Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies
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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 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.
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Now published in GigaScience doi: 10.1093/gigascience/gix084
Jonathan A. Atkinson 1Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, United KingdomFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Jonathan A. AtkinsonGuillaume Lobet 2Agrosphere, IBG3, Forschungszentrum Jülich, Jülich, Germany3Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, BelgiumFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Guillaume LobetManuel Noll 4InBios, Université de Liège, Liège, BelgiumFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePatrick E. Meyer 4InBios, Université de Liège, Liège, BelgiumFind this author on Google …
Now published in GigaScience doi: 10.1093/gigascience/gix084
Jonathan A. Atkinson 1Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, United KingdomFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Jonathan A. AtkinsonGuillaume Lobet 2Agrosphere, IBG3, Forschungszentrum Jülich, Jülich, Germany3Earth and Life Institute, Université catholique de Louvain, Louvain-la-Neuve, BelgiumFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Guillaume LobetManuel Noll 4InBios, Université de Liège, Liège, BelgiumFind this author on Google ScholarFind this author on PubMedSearch for this author on this sitePatrick E. Meyer 4InBios, Université de Liège, Liège, BelgiumFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteMarcus Griffiths 1Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, United KingdomFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Marcus GriffithsDarren M. Wells 1Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, United KingdomFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Darren M. Wells
A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/gix084 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.
These peer reviews were as follows:
Reviewer 1: http://dx.doi.org/10.5524/REVIEW.100810 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.100811
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