; Sensor data

Indexed keywords

ANIMAL; ARTICLE; BOVINE MASTITIS; CATTLE; CHEMISTRY; DAIRYING; DECISION TREE; ELECTRIC CONDUCTIVITY; FEMALE; HUMAN; INSTRUMENTATION; METHODOLOGY; MILK; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; BOVINAE; DEVICES; MASTITIS, BOVINE; PROCEDURES;

ANIMALS; CATTLE; DAIRYING; DECISION TREES; ELECTRIC CONDUCTIVITY; FEMALE; HUMANS; MASTITIS, BOVINE; MILK; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 77955028651     PISSN: 00220302     EISSN: 15253198     Source Type: Journal    
DOI: 10.3168/jds.2010-3228     Document Type: Article

References (29)

1. Claycomb R.W., Johnstone P.T., Mein G.A., Sherlock R.A. An automated in-line clinical mastitis detection system using measurement of conductivity from foremilk of individual udder quarters. N. Z. Vet. J. 2009, 57:208-214.
2. de Koning K., Slaghuis B., van der Vorst Y. Milk quality on farms with an automatic milking system. Automatic Milking-A Better Understanding 2004, 311-320. Wageningen Academic Publishers, Wageningen, the Netherlands. A. Meijering, H. Hogeveen, C.J.A.M. de Koning (Eds.).
3. de Mol R.M., Kroeze G.H., Achten J.M.F.H., Maatje K., Rossing W. Results of a multivariate approach to automated oestrus and mastitis detection. Livest. Prod. Sci. 1997, 48:219-227.
4. Detilleux J., Arendt J., Lomba F., Leroy P. Methods for estimating areas under receiver-operating characteristic curves: Illustration with somatic-cell scores in subclinical intramammary infections. Prev. Vet. Med. 1999, 41:75-88.
5. Dohmen W., Neijenhuis F., Hogeveen H. Relationship between udder health and hygiene on farms with an automatic milking system. J. Dairy Sci. 2010, 10.3168/jds.2010-3028.
6. Freund Y., Schapire R.E. Experiments with a new boosting algorithm. Proceedings of the Thirteenth International Conference of Machine Learning 1996, 148-156. Morgan Kaufmann Publishers, San Francisco, CA. L. Saitta (Ed.).
7. Friggens N.C., Chagunda M.G.G., Bjerring M., Ridder C., Hojsgaard S., Larsen T. Estimating degree of mastitis from time-series measurements in milk: A test of a model based on lactate dehydrogenase measurements. J. Dairy Sci. 2007, 90:5415-5427.
8. Halasa T., Huijps K., Osteras O., Hogeveen H. Economic effects of bovine mastitis and mastitis management: A review. Vet. Q. 2007, 29:18-31.
9. Hillerton J.E. Detecting mastitis cow-side. National Mastitis Council 39th Annual Meeting 2000, 48-53.
10. Hogeveen H., Ouweltjes W. Sensors and management support in high-technology milking. J. Anim. Sci. 2003, 81(Suppl. 3):1-10.
11. ISO. 2007. Automatic milking systems-requirements and testing. Annex C: Example of methods of evaluating detection systems for milk deemed as abnormal due to blood or to changes in homogeneity. ISO 20966:2007. International Organization for Standardization, Geneva, Switzerland.
12. Kamphuis C., Mollenhorst H., Feelders A.J., Pietersma D., Hogeveen H. Decision-tree induction to detect clinical mastitis with automatic milking. Comput. Electron. Agric. 2010, 70:60-68.
13. Kamphuis C., Mollenhorst H., Heesterbeek J.A.P., Hogeveen H. Data mining to detect clinical mastitis with automatic milking. Proceedings of the 5th IDF Mastitis Conf. 2010, 568-572. J.E. Hillerton (Ed.).
14. Kamphuis C., Pietersma D., van der Tol R., Wiedemann M., Hogeveen H. Using sensor data patterns from an automatic milking system to develop predictive variables for classifying clinical mastitis and abnormal milk. Comput. Electron. Agric. 2008, 62:169-181.
15. 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. J. Dairy Sci. 2008, 91:4560-4570.
16. Keefe G., McCarron J., MacDonald K., Cameron M. The scientific bases for using on-farm culture systems. Proceedings of the 49th Annual Meeting of the National Mastitis Council 2010, 141-148.
17. Kramer E., Cavero D., Stamer E., Krieter J. Mastitis and lameness detection in dairy cows by application of fuzzy logic. Livest. Sci. 2009, 125:92-96.
18. McClish D.K. Analyzing a portion of the ROC curve. Med. Decis. Making 1989, 9:190-195.
19. Mein G.A., Rasmussen M.D. Performance evaluation of systems for automated monitoring of udder health: Would the real gold standard please stand up?. Mastitis Control-From Science to Practice 2008, 259-266. Wageningen Academic Publishers, Wageningen, the Netherlands. T.J.G.M. Lam (Ed.).
20. Mitchell T.M. Machine learning and data mining. Commun. ACM 1999, 42:30-36.
21. Norberg E., Hogeveen H., Korsgaard I.R., Friggens N.C., Sloth K.H.M.N., Lovendahl P. Electrical conductivity of milk: Ability to predict mastitis status. J. Dairy Sci. 2004, 87:1099-1107.
22. Quinlan J.R. Induction of decision trees. Mach. Learn. 1986, 1:81-106.
23. Rasmussen M.D. Visual scoring of clots in foremilk. J. Dairy Res. 2005, 72:406-414.
24. Rasmussen M.D., Bjerring M. Visual scoring of milk mixed with blood. J. Dairy Res. 2005, 72:257-263.
25. Sherlock R., Hogeveen H., Mein G., Rasmussen M.D. Performance evaluation of systems for automated monitoring of udder health: Analytical issues and guidelines. Mastitis Control-From Science to Practice 2008, 275-282. Wageningen Academic Publishers, Wageningen, the Netherlands. T.J.G.M. Lam (Ed.).
26. Steeneveld W., Gaag L.C.d., Ouweltjes W., Mollenhorst H., Hogeveen H. Discriminating between true-positive and false-positive clinical mastitis alerts from automatic milking systems. J. Dairy Sci. 2010, 93:2559-2568. 10.3168/jds.2009-3020.
27. Van den Borne B.H.P., Van Schaik G., Lam T.J.G.M., Nielen M. A comparison of the occurrence of mastitis in Dutch primi- and multiparous cows. Mastitis Control-From Science to Practice 2008, 55-62. Wageningen Academic Publishers, Wageningen, the Netherlands. T.J.G.M. Lam (Ed.).
28. Webb G.I. Multiboosting: A technique for combining boosting and bagging. Mach. Learn. 2000, 40:159-196.
29. Witten I.H., Frank E. Data Mining; Practical Machine Learning Tools and Techniques 2005, Morgan Kaufmann Publishers, San Francisco, CA. 2nd ed. I.H. Witten, E. Frank (Eds.).