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Related Experiment Video

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Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems
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Modeling Crop Genetic Resources Phenotyping Information Systems.

Christoph U Germeier1, Stefan Unger2

  • 1Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute, Federal Research Centre for Cultivated Plants, Quedlinburg, Germany.

Frontiers in Plant Science
|July 9, 2019
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Summary

This study presents comprehensive data and object models for multi-site field phenotyping, improving access to crucial plant genetic resources data. The developed models enhance data acquisition and usability for researchers in breeding and biodiversity.

Keywords:
class modelsdocumentationentity relationship modelsphenotypingplant genetic resourcesweb applicationswork flows

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Area of Science:

  • Agricultural Science
  • Bioinformatics
  • Genetics

Background:

  • Phenotype data documentation is critical for biodiversity, crop modeling, breeding, ecology, and evolution research.
  • Limited access to phenotype information hinders the effective utilization of plant genetic resources.
  • Phenotype data complexity necessitates contextual information for accurate interpretation and analysis.

Purpose of the Study:

  • To describe comprehensive data and object models for multi-site field phenotyping and data acquisition.
  • To develop blueprints for phenotyping information systems, enhancing usability for domain experts.
  • To facilitate statistical analysis, meta-analysis, and knowledge discovery through standardized data models.

Main Methods:

  • Utilized an object-oriented modeling approach, aligning data and object models with domain concepts.
  • Developed web interfaces for multi-site field phenotyping and data acquisition within the European Cooperative Programme for Plant Genetic Resources.
  • Focused on modeling field experiments, including randomized plots, and linking phenotype observations to metadata.

Main Results:

  • Created comprehensive data and object models supporting web interfaces for phenotyping data acquisition.
  • Ensured models are understandable and usable for domain experts, promoting modular software artifact sharing.
  • Established clear data types enabling better identification of links to other information systems, such as geographic information systems.

Conclusions:

  • The developed models serve as blueprints for robust phenotyping information systems.
  • Enhanced data accessibility and usability of plant genetic resources for research and breeding.
  • Object-oriented modeling facilitates better database and software design, bridging the gap between domain experts and IT specialists.