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A 3D printed plant model for accurate and reliable 3D plant phenotyping.

Jonas BömerFelix EsserElias MarksRadu Alexandru RosuSven BehnkeLasse KlingbeilHeiner KuhlmannCyrill StachnissAnne-Katrin MahleinStefan Paulus
Published in: GigaScience (2024)
Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.
Keyphrases
  • machine learning
  • high throughput
  • deep learning
  • risk assessment
  • high resolution
  • electronic health record
  • mass spectrometry
  • single cell