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Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity
Year:
2013
Source of publication :
Journal of Dairy Science
Authors :
Antler, Aharon
;
.
Halachmi, Ilan
;
.
Maltz, Ephraim
;
.
Van-Hertem, Tom
;
.
Volume :
96
Co-Authors:
Van Hertem, T., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel, Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Antler, A., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Romanini, C.E.B., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Viazzi, S., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Bahr, C., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Schlageter-Tello, A., WageningenUR Livestock Research, PO Box 65, NL-8200AB Lelystad, Netherlands
Lokhorst, C., WageningenUR Livestock Research, PO Box 65, NL-8200AB Lelystad, Netherlands
Berckmans, D., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Facilitators :
From page:
4286
To page:
4298
(
Total pages:
13
)
Abstract:
The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well. © 2013 American Dairy Science Association.
Note:
Related Files :
Animal
animal behavior
animal disease
animal housing
Animals
cattle
Female
lactation
milk
Monitoring, Physiologic
Show More
Related Content
More details
DOI :
10.3168/jds.2012-6188
Article number:
0
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
21178
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:42
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Scientific Publication
Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity
96
Van Hertem, T., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel, Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Antler, A., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Romanini, C.E.B., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Viazzi, S., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Bahr, C., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Schlageter-Tello, A., WageningenUR Livestock Research, PO Box 65, NL-8200AB Lelystad, Netherlands
Lokhorst, C., WageningenUR Livestock Research, PO Box 65, NL-8200AB Lelystad, Netherlands
Berckmans, D., Division Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, BE-3001 Heverlee, Belgium
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, PO Box 6, Bet-Dagan IL-50250, Israel
Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity
The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY = 0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well. © 2013 American Dairy Science Association.
Scientific Publication
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