Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

ABSTRACT: Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previous

SUBMITTER: Atkinson JA 

PROVIDER: S-EPMC5632292 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature