Detection of mastitis and lameness in dairy cows using wavelet analysis

Livestock Science

Volumn 148, Issue 3, 2012, Pages 227-236

  Miekley, Bettina ^a   Traulsen, Imke ^a   Krieter, Joachim ^a  

^a UNIVERSITY OF KIEL   (Germany)

Author keywords

CUSUM chart; Lameness detection; Mastitis detection; Wavelet analysis

Indexed keywords

BOS;

EID: 84864777838     PISSN: 18711413     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.livsci.2012.06.010     Document Type: Article

References (40)

1. Brandt M., Haeussermann A., Hartung E. Invited review: technical solutions for analysis of milk constituents and abnormal milk. J. Dairy Sci. 2010, 93:427-436.
2. Cavero D., Tölle K.H., Henze C., Buxadé C., Krieter J. Mastitis detection in dairy cows by application of neural networks. Livest. Sci. 2008, 114:280-286.
3. Cavero D., Tölle K.H., Rave G., Buxadé C., Krieter J. Analysing serial data for mastitis detection by means of local regression. Livest. Sci. 2007, 110:101-110.
4. Chuganda M.G.G., Friggens N.C., Rasmussen M.D., Larsen T. A model for detection of individual cow mastitis based on an indicator measured in milk. J. Dairy Sci. 2006, 89:2980-2998.
5. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 1990, 36:961-1005.
6. 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.
7. de Mol R.M., Ouweltjes W., Kroeze G.H., Hendriks M.M.W.B. Detection of oestrus and mastitis: field performance of a model. Eng. Agric 2001.
8. de Vries A., Reneau J.K. Application of statistical process control charts to monitor changes in animal production systems. J. Anim. Sci. 2010, 88:E11-24.
9. DVG (Deutsche Veterinärmedizinische Gesellschaft), 2002. Leitlinien zur Bekämpfung der Mastitis des Rindes als Bestandsproblem. Sachverständigenausschuss "Subklinische Mastitis".
10. Ganesan R., Das T.K., Venkataraman V. Wavelet-based multiscale statistical process monitoring: a literature review. IIE Trans. 2004, 36:787-806.
11. Gencay Z., Selcuk F., Whitcher B. An introduction to wavelets and other filtering methods in finance and economics 2002, Academic Press, San Diego, USA.
12. Green L.E., Hedges V.J., Schukken Y.H., Blowey R.W., Packington A.J. The impact of clinical lameness on the milk yield of dairy cows. J. Dairy Sci. 2002, 85:2250-2256.
13. Gröhn Y.T., Wilson D.J., González R.N., Hertl J.A., Schulte H., Bennett G., Schukken Y.H. Effect of pathogen-specific clinical mastitis on milk yield in dairy cows. J. Dairy Sci. 2004, 87:3358-3374.
14. Hawkins, D.M., Olwell, D.H., 1998. Cumulative Sum Charts and Charting for Quality Improvement. New York.
15. Hogeveen H., Kamphuis C., Steeneveld W., Mollenhorst H. Sensors and clinical mastitis-the quest for the perfect alert. Sensors 2010, 10:7991-8009.
16. Hogeveen H., Ouweltjes W. Sensors and management support in high-technology milking. J. Anim. Sci. 2003, 81:1-10.
17. Jun-Zeh Y., Chin-Shan Y., Chih-Wen L. A new method for power signal harmonic analysis. IEEE Trans. Power Delivery 2005, 20:1235-1239.
18. Kamphuis C., Mollenhorst H., Feelders A., Pietersma D., Hogeveen H. Decision-tree induction to detect clinical mastitis with automatic milking. Comput. Electron. Agric. 2010, 70:60-68.
19. 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.
20. Kara S., Dirgenali F. A system to diagnose atherosclerosis via wavelet transforms, principal component analysis and artificial neural networks. Expert Syst. Appl. 2007, 32:632-640.
21. 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.
22. Krieter J., Engler J., Tölle K.H., Timm H.H., Hohls E. Control charts applied to simulated sow herd datasets. Livest. Sci. 2009, 121:281-287.
23. Kruse S., Traulsen I., Salau J., Krieter J. A note on using wavelet analysis for disease detection in lactating sows. Comput. Electron. Agric. 2011, 77:105-109.
24. Lio P. Wavelets in bioinformatics and computational biology: state of art and perspectives. Bioinformatics 2003, 19:2-9.
25. Lu N., Wang F., Gao F. Combination method of principal component and wavelet analysis for multivariate process monitoring and fault diagnosis. Ind. Eng. Chem. Res. 2003, 42:4198-4207.
26. Lukas J.M., Reneau J.K., Wallace R., Hawkins D., Munoz-Zanzi C. A novel method of analyzing daily milk production and electrical conductivity to predict disease onset. J. Dairy Sci. 2009, 92:5964-5976.
27. Madsen T.N., Andersen S., Kristensen A.R. Modelling the drinking patterns of young pigs using a state space model. Comput. Electron. Agric. 2005, 48:39-61.
28. MATLAB, 2010. Wavelet Toolbox, for Use with MATLAB® 7.10.0. MathWorks.
29. Mazrier H., Tal S., Aizinbud E., Bargai U. A field investigation of the use of the pedometer for the early detection of lameness in cattle. Can. Vet. J. 2006, 883-886.
30. Mertens K., Decuypere E., De Baerdemaeker J., De Ketelaere B. Statistical control charts as a support tool for the management of livestock production. J. Agric. Sci. 2011, 149:369-384.
31. Mertikas S., Damianidis K. Monitoring the quality of GPS station coordinates in real time. GPS Solutions 2007, 11:119-128.
32. Milner P., Page K.L., Hillerton J.E. The effects of early antibiotic treatment following diagnosis of mastitis detected by a change in the electrical conductivity of milk. J. Dairy Sci. 1997, 80:859-863.
33. Montgomery D.C. Statistical quality control: a modern introduction 2009, John Wiley and Sons, Inc., Arizona.
34. Pastell M., Tiusanen J., Hakojärvi M., Hänninen L. A wireless accelerometer system with wavelet analysis for assessing lameness in cattle. Biosyst. Eng. 2009, 104:545-551.
35. Pastell M.E., Kujala M. A probabilistic neural network model for lameness detection. J. Dairy Sci. 2007, 90:2283-2292.
36. Pyörälä S. Indicators of inflammation in the diagnosis of mastitis. Vet. Res. 2003, 34:565-578.
37. Quimby W.F., Sowell B.F., Bowmann J.G.P., Branine M.E., Hubbert M.E., Sherwood H.W. Application of feeding behavior to predict morbidity of newly received calves in a commercial feedlot. Can. J. Anim. Sci. 2001, 81:315-320.
38. Sang Y.-F., Wang D., Wu J.-C. Entropy-Based method of choosing the decomposition level in wavelet threshold de-noising. Entropy 2010, 12:1499-1513.
39. SAS, 2009. SAS/STAT User's Guide (Release 9.1). SAS Institute, Cary NC, USA.
40. Windig J.J., Calus M.P.L., de Jong G., Veerkamp R.F. The association between somatic cell count patterns and milk production prior to mastitis. Livest. Prod. Sci. 2005, 96:291-299.