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@ARTICLE{Mertens:904510,
      author       = {Mertens, Stien and Verbraeken, Lennart and Sprenger, Heike
                      and Demuynck, Kirin and Maleux, Katrien and Cannoot, Bernard
                      and De Block, Jolien and Maere, Steven and Nelissen, Hilde
                      and Bonaventure, Gustavo and Crafts-Brandner, Steven J. and
                      Vogel, Jonathan T. and Bruce, Wesley and Inzé, Dirk and
                      Wuyts, Nathalie},
      title        = {{P}roximal {H}yperspectral {I}maging {D}etects {D}iurnal
                      and {D}rought-{I}nduced {C}hanges in {M}aize {P}hysiology},
      journal      = {Frontiers in Functional Plant Ecology},
      volume       = {12},
      issn         = {1664-462X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {FZJ-2021-06080},
      pages        = {640914},
      year         = {2021},
      abstract     = {Hyperspectral imaging is a promising tool for
                      non-destructive phenotyping of plant physiological traits,
                      which has been transferred from remote to proximal sensing
                      applications, and from manual laboratory setups to automated
                      plant phenotyping platforms. Due to the higher resolution in
                      proximal sensing, illumination variation and plant geometry
                      result in increased non-biological variation in plant
                      spectra that may mask subtle biological differences. Here, a
                      better understanding of spectral measurements for proximal
                      sensing and their application to study drought,
                      developmental and diurnal responses was acquired in a
                      drought case study of maize grown in a greenhouse
                      phenotyping platform with a hyperspectral imaging setup. The
                      use of brightness classification to reduce the
                      illumination-induced non-biological variation is
                      demonstrated, and allowed the detection of diurnal,
                      developmental and early drought-induced changes in maize
                      reflectance and physiology. Diurnal changes in transpiration
                      rate and vapor pressure deficit were significantly
                      correlated with red and red-edge reflectance.
                      Drought-induced changes in effective quantum yield and water
                      potential were accurately predicted using partial least
                      squares regression and the newly developed Water Potential
                      Index 2, respectively. The prediction accuracy of
                      hyperspectral indices and partial least squares regression
                      were similar, as long as a strong relationship between the
                      physiological trait and reflectance was present. This
                      demonstrates that current hyperspectral processing
                      approaches can be used in automated plant phenotyping
                      platforms to monitor physiological traits with a high
                      temporal resolution.},
      cin          = {IBG-2},
      ddc          = {570},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2171 - Biological and environmental resources for
                      sustainable use (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2171},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33692820},
      UT           = {WOS:000626045100001},
      doi          = {10.3389/fpls.2021.640914},
      url          = {https://juser.fz-juelich.de/record/904510},
}