New Results
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
Hong Xuan
2Department of Computer Science, George Washington University, Washington D.C.
Byron Evers
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
Sandesh Shrestha
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
Robert Pless
2Department of Computer Science, George Washington University, Washington D.C.
Jesse Poland
1Department of Plant Pathology, Kansas State University, Manhattan, KS 66506
Posted January 23, 2019.
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
Subject Area
Subject Areas
- Biochemistry (11740)
- Bioengineering (8750)
- Bioinformatics (29189)
- Biophysics (14967)
- Cancer Biology (12093)
- Cell Biology (17410)
- Clinical Trials (138)
- Developmental Biology (9420)
- Ecology (14178)
- Epidemiology (2067)
- Evolutionary Biology (18301)
- Genetics (12239)
- Genomics (16797)
- Immunology (11865)
- Microbiology (28070)
- Molecular Biology (11583)
- Neuroscience (60953)
- Paleontology (451)
- Pathology (1870)
- Pharmacology and Toxicology (3238)
- Physiology (4957)
- Plant Biology (10425)
- Synthetic Biology (2884)
- Systems Biology (7338)
- Zoology (1651)