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@INPROCEEDINGS{Kierdorf:916837,
      author       = {Kierdorf, Jana and Junker-Frohn, Laura and Delaney, Mike
                      and Donoso Olave, Mariele and Burkart, Andreas and Jaenicke,
                      Hannah and Muller, Onno and Rascher, Uwe and Roscher,
                      Ribana},
      title        = {{G}rowliflower: {A}n image time series dataset for growth
                      analysis of cauliflower},
      school       = {University Bonn},
      reportid     = {FZJ-2023-00134},
      year         = {2022},
      abstract     = {In our video, we present our benchmark dataset
                      GrowliFlower. It contains weekly, georeferenced UAV captured
                      image time series of two fields sized 0.39 to 0.60 ha for
                      one growing period of cauliflower in 2020 and 2021 each. We
                      extract and provide image time series for thousands of
                      individual plants and collect in-situ reference data in the
                      field. The reference data contain phenotypic traits such as
                      phenological development, diameter, height, head size and
                      more. Additionally, we have defoliated cauliflower heads and
                      capture image data before and after defoliation.
                      Furthermore, we provide pixel-precise leaf and plant
                      instance segmentation as well as stem annotations. Our
                      benchmark is used to develop and evaluate machine learning
                      models for instance for classification, detection, semantic
                      segmentation, instance segmentation or stem detection tasks,
                      but also for time series analysis. An example for the
                      application of our benchmark can be the analysis of plant
                      development and the determination of the harvest time of
                      cauliflower. The entire dataset will be published and
                      publicly accessible.},
      month         = {Mar},
      date          = {2022-03-28},
      organization  = {digicrop 2022, online (Germany), 28
                       Mar 2022 - 30 Mar 2022},
      subtyp        = {After Call},
      cin          = {IBG-2},
      cid          = {I:(DE-Juel1)IBG-2-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/916837},
}