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@PHDTHESIS{Selzner:1038284,
      author       = {Selzner, Tobias},
      title        = {3{D} {R}econstruction of {P}lant {R}oots from {MRI}
                      {I}mages to {A}dvance {R}oot-{S}oil {S}ystems {M}odelling},
      school       = {University of Bonn},
      type         = {Dissertation},
      reportid     = {FZJ-2025-01295},
      pages        = {156 p.},
      year         = {2024},
      note         = {Dissertation, University of Bonn, 2024},
      abstract     = {Abstract: Roots are of particular interest for the
                      efficient use of nutrients and water by plants. Therefore,
                      the optimization of root system architecture (RSA) offers
                      large potential in finding more sustainable agricultural
                      practices. Magnetic resonance imaging (MRI) is one of the
                      few phenotyping methods that allows us to observe the 3D RSA
                      in opaque soil. Such volumetric data are essential to
                      investigate favorable RSA traits with functional-structural
                      root architecture models (FSRMs). However, the processing of
                      MRI images and their integration into FSRMs is challenging
                      and limits the use of the data to this day. In this work, we
                      investigated how MRI images of plant roots and related
                      experimental data can be processed more efficiently, and how
                      their meaningful use in FSRMs can be optimized.To alleviate
                      the bottleneck in MRI image processing, we deployed a novel
                      approach for automated root system reconstruction. The
                      approach combines a semantic segmentation of raw MRI images
                      into roots and soil with a root reconstruction algorithm. We
                      evaluated the results by comparing them with
                      state-of-the-art manual expert reconstructions. In the next
                      step, we investigated if the current soil process
                      descriptions in FSRMs are adequate to derive realistic root
                      water uptake (RWU) predictions for RSAs derived from MRI
                      images. We performed a soil grid convergence study of our
                      default modelling approach in CPlantBox and implemented an
                      alternative approach for RWU calculation. The results were
                      evaluated by comparing them to a numerical reference
                      solution. Finally, we explored new methods for the virtual
                      replication of MRI experiments in FSRMs. We devised a novel
                      parameterization method for mimicking root growth based on
                      MRI time series. By combining the measured root growth with
                      additional experimental data, we performed a virtual
                      repetition of an MRI experiment.We observed that the U-Net
                      segmentation improved reconstruction performance in manual
                      and automated workflows of root system reconstruction and
                      allowed us to process MRI images more efficiently.
                      Furthermore, the segmentation enabled the application of the
                      automated reconstruction algorithm for MRI images with a low
                      contrast-to-noise ratio. The soil grid convergency study
                      highlighted that root system scale models are not able to
                      spatially resolve the steep soil water potential gradients
                      near plant roots during water uptake. This resulted in large
                      errors in simulated RWU for dry soil conditions. The
                      implemented alternative approach for RWU calculation showed
                      the best agreement with the reference solution, while the
                      computational cost was kept low. Mimicking root growth based
                      on MRI time-series data with the novel parameterization
                      method allowed us to derive time-dependent root system
                      metrics and to create a functional representation of growing
                      root systems. By combining this functional representation of
                      growing root systems with additional experimental data, we
                      have created a parameterization framework that allows a
                      data-driven replication of the observed RWU in CPlantBox.We
                      were able to improve several aspects of the 3D
                      reconstruction of plant roots from MRI images and their
                      integration into root-soil-system models. The improvements
                      to manual and automated workflows for RSA reconstruction
                      will facilitate the parameterization of RSA submodels with
                      MRI data. In addition, the ability to derive RSAs from low
                      CNR images broadens the general scope of MRI experiments.
                      The grid convergence study raised awareness for errors
                      related to current RWU modelling paradigms under drought
                      conditions. Using the alternative approach for RWU
                      calculation makes it possible to bring the level of detail
                      of FSRMs closer to that of MRI-based RSAs. The novel
                      parameterization method for virtual replication of MRI
                      experiments facilitates the parameterization of RSA
                      submodels based on time-dependent root system metrics.
                      Furthermore, the parameterization method refines our ability
                      to validate the mechanisms and assumptions underlying RWU in
                      FSRMs.},
      cin          = {IBG-3},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {2173 - Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217)},
      pid          = {G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:hbz:5-79736},
      url          = {https://juser.fz-juelich.de/record/1038284},
}