RT Journal Article
SR Electronic
T1 Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 152702
DO 10.1101/152702
A1 Jonathan A. Atkinson
A1 Guillaume Lobet
A1 Manuel Noll
A1 Patrick E. Meyer
A1 Marcus Griffiths
A1 Darren M. Wells
YR 2017
UL http://biorxiv.org/content/early/2017/06/20/152702.abstract
AB Background Genetic analyses of plant root system development 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).Findings 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 Quantitative Trait Loci that had previously been discovered using a semi-automated method.Conclusions We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.