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@ARTICLE{Helmrich:1020312,
      author       = {Helmrich, Dirk Norbert and Bauer, Felix Maximilian and
                      Giraud, Mona and Schnepf, Andrea and Göbbert, Jens Henrik
                      and Scharr, Hanno and Hvannberg, Ebba Þora and Riedel,
                      Morris},
      title        = {{A} scalable pipeline to create synthetic datasets from
                      functional–structural plant models for deep learning},
      journal      = {In silico plants},
      volume       = {6},
      number       = {1},
      issn         = {2517-5025},
      address      = {[Oxford]},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2024-00054},
      pages        = {diad022},
      year         = {2024},
      abstract     = {In plant science, it is an established method to obtain
                      structural parameters of crops using image analysis. In
                      recent years, deep learning techniques have improved the
                      underlying processes significantly. However, since data
                      acquisition is time and resource consuming, reliable
                      training data are currently limited. To overcome this
                      bottleneck, synthetic data are a promising option for not
                      only enabling a higher order of correctness by offering more
                      training data but also for validation of results. However,
                      the creation of synthetic data is complex and requires
                      extensive knowledge in Computer Graphics, Visualization and
                      High-Performance Computing. We address this by introducing
                      Synavis, a framework that allows users to train networks on
                      real-time generated data. We created a pipeline that
                      integrates realistic plant structures, simulated by the
                      functional–structural plant model framework CPlantBox,
                      into the game engine Unreal Engine. For this purpose, we
                      needed to extend CPlantBox by introducing a new leaf
                      geometrization that results in realistic leafs. All
                      parameterized geometries of the plant are directly provided
                      by the plant model. In the Unreal Engine, it is possible to
                      alter the environment. WebRTC enables the streaming of the
                      final image composition, which, in turn, can then be
                      directly used to train deep neural networks to increase
                      parameter robustness, for further plant trait detection and
                      validation of original parameters. We enable user-friendly
                      ready-to-use pipelines, providing virtual plant experiment
                      and field visualizations, a python-binding library to access
                      synthetic data and a ready-to-run example to train models.},
      cin          = {JSC / IBG-3 / IAS-8},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBG-3-20101118 /
                      I:(DE-Juel1)IAS-8-20210421},
      pnm          = {2A3 - Remote Sensing (CARF - CCA) (POF4-2A3) / 5243 -
                      Information Processing in Distributed Systems (POF4-524) /
                      5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / EXC 2070:  PhenoRob -
                      Robotics and Phenotyping for Sustainable Crop Production
                      (390732324) / EUROCC-2 (DEA02266) / BMBF 01 1H1 6013, NRW
                      325 – 8.03 – 133340 - SiVeGCS (DB001492)},
      pid          = {G:(DE-HGF)POF4-2A3 / G:(DE-HGF)POF4-5243 /
                      G:(DE-HGF)POF4-5112 / G:(BMBF)390732324 /
                      G:(DE-Juel-1)DEA02266 / G:(DE-Juel-1)DB001492},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001145971900001},
      doi          = {10.1093/insilicoplants/diad022},
      url          = {https://juser.fz-juelich.de/record/1020312},
}