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@ARTICLE{Aksoy:186416,
      author       = {Aksoy, Eren Erdal and Abramov, Alexey and Wörgötter,
                      Florentin and Scharr, Hanno and Fischbach, Andreas and
                      Dellen, Babette},
      title        = {{M}odeling leaf growth of rosette plants using infrared
                      stereo image sequences},
      journal      = {Computers and electronics in agriculture},
      volume       = {110},
      issn         = {0168-1699},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2015-00492},
      pages        = {78 - 90},
      year         = {2015},
      abstract     = {In this paper, we present a novel multi-level procedure for
                      finding and tracking leaves of a rosette plant, in our case
                      up to 3 weeks old tobacco plants, during early growth from
                      infrared-image sequences. This allows measuring important
                      plant parameters, e.g. leaf growth rates, in an automatic
                      and non-invasive manner. The procedure consists of three
                      main stages: preprocessing, leaf segmentation, and leaf
                      tracking. Leaf-shape models are applied to improve leaf
                      segmentation, and further used for measuring leaf sizes and
                      handling occlusions. Leaves typically grow radially away
                      from the stem, a property that is exploited in our method,
                      reducing the dimensionality of the tracking task. We
                      successfully tested the method on infrared image sequences
                      showing the growth of tobacco-plant seedlings up to an age
                      of about 30 days, which allows measuring relevant plant
                      growth parameters such as leaf growth rate. By robustly
                      fitting a suitably modified autocatalytic growth model to
                      all growth curves from plants under the same treatment,
                      average plant growth models could be derived. Future
                      applications of the method include plant-growth monitoring
                      for optimizing plant production in green houses or plant
                      phenotyping for plant research.},
      cin          = {IBG-2},
      ddc          = {620},
      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:000349881000009},
      doi          = {10.1016/j.compag.2014.10.020},
      url          = {https://juser.fz-juelich.de/record/186416},
}