Estimating conformational traits in dairy cattle with deepAPS : A two-step deep learning automated phenotyping and segmentation approach
Nye, Jessica (Centre de Recerca en Agrigenòmica)
Zingaretti, Laura M. (Centre de Recerca en Agrigenòmica)
Perez-Enciso, Miguel (Centre de Recerca en Agrigenòmica)

Fecha:	2020
Resumen:	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 R2 = 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 h2 = 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.
Ayudas:	Agencia Estatal de Investigación AGL2016-78709-R Ministerio de Economía y Competitividad BFU2016-77236-P Ministerio de Economía y Competitividad SEV-2015-0533
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Image analysis ; Morphology ; Phenomics ; Image mask ; Deep learning ; Dairy cattle
Publicado en:	Frontiers in genetics, Vol. 11 (May 2020) , art. 513, ISSN 1664-8021

DOI: 10.3389/fgene.2020.00513
PMID: 32508888

9 p, 1.0 MB

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