% 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{Mahlein:873143,
      author       = {Mahlein, Anne-Katrin and Kuska, Matheus Thomas and Thomas,
                      Stefan and Wahabzada, Mirwaes and Behmann, Jan and Rascher,
                      Uwe and Kersting, Kristian},
      title        = {{Q}uantitative and qualitative phenotyping of disease
                      resistance of crops by hyperspectral sensors: seamless
                      interlocking of phytopathology, sensors, and machine
                      learning is needed!},
      journal      = {Current opinion in plant biology},
      volume       = {50},
      issn         = {1369-5266},
      address      = {London},
      publisher    = {Current Biology Ltd.},
      reportid     = {FZJ-2020-00588},
      pages        = {156 - 162},
      year         = {2019},
      abstract     = {Determination and characterization of resistance reactions
                      of crops against fungal pathogens are essential to select
                      resistant genotypes. In plant breeding, phenotyping of
                      genotypes is realized by time consuming and expensive visual
                      plant ratings. During resistance reactions and during
                      pathogenesis plants initiate different structural and
                      biochemical defence mechanisms, which partly affect the
                      optical properties of plant organs. Recently, intensive
                      research has been conducted to develop innovative optical
                      methods for an assessment of compatible and incompatible
                      plant pathogen interaction. These approaches, combining
                      classical phytopathology or microbiology with technology
                      driven methods — such as sensors, robotics, machine
                      learning, and artificial intelligence — are summarized by
                      the term digital phenotyping. In contrast to common visual
                      rating, detection and assessment methods, optical sensors in
                      combination with advanced data analysis methods are able to
                      retrieve pathogen induced changes in the physiology of
                      susceptible or resistant plants non-invasively and
                      objectively. Phenotyping disease resistance aims different
                      tasks. In an early breeding step, a qualitative assessment
                      and characterization of specific resistance action is aimed
                      to link it, for example, to a genetic marker. Later, during
                      greenhouse and field screening, the assessment of the level
                      of susceptibility of different genotypes is relevant. Within
                      this review, recent advances of digital phenotyping
                      technologies for the detection of subtle resistance
                      reactions and resistance breeding are highlighted and
                      methodological requirements are critically discussed},
      cin          = {IBG-2},
      ddc          = {580},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {582 - Plant Science (POF3-582)},
      pid          = {G:(DE-HGF)POF3-582},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31387067},
      UT           = {WOS:000486357700019},
      doi          = {10.1016/j.pbi.2019.06.007},
      url          = {https://juser.fz-juelich.de/record/873143},
}