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@ARTICLE{Wilhelm:910586,
      author       = {Wilhelm, Jens and Wojciechowski, Tobias and Postma,
                      Johannes A. and Jollet, Dirk and Heinz, Kathrin and Boeckem,
                      Vera and Müller-Linow, Mark},
      othercontributors = {Putz, Alexander},
      title        = {{A}ssessing the {S}torage {R}oot {D}evelopment of {C}assava
                      with a {N}ew {A}nalysis {T}ool},
      journal      = {Plant phenomics},
      volume       = {2022},
      issn         = {2643-6515},
      address      = {Washington, D.C.},
      publisher    = {American Association for the Advancement of Science},
      reportid     = {FZJ-2022-03964},
      pages        = {1 - 15},
      year         = {2022},
      abstract     = {Storage roots of cassava plants crops are one of the main
                      providers of starch in many South American, African, and
                      Asian countries. Finding varieties with high yields is
                      crucial for growing and breeding. This requires a better
                      understanding of the dynamics of storage root formation,
                      which is usually done by repeated manual evaluation of root
                      types, diameters, and their distribution in excavated roots.
                      We introduce a newly developed method that is capable to
                      analyze the distribution of root diameters automatically,
                      even if root systems display strong variations in root
                      widths and clustering in high numbers. An application study
                      was conducted with cassava roots imaged in a video
                      acquisition box. The root diameter distribution was
                      quantified automatically using an iterative ridge detection
                      approach, which can cope with a wide span of root diameters
                      and clustering. The approach was validated with virtual root
                      models of known geometries and then tested with a
                      time-series of excavated root systems. Based on the
                      retrieved diameter classes, we show plausibly that the
                      dynamics of root type formation can be monitored
                      qualitatively and quantitatively. We conclude that this new
                      method reliably determines important phenotypic traits from
                      storage root crop images. The method is fast and robustly
                      analyses complex root systems and thereby applicable in
                      high-throughput phenotyping and future breeding.},
      cin          = {IBG-2},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217) / Bioökonomie International
                      2015: CASSAVASTORe - Genetische und phänotypische Analysen
                      zur Verbesserung der Speicherwurzelentwicklung von Maniok
                      (031B0070)},
      pid          = {G:(DE-HGF)POF4-2171 / G:(BMBF)031B0070},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000876236000002},
      doi          = {10.34133/2022/9767820},
      url          = {https://juser.fz-juelich.de/record/910586},
}