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Poster De Conférence Année : 2014

Analysing the back of dairy cows in 3D imaging to better assess body condition

Résumé

Body condition is an important trait in dairy cow management, usually measured with the body condition score (BCS), which is subjective, and not very sensitive. The aim of this work was to develop and to validate a method, NEC3D, estimating BCS with 3D pictures of dairy cows back, from the pins to the hooks, which is commonly used as reference area in the BCS scoring systems. A 57 cows 3D-shapes dataset with large BCS variability (0.5 to 4.75 on a 0-5 scale), transformed with a principal component analysis, was built for calibration. The principal components were performed on BCS with multiple linear regressions. Four anatomical points had to be identi¿ed manually to normalise the pictures. Influence of two different ways of points’ identification and of the picture resolution on method quality was analysed. External validation was evaluated on two additional datasets: one with cows used for calibration, but with a different stage in milking (valididem) and one with cows not used for calibration (validdiff). To fully qualify the method, the reproducibility was estimated with 6 cows using 8 3D-shapes of each cow obtained the same day. Both ways of points’ identification had quite good results in terms of calibration (R2=1) and differed slightly on validation quality (RMSE=0.34 vs 0.32 for validdiff). Nec3d was 2.8 times more reproducible than usual BCS (σ=0.1 vs 0.28). The lowest resolution implied a loss of reproducibility, but did not increase the error of prediction. A simplified acquisition system, implying low resolution, could therefore be developed. The error of prediction was similar for valididem and validdiff, indicating that the NEC3D is not less efficient for cows not used in the calibration set. Assessing body condition thanks to 3D shapes appears to be a promising tool which can improve phenotyping of this trait.
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Dates et versions

hal-01210673 , version 1 (02-10-2015)

Identifiants

  • HAL Id : hal-01210673 , version 1
  • PRODINRA : 270956

Citer

Amélie Fischer, T. Luginbühl, L. Delattre, J.M. Delouard, Philippe Faverdin. Analysing the back of dairy cows in 3D imaging to better assess body condition. 65. Annual Meeting of the European Federation of Animal Science (EAAP), Aug 2014, Copenhague, Denmark. Wageningen Academic Publishers, Annual Meeting of the European Association for Animal Production, 20, 2014, Annual Meeting of the European Association for Animal Production. ⟨hal-01210673⟩
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