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@ARTICLE{Baker:1049169,
      author       = {Baker, Dirk N and Giraud, Mona and Göbbert, Jens Henrik
                      and Scharr, Hanno and Riedel, Morris and Hvannberg, Ebba
                      Þóra and Schnepf, Andrea},
      title        = {{V}irtual world coupling with photosynthesis evaluation for
                      synthetic data production},
      journal      = {In silico plants},
      volume       = {7},
      number       = {2},
      issn         = {2517-5025},
      address      = {[Oxford]},
      publisher    = {Oxford University Press},
      reportid     = {FZJ-2025-05252},
      pages        = {diaf018},
      year         = {2025},
      note         = {This work has partly been funded by the EUROCC2 project
                      funded by the European High-Performance Computing Joint
                      Undertaking (JU) and EU/EEA states under grant agreement No
                      101101903. This work has partly been funded by the German
                      Research Foundation under Germany’s Excellence Strategy,
                      EXC-2070 - 390732324 - PhenoRob and by the German Federal
                      Ministry of Education and Research (BMBF) in the framework
                      of the funding initiative ‘Plant roots and soil
                      ecosystems, significance of the rhizosphere for the
                      bio-economy’ (Rhizo4Bio), subproject CROP (ref. FKZ
                      031B0909A). The authors would like to acknowledge funding
                      provided by the BMBF to the Gauss Centre for Supercomputing
                      via the InHPC-DE project (01–H17001).},
      abstract     = {In this work, we couple the functional–structural plant
                      model CPlantBox to the Unreal Engine by exploiting the
                      implemented raytracing pipeline to evaluate light influx on
                      the plant surface. There are many approaches for
                      photosynthesis computation and light evaluation, though they
                      typically are limited by versatility, compute speed, or
                      operate on much coarser resolutions. This work specifically
                      addresses the concern that data generation pipelines tend to
                      be unresponsive and do not include model-based knowledge as
                      part of the generation pipeline. Using established
                      photosynthesis solvers, we model the interaction between the
                      Unreal Engine and the FSPM to measure physical properties in
                      the virtual world. This is successful if we are able to
                      reproduce experimental results using an in silico model. As
                      part of the pipeline, we generate a surface geometry and
                      utilize material shaders that are designed to establish a
                      baseline surface model for light interception and
                      transmission, based on simple parameter sets that can be
                      calibrated. Using a Selhausen field experiment as baseline,
                      we reproduce the photosynthesis effectiveness of the plants
                      in the 2016 winter wheat experiments. Our pipeline is deeply
                      intertwined with data generation and has been proven to
                      perform well at scale. In this work, we build on our
                      previous work by showcasing both a simulation study of a
                      light evaluation as well as quantifying how well our system
                      performs on high-performance computing systems.},
      cin          = {IBG-3},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / Rhizo4Bio (Phase 1): RhizoWheat -
                      Rhizosphärenprozesse und Ertragsdepressionen in
                      Weizenfruchtfolgen, TP B (031B0910B) / DFG project
                      G:(GEPRIS)390732324 - EXC 2070: PhenoRob - Robotik und
                      Phänotypisierung für Nachhaltige Nutzpflanzenproduktion
                      (390732324)},
      pid          = {G:(DE-HGF)POF4-2173 / G:(BMBF)031B0910B /
                      G:(GEPRIS)390732324},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1093/insilicoplants/diaf018},
      url          = {https://juser.fz-juelich.de/record/1049169},
}