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@ARTICLE{Kierdorf:916363,
      author       = {Kierdorf, Jana and Junker-Frohn, Laura Verena and Delaney,
                      Mike and Olave, Mariele Donoso and Burkart, Andreas and
                      Jaenicke, Hannah and Muller, Onno and Rascher, Uwe and
                      Roscher, Ribana},
      title        = {{G}rowli{F}lower: {A}n image time‐series dataset for
                      {GROW}th analysis of cau{LIFLOWER}},
      journal      = {Journal of field robotics},
      volume       = {40},
      number       = {2},
      issn         = {1556-4959},
      address      = {New York, NY},
      publisher    = {Wiley},
      reportid     = {FZJ-2022-06164},
      pages        = {173-192},
      year         = {2023},
      abstract     = {In this paper, we present GrowliFlower, a georeferenced,
                      image-based unmanned aerial vehicle time-series dataset of
                      two monitored cauliflower fields (0.39 and 0.60 ha)
                      acquired in 2 years, 2020 and 2021. The proposed dataset
                      contains RGB and multispectral orthophotos with coordinates
                      of approximately 14,000 individual cauliflower plants. The
                      coordinates enable the extraction of complete and incomplete
                      time-series of image patches showing individual plants. The
                      dataset contains the collected phenotypic traits of 740
                      plants, including the developmental stage and plant and
                      cauliflower size. The harvestable product is completely
                      covered by leaves, thus, plant IDs and coordinates are
                      provided to extract image pairs of plants pre- and
                      post-defoliation. In addition, to facilitate classification,
                      detection, segmentation, instance segmentation, and other
                      similar computer vision tasks, the proposed dataset contains
                      pixel-accurate leaf and plant instance segmentations, as
                      well as stem annotations. The proposed dataset was created
                      to facilitate the development and evaluation of various
                      machine-learning approaches. It focuses on the analysis of
                      growth and development of cauliflower and the derivation of
                      phenotypic traits to advance automation in agriculture. Two
                      baseline results of instance segmentation tasks at the plant
                      and leaf level based on labeled instance segmentation data
                      are presented. The complete GrowliFlower dataset is publicly
                      available (http://rs.ipb.uni-bonn.de/data/growliflower/).},
      cin          = {IBG-2},
      ddc          = {620},
      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:000868525100001},
      doi          = {10.1002/rob.22122},
      url          = {https://juser.fz-juelich.de/record/916363},
}