% 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{Lobet:828812,
      author       = {Lobet, Guillaume and Koevoets, Iko T. and Noll, Manuel and
                      Meyer, Patrick E. and Tocquin, Pierre and Pagès, Loïc and
                      Périlleux, Claire},
      title        = {{U}sing a {S}tructural {R}oot {S}ystem {M}odel to
                      {E}valuate and {I}mprove the {A}ccuracy of {R}oot {I}mage
                      {A}nalysis {P}ipelines},
      journal      = {Frontiers in Functional Plant Ecology},
      volume       = {8},
      issn         = {1664-462X},
      address      = {Lausanne},
      publisher    = {Frontiers Media88991},
      reportid     = {FZJ-2017-02668},
      pages        = {447},
      year         = {2017},
      abstract     = {Root system analysis is a complex task, often performed
                      with fully automated image analysis pipelines. However, the
                      outcome is rarely verified by ground-truth data, which might
                      lead to underestimated biases. We have used a root model,
                      ArchiSimple, to create a large and diverse library of
                      ground-truth root system images (10,000). For each image,
                      three levels of noise were created. This library was used to
                      evaluate the accuracy and usefulness of several image
                      descriptors classically used in root image analysis
                      softwares. Our analysis highlighted that the accuracy of the
                      different traits is strongly dependent on the quality of the
                      images and the type, size, and complexity of the root
                      systems analyzed. Our study also demonstrated that machine
                      learning algorithms can be trained on a synthetic library to
                      improve the estimation of several root system traits.
                      Overall, our analysis is a call to caution when using
                      automatic root image analysis tools. If a thorough
                      calibration is not performed on the dataset of interest,
                      unexpected errors might arise, especially for large and
                      complex root images. To facilitate such calibration, both
                      the image library and the different codes used in the study
                      have been made available to the community.},
      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:000398172200001},
      pubmed       = {pmid:28421089},
      doi          = {10.3389/fpls.2017.00447},
      url          = {https://juser.fz-juelich.de/record/828812},
}