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 549327
Title Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions
Author(s) Song, X.; Bokkers, E.A.M.; Mourik, S. van; Groot Koerkamp, P.W.G.; Tol, P.P.J. van der
Source Journal of Dairy Science 102 (2019)5. - ISSN 0022-0302 - p. 4294 - 4308.
Department(s) Farm Technology
Animal Production Systems
Livestock & Environment
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) 3-dimensioanl camera - automatic - body condition score - dairy cattle

Machine vision technology has been used in automated body condition score (BCS) classification of dairy cows. The current vision-based classifications use information acquired from a limited number of body regions of the cow. Our study aimed to improve automated BCS classification by including multiple body condition–related features extracted from 3 viewpoints in 8 body regions. The data set of this study included 44 lactating cows with their BCS evenly distributed over the scale of BCS from 1.5 to 4.5 units. The body images of these cows were recorded over 2 consecutive days using 3-dimensional cameras positioned to view the cow from the top, right side, and rear. Each image was automatically processed to identify anatomical landmarks on the body surface. Around these anatomical landmarks, the bony prominences and body surface depressions were quantified to describe 8 body condition–related features. A manual BCS of each cow was independently assigned by 2 trained assessors using the same predefined protocol. With the extracted features as inputs and manual BCS as the reference, we built a nearest-neighbor classification model to classify BCS and obtained an overall classification sensitivity of 0.72 using a 10-fold cross-validation. We conclude that the sensitivity of automated BCS classification has been improved by expanding the selection of body condition–related features extracted from multiple body regions.

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