Skip to main content

Research Repository

Advanced Search

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

Atkinson, Jonathan A.; Lobet, Guillaume; Noll, Manuel; Meyer, Patrick E.; Griffiths, Marcus; Wells, Darren M.


Future Food Beacon:Technologist in Phenomics

Guillaume Lobet

Manuel Noll

Patrick E. Meyer

Marcus Griffiths

Principal Research Fellow


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 previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of 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 areas of plant phenotyping.


Atkinson, J. A., Lobet, G., Noll, M., Meyer, P. E., Griffiths, M., & Wells, D. M. (2017). Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. GigaScience, 6(10),

Journal Article Type Article
Acceptance Date Aug 16, 2017
Online Publication Date Aug 23, 2017
Publication Date Oct 1, 2017
Deposit Date Oct 11, 2017
Publicly Available Date Oct 11, 2017
Journal GigaScience
Electronic ISSN 2047-217X
Publisher Oxford University Press
Peer Reviewed Peer Reviewed
Volume 6
Issue 10
Public URL
Publisher URL
Copyright Statement Copyright information regarding this work can be found at the following address:


You might also like

Downloadable Citations