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://github.com/lauzingaretti/deepaps.
Highlights
Breeding programs depend on large-scale, accurate phenotyping, which is critical for genomic dissection of complex traits
When we used the supervised algorithm Mask R-CNN and applied the mask to the input images (Figure 2B), we observed in all cases parts of the cow body were removed along with the background
Phenomics is extremely important in breeding programs in particular, as the desired outcome is a change in a phenotype
Summary
Breeding programs depend on large-scale, accurate phenotyping, which is critical for genomic dissection of complex traits. While the genome of an organism can be characterized, e.g., with high density genotyping arrays, the “phenome” is much more complex and can never be fully described, as it varies over time and changes with the environment (Houle et al, 2010). The cost of genotyping continues to drop, but there is still a need for improvements in obtaining high-performance phenotypes at a lower cost (Tardieu et al, 2017). The number of phenotypes recorded in traditional breeding schemes is relatively small, because its recording is expensive. Yearly milk yield is usually inferred by extrapolation using a few lactation measurements, whereas actual milk production can be measured individually and daily using automated milking robots.
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