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

Frontiers in Genetics
Jessica NyeMiguel Pérez-Enciso

Abstract

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 machine-learning approach. DeepAPS allows for the rapid and accurate quantification of multiple phenotypic measurements while minimizing study cost. The pipeline is available at https:/...Continue Reading

References

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Citations

Nov 17, 2020·Frontiers in Veterinary Science·Arthur Francisco Araújo FernandesGuilherme Jordão de Magalhães Rosa
Mar 7, 2021·Genetics, Selection, Evolution : GSE·Miguel Pérez-Enciso, Juan P Steibel

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Methods Mentioned

BETA
dissection
genotyping
scraping

Software Mentioned

Mask R - CNN
DeepAPS
GIMP
Mask R CNN
R
scikit
Beautiful Soup
Mask
morphology
OpenCV

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