Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions
Cite this dataset:
Ferguson, John; Fernandes, Samuel; Monier, Brandon; Miller, Nathan; Allen, Dylan; Dmitrieva, Anna; Schmuker, Peter; Lozano, Roberto; Valluru, Ravi; Buckler, Edward; Gore, Michael; Brown, Patrick; Spalding, Edgar; Leakey, Andrew (2021): Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-5565022_V2
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Dataset Description

This dataset contains the images of a photoperiod sensitive sorghum accession population used for a GWAS/TWAS study of leaf traits related to water use efficiency in 2016 and 2017.
*Note: new in this second version is that JPG images outputted from the nms files were added

Accessions_2016.zip and Accessions_2017.zip: contain raw images produced by Optical Topometer (nms files) for all sorghum accessions. Images can be opened with Nanofocus μsurf analysis extended software (Oberhausen,Germany).
Accessions_2016_jpg.zip and Accessions_2017_jpg.zip: contain jpg images outputted from the nms files and used in the machine learning phenotyping.

Subject Life Sciences
Keywords stomata; segmentation; water use efficiency
License CC BY
Corresponding Creator Andrew Leakey
Downloaded 168 times
Version DOI Comment Publication Date
2 10.13012/B2IDB-5565022_V2 Added JPG images outputted from the nms files 2021-08-12
1 10.13012/B2IDB-5565022_V1 2021-07-12
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