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@ARTICLE{Scharr:817744,
      author       = {Scharr, Hanno and Dee, Hannah and French, Andrew P. and
                      Tsaftaris, Sotirios A.},
      title        = {{S}pecial issue on computer vision and image analysis in
                      plant phenotyping},
      journal      = {Machine vision and applications},
      volume       = {27},
      number       = {5},
      issn         = {1432-1769},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {FZJ-2016-04387},
      pages        = {607 - 609},
      year         = {2016},
      note         = {Editorial of the special issue},
      abstract     = {Plant phenotyping is the identification of effects on the
                      phenotype (i.e., the plant appearance and behavior) as a
                      result of genotype differences (i.e., differences in the
                      genetic code) and the environment. Previously, the process
                      of taking phenotypic measurements has been laborious,
                      costly, and time-consuming. In recent years, noninvasive,
                      imaging-based methods have become more common. These images
                      are recorded by a range of capture devices from small
                      embedded camera systems to multi-million Euro smart
                      greenhouses, at scales ranging from microscopic images of
                      cells, to entire fields captured by UAV imaging.},
      cin          = {IBG-2},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000379708400001},
      doi          = {10.1007/s00138-016-0787-1},
      url          = {https://juser.fz-juelich.de/record/817744},
}