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High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat

Xu Wang, Hong Xuan, Byron Evers, Sandesh Shrestha, Robert Pless, Jesse Poland
doi: https://doi.org/10.1101/527911

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doi 
https://doi.org/10.1101/527911
History 
  • January 23, 2019.
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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.

Author Information

  1. Xu Wang1,a,
  2. Hong Xuan2,a,
  3. Byron Evers1,
  4. Sandesh Shrestha1,
  5. Robert Pless2,* and
  6. Jesse Poland1,a,*
  1. 1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
  2. 2Department of Computer Science, George Washington University, Washington D.C.
  1. ↵*Corresponding Authors: Robert Pless (pless{at}gwu.edu), Jesse Poland (jpoland{at}ksu.edu)

  1. ↵a These authors contributed equally to this work

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Posted January 23, 2019.
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High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat
Xu Wang, Hong Xuan, Byron Evers, Sandesh Shrestha, Robert Pless, Jesse Poland
bioRxiv 527911; doi: https://doi.org/10.1101/527911
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