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@ARTICLE{Drees:904496,
      author       = {Drees, Lukas and Junker-Frohn, Laura Verena and Kierdorf,
                      Jana and Roscher, Ribana},
      title        = {{T}emporal prediction and evaluation of {B}rassica growth
                      in the field using conditional generative adversarial
                      networks},
      journal      = {Computers and electronics in agriculture},
      volume       = {190},
      issn         = {0168-1699},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-06066},
      pages        = {106415 -},
      year         = {2021},
      abstract     = {Farmers frequently assess plant growth and performance as
                      basis for making decisions when to take action in the field,
                      such as fertilization, weed control, or harvesting. The
                      prediction of plant growth is a major challenge, as it is
                      affected by numerous and highly variable environmental
                      factors. This paper proposes a novel monitoring approach
                      that comprises high-throughput imaging sensor measurements
                      and their automatic analysis to predict future plant growth.
                      Our approach’s core is a novel machine learning-based
                      generative growth model based on conditional generative
                      adversarial networks, which is able to predict the future
                      appearance of individual plants. In experiments with RGB
                      time series images of laboratory-grown Arabidopsis thaliana
                      and field-grown cauliflower plants, we show that our
                      approach produces realistic, reliable, and reasonable images
                      of future growth stages. The automatic interpretation of the
                      generated images through neural network-based instance
                      segmentation allows the derivation of various phenotypic
                      traits that describe plant growth.},
      cin          = {IBG-2},
      ddc          = {004},
      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},
      UT           = {WOS:000702814600002},
      doi          = {10.1016/j.compag.2021.106415},
      url          = {https://juser.fz-juelich.de/record/904496},
}