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אסיף מאגר המחקר החקלאי
פותח על ידי קלירמאש פתרונות בע"מ -
On farm implementation of a fully automatic computer vision system for monitoring gait related measures in dairy cows
Year:
2014
Authors :
הלחמי, אילן
;
.
מלץ, אפרים
;
.
Volume :
4
Co-Authors:

Van Hertem, T., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Bahr, C., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Viazzi, S., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Steensels, M., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Romanini, C.E.B., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Lokhorst, C., Livestock Research, Wageningen UR, P.O. Box 65, Lelystad, Netherlands
Schlageter-Tello, A., Livestock Research, Wageningen UR, P.O. Box 65, Lelystad, Netherlands
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 6, Bet Dagan, Israel
Berckmam, D., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium

Facilitators :
From page:
2497
To page:
2505
(
Total pages:
9
)
Abstract:
The objective of this study was to implement a computer vision system for automatic monitoring of animal based measures relevant for lameness detection in a commercial dairy farm. The implementation procedure comprised the following steps: (1) start and stop of the video recordings, (2) identification of the cow in the video, and (3) video processing including the filtering of good quality images and the calculation of the back posture parameters used for classifying cows as lame or not lame. After implementation, the performance of the system was evaluated. All data were gathered from a Belgian commercial dairy farm. Between 20 September 2013 and 30 March 2014, 323 recording sessions were performed, together with 33 locomotion scoring events spread over time. The first step after recording the videos was identifying the cows in the video, which was successful for 79.2% ± 6.2% of the milked cows. In the second step of the video processing where the lameness related feature variables are extracted from the images, obtained an average analysis rate of 49.9% ± 11.3%. On average 80%) of the individual cows were at least 5 times per week automatically scored. Based on 3130 complete cow observations spread over time, a group level analysis was performed in the form of a receiver operating characteristics curve. The back posture measure (BPM) and ⊖2 were the two feature variables that reached the level of a fair measure for lameness detection.
Note:
Related Files :
Agriculture
computer vision
Computer vision system
Cow traffic
Farms
monitoring
Video cameras
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
0
Affiliations:
Database:
סקופוס
Publication Type:
מאמר מתוך כינוס
;
.
Language:
אנגלית
Editors' remarks:
ID:
24234
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:06
You may also be interested in
Scientific Publication
On farm implementation of a fully automatic computer vision system for monitoring gait related measures in dairy cows
4

Van Hertem, T., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Bahr, C., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Viazzi, S., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Steensels, M., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Romanini, C.E.B., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium
Lokhorst, C., Livestock Research, Wageningen UR, P.O. Box 65, Lelystad, Netherlands
Schlageter-Tello, A., Livestock Research, Wageningen UR, P.O. Box 65, Lelystad, Netherlands
Maltz, E., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 6, Bet Dagan, Israel
Halachmi, I., Institute of Agricultural Engineering, Agricultural Research Organization (ARO), Volcani Center, P.O. Box 6, Bet Dagan, Israel
Berckmam, D., M3-BIORES: Measure, Model and Manage Bioresponses, KU Leuven, Kasteelpark Arenberg 30, bus 2456, Heverlee, Belgium

On farm implementation of a fully automatic computer vision system for monitoring gait related measures in dairy cows
The objective of this study was to implement a computer vision system for automatic monitoring of animal based measures relevant for lameness detection in a commercial dairy farm. The implementation procedure comprised the following steps: (1) start and stop of the video recordings, (2) identification of the cow in the video, and (3) video processing including the filtering of good quality images and the calculation of the back posture parameters used for classifying cows as lame or not lame. After implementation, the performance of the system was evaluated. All data were gathered from a Belgian commercial dairy farm. Between 20 September 2013 and 30 March 2014, 323 recording sessions were performed, together with 33 locomotion scoring events spread over time. The first step after recording the videos was identifying the cows in the video, which was successful for 79.2% ± 6.2% of the milked cows. In the second step of the video processing where the lameness related feature variables are extracted from the images, obtained an average analysis rate of 49.9% ± 11.3%. On average 80%) of the individual cows were at least 5 times per week automatically scored. Based on 3130 complete cow observations spread over time, a group level analysis was performed in the form of a receiver operating characteristics curve. The back posture measure (BPM) and ⊖2 were the two feature variables that reached the level of a fair measure for lameness detection.
Scientific Publication
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