Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

  • John Ferguson (Creator)
  • Samuel Fernandes (Creator)
  • Brandon Monier (Creator)
  • Nathan Miller (Creator)
  • Dylan Allen (Creator)
  • Anna Dmitrieva (Creator)
  • Peter Schmuker (Creator)
  • Roberto Lozano (Creator)
  • Ravi Valluru (Creator)
  • Edward Buckler (Creator)
  • Michael Gore (Creator)
  • Patrick Brown (Creator)
  • Edgar Spalding (Creator)
  • Andrew Leakey (Creator)



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.
*<b>Note:</b> new in this second version is that JPG images outputted from the nms files were added

<b></b> and <b></b>: contain raw images produced by Optical Topometer (nms files) for all sorghum accessions. Images can be opened with Nanofocus ╬╝surf analysis extended software (Oberhausen,Germany).
<b></b> and <b></b>: contain jpg images outputted from the nms files and used in the machine learning phenotyping.
Date made availableAug 12 2021
PublisherUniversity of Illinois Urbana-Champaign


  • water use efficiency
  • segmentation
  • stomata

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