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Using a 3D camera to evaluate the back posture of dairy cows
Year:
2013
Source of publication :
Authors :
Halachmi, Ilan
;
.
Van-Hertem, Tom
;
.
Volume :
Co-Authors:

S Viazzi, ,  A Schlagater-Tello, C Bahr, C EB Romanini, , C Lokhorst, D Berckmans

Facilitators :
From page:
0
To page:
0
(
Total pages:
1
)
Abstract:

n this study, a new computer vision technique to automatically detect lameness in dairy cows was evaluated. A 3D camera system was used to extract the back posture of the animals from top view perspective in a fully automatic way. Four parameters to describe the curvature of the back of the cows were used by a decision tree to classify lame and not lame cows. The experiment was conducted in a commercial Israeli dairy farm. The classification performance of the 3D algorithm was evaluated against the visual locomotion scores given by an expert veterinary. A dataset of 273 cows served to train the model and a dataset of 906 cows to validate it. The analysis led to a sensitivity of 67% and a specificity of 90% on a 2-point level scale (lame or not lame) on the validation dataset. These results show that the application of a 3D camera in dairy farming is feasible and can be used in order to develop a fully automatic lameness monitoring tool in dairy farming.

Note:
Related Files :
3D
Cameras
Computer vision techniques
Dairy cow
Lameness detection
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Related Content
More details
DOI :
http://dx.doi.org/10.13031/aim.20131620172) @201
Article number:
0
Affiliations:
Database:
Google Scholar
Publication Type:
Abstract
;
.
Language:
English
Editors' remarks:
ID:
43822
Last updated date:
02/03/2022 17:27
Creation date:
08/09/2019 12:53
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Scientific Publication
Using a 3D camera to evaluate the back posture of dairy cows

S Viazzi, ,  A Schlagater-Tello, C Bahr, C EB Romanini, , C Lokhorst, D Berckmans

Using a 3D camera to evaluate the back posture of dairy cows .

n this study, a new computer vision technique to automatically detect lameness in dairy cows was evaluated. A 3D camera system was used to extract the back posture of the animals from top view perspective in a fully automatic way. Four parameters to describe the curvature of the back of the cows were used by a decision tree to classify lame and not lame cows. The experiment was conducted in a commercial Israeli dairy farm. The classification performance of the 3D algorithm was evaluated against the visual locomotion scores given by an expert veterinary. A dataset of 273 cows served to train the model and a dataset of 906 cows to validate it. The analysis led to a sensitivity of 67% and a specificity of 90% on a 2-point level scale (lame or not lame) on the validation dataset. These results show that the application of a 3D camera in dairy farming is feasible and can be used in order to develop a fully automatic lameness monitoring tool in dairy farming.

Scientific Publication
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