Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 536219
Title Automated body weight prediction of dairy cows using 3-dimensional vision
Author(s) Song, X.; Bokkers, E.A.M.; Tol, P.P.J. van der; Groot Koerkamp, P.W.G.; Mourik, S. van
Source Journal of Dairy Science 101 (2018)5. - ISSN 0022-0302 - p. 4448 - 4459.
DOI https://doi.org/10.3168/jds.2017-13094
Department(s) Farm Technology Group
Animal Production Systems
WIAS
PE&RC
WIMEK
LR - Veehouderij en omgeving
Publication type Refereed Article in a scientific journal
Publication year 2018
Keyword(s) automation - dairy cattle - morphological trait - three-dimensional vision - uncertainty
Abstract The objectives of this study were to quantify the error of body weight prediction using automatically measured morphological traits in a 3-dimensional (3-D) vision system and to assess the influence of various sources of uncertainty on body weight prediction. In this case study, an image acquisition setup was created in a cow selection box equipped with a top-view 3-D camera. Morphological traits of hip height, hip width, and rump length were automatically extracted from the raw 3-D images taken of the rump area of dairy cows (n = 30). These traits combined with days in milk, age, and parity were used in multiple linear regression models to predict body weight. To find the best prediction model, an exhaustive feature selection algorithm was used to build intermediate models (n = 63). Each model was validated by leave-one-out cross-validation, giving the root mean square error and mean absolute percentage error. The model consisting of hip width (measurement variability of 0.006 m), days in milk, and parity was the best model, with the lowest errors of 41.2 kg of root mean square error and 5.2% mean absolute percentage error. Our integrated system, including the image acquisition setup, image analysis, and the best prediction model, predicted the body weights with a performance similar to that achieved using semi-automated or manual methods. Moreover, the variability of our simplified morphological trait measurement showed a negligible contribution to the uncertainty of body weight prediction. We suggest that dairy cow body weight prediction can be improved by incorporating more predictive morphological traits and by improving the prediction model structure.
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