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Abstract
Background: Plant breeding and crop research rely on experimental phenotyping trials. These trials generate data for large numbers of traits and plant varieties that needs to be captured efficiently and accurately to support further research and downstream analysis. Traditionally scored by hand, phenotypic data is nowadays collected using spreadsheets or specialized apps. While many solutions exist, which increase efficiency and reduce errors, none offer the same familiarity as printed field plans which have been used for decades and offer an intuitive overview over the trial setup, previously recorded data and plots still requiring scoring.
Results: We introduce GridScore which utilizes cutting-edge web technologies to reproduce the familiarity of printed field plans while enhancing the phenotypic data collection process by adding advanced features like georeferencing, image tagging and speech recognition. GridScore is a cross-platform open-source plant phenotyping app that combines barcode-based systems with a guided data collection approach while offering a top-down view onto the data collected in a field layout. GridScore is compared to existing tools across a wide spectrum of criteria including support for barcodes, multiple platforms, and visualizations.
Conclusion: Compared to its competition, GridScore shows strong performance across the board offering a complete manual phenotyping experience.
Original language | English |
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Article number | 214 |
Number of pages | 8 |
Journal | BMC Bioinformatics |
Volume | 23 |
DOIs | |
Publication status | Published - 6 Jun 2022 |
Keywords
- Crops, Agricultural
- Data Collection
- Phenotype
- Plant Breeding
- Phenotyping
- Data-collection
- Plant-breeding
- Data-transfer
- Bioinformatics
- Data-visualization
- Plant-genetics
ASJC Scopus subject areas
- Applied Mathematics
- Molecular Biology
- Structural Biology
- Biochemistry
- Computer Science Applications
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Dive into the research topics of 'GridScore: a tool for accurate, cross-platform phenotypic data collection and visualization'. Together they form a unique fingerprint.Projects
- 1 Finished
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16 ERA-CAPS Barley Yield Associated Networks (Joint with IPK Gatersleben as lead and University of Minnesota)
Waugh, R. (Investigator)
Biotechnology and Biological Sciences Research Council
13/08/18 → 30/06/22
Project: Research