% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Atkinson:840324,
      author       = {Atkinson, Jonathan A. and Lobet, Guillaume and Noll, Manuel
                      and Meyer, Patrick E. and Griffiths, Marcus and Wells,
                      Darren M.},
      title        = {{C}ombining semi-automated image analysis techniques with
                      machine learning algorithms to accelerate large-scale
                      genetic studies},
      journal      = {GigaScience},
      volume       = {6},
      number       = {10},
      issn         = {2047-217X},
      address      = {London},
      publisher    = {Biomed Central},
      reportid     = {FZJ-2017-07866},
      pages        = {1 - 7},
      year         = {2017},
      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.},
      cin          = {IBG-3},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {255 - Terrestrial Systems: From Observation to Prediction
                      (POF3-255)},
      pid          = {G:(DE-HGF)POF3-255},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000412397500009},
      pubmed       = {pmid:29020748},
      doi          = {10.1093/gigascience/gix084},
      url          = {https://juser.fz-juelich.de/record/840324},
}