Corrigendum to “On the value of the phenotypes in the genomic era” (J. Dairy Sci. 97:7905–7915)

González-Recio, Óscar; Coffey, Michael P.; Pryce, J.E.

doi:10.3168/jds.2019-102-6-5764

Cited by 1 publication

(1 citation statement)

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“…The validity and speed of this method allows for multiple quantitative morphological traits to be implemented in breeding programs. Despite the success of ongoing dairy breeding programs (Wiggans et al, 2017), including more and accurately quantified measurements has the potential to result in further improvements (Goddard, 2009;Gonzalez-Recio et al, 2014). Furthermore, this method uses standard side-view stud images which are inexpensive to generate and store.…”

Section: Discussionmentioning

confidence: 99%

Estimating Conformational Traits in Dairy Cattle With DeepAPS: A Two-Step Deep Learning Automated Phenotyping and Segmentation Approach

2020

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Assessing conformation features in an accurate and rapid manner remains a challenge in the dairy industry. While recent developments in computer vision has greatly improved automated background removal, these methods have not been fully translated to biological studies. Here, we present a composite method (DeepAPS) that combines two readily available algorithms in order to create a precise mask for an animal image. This method performs accurately when compared with manual classification of proportion of coat color with an adjusted R 2 = 0.926. Using the output mask, we are able to automatically extract useful phenotypic information for 14 additional morphological features. Using pedigree and image information from a web catalog (www.semex.com), we estimated high heritabilities (ranging from h 2 = 0.18-0.82), indicating that meaningful biological information has been extracted automatically from imaging data. This method can be applied to other datasets and requires only a minimal number of image annotations (∼50) to train this partially supervised machinelearning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https://github.com/lauzingaretti/deepaps.

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Section: Discussionmentioning

confidence: 99%