By Alchanatis, Victor and Antler, Aharon and Bahr, Claudia and Berckmans, Daniel and Halachmi, Ilan and Lokhorst, Kees and Maltz, Ephraim and Romanini, Eduardo C. B. and Schlageter-Tello, Andres A. and Steensels, Machteld and Van Hertem, Tom and Viazzi, Stefano, Biosystems Engineering, 2014
Research Paper Web Link / URL:
http://www.sciencedirect.com/science/article/pii/S1537511014000142
http://www.sciencedirect.com/science/article/pii/S1537511014000142
Description
Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3D-camera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.
Manual locomotion scoring for lameness detection is a time-consuming and subjective procedure. Therefore, the objective of this study is to optimise the classification output of a computer vision based algorithm for automated lameness scoring. Cow gait recordings were made during four consecutive night-time milking sessions on an Israeli dairy farm, using a 3D-camera. A live on-the-spot assessed 5-point locomotion score was the reference for the automatic lameness score evaluation. A dataset of 186 cows with four automatic lameness scores and four live locomotion score repetitions was used for testing three different classification methods. The analysis of the automatic scores as independent observations led to a correct classification rate of 53.0% on a 5-point level scale. A multinomial logistic regression model based on four individual consecutive measures obtained a correct classification rate of 60.2%. When allowing a 1 unit error on the 5-point level scale, a correct classification rate of 90.9% was obtained. Strict binary classification to Lame vs. Not-Lame categories reached 81.2% correct classification rate. The use of cow individual consecutive measurements improved the correct classification rate of an automatic lameness detection system.
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