Journal Article

FZJ-2025-05252

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Virtual world coupling with photosynthesis evaluation for synthetic data production

Baker, D. N. (Corresponding author) ; Giraud, M.FZJ* ; Göbbert, J. H.FZJ* ; Scharr, H.FZJ* ; Riedel, M.FZJ* ; Hvannberg, E. Þ. ; Schnepf, A.FZJ*

2025
Oxford University Press [Oxford]

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Please use a persistent id in citations: doi:10.1093/insilicoplants/diaf018  doi:10.34734/FZJ-2025-05252

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.

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).

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Contributing Institute(s):

1. Agrosphäre (IBG-3)

Research Program(s):

1. 2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217) (POF4-217)
2. Rhizo4Bio (Phase 1): RhizoWheat - Rhizosphärenprozesse und Ertragsdepressionen in Weizenfruchtfolgen, TP B (031B0910B) (031B0910B)
3. DFG project G:(GEPRIS)390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion (390732324) (390732324)

Appears in the scientific report 2025

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