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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, View ORCID ProfileJesse Poland
doi: https://doi.org/10.1101/527911
Xu Wang
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
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Hong Xuan
2Department of Computer Science, George Washington University, Washington D.C.
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Byron Evers
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
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Sandesh Shrestha
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
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Robert Pless
2Department of Computer Science, George Washington University, Washington D.C.
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  • For correspondence: pless@gwu.edu jpoland@ksu.edu
Jesse Poland
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
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  • ORCID record for Jesse Poland
  • For correspondence: pless@gwu.edu jpoland@ksu.edu
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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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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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