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פותח על ידי קלירמאש פתרונות בע"מ -
Lameness detection in dairy cattle: Single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
Year:
2016
Source of publication :
Animal
Authors :
הלחמי, אילן
;
.
מלץ, אפרים
;
.
Volume :
10
Co-Authors:
Van Hertem, T., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium, Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Bahr, C., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Tello, A.S., Wageningen UR Livestock Research, PO Box 338, Wageningen, Netherlands
Viazzi, S., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Steensels, M., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Romanini, C.E.B., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Lokhorst, C., Wageningen UR Livestock Research, PO Box 338, Wageningen, Netherlands
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Berckmans, D., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Facilitators :
From page:
1525
To page:
1532
(
Total pages:
8
)
Abstract:
The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used. © The Animal Consortium 2015.
Note:
Related Files :
Dairy cow
Individual history
Lameness detection
Multi-sensing
Sensor technology
עוד תגיות
תוכן קשור
More details
DOI :
10.1017/S1751731115001457
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
26766
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:25
Scientific Publication
Lameness detection in dairy cattle: Single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
10
Van Hertem, T., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium, Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Bahr, C., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Tello, A.S., Wageningen UR Livestock Research, PO Box 338, Wageningen, Netherlands
Viazzi, S., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Steensels, M., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Romanini, C.E.B., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Lokhorst, C., Wageningen UR Livestock Research, PO Box 338, Wageningen, Netherlands
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, PO Box 6, Bet Dagan, Israel
Berckmans, D., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Leuven, Belgium
Lameness detection in dairy cattle: Single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing
The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC)=0.757±0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate video-based system (AUC=0.732±0.011) performed better than the multivariate milk sensors (AUC=0.604±0.026) and the multivariate behaviour sensors (AUC=0.633±0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC=0.669±0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ2, the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1%±4.7%) and specificity (83.2%±2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used. © The Animal Consortium 2015.
Scientific Publication
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