FIGS_ICDW_Scab_2017
The FIGSICDWScab2017 FIGS set was constructed using evaluation data from more than 5590 durum wheat accessions and machine learning algorithms including Random Forest, support vector machine and kernel nearest neighbors, and using daily climatic data aligned to onset to capture durum wheat physiological stage. The metrics from the machine learning were medium to high (Accuracy = 0.75 and Kappa = 0.35). 210 accessions were then selected based on higher predictive probability. FIGSICDWScab2017 is the name of the FIGS subset including the crop, trait name and the year of construction.
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Language: | English |
| Published: |
MELDATA
2017
|
| Subjects: | Agricultural Sciences, wheat, fusarium, goal 1 no poverty, goal 2 zero hunger, |
| Online Access: | https://hdl.handle.net/20.500.11766.1/GYJLXC |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The FIGSICDWScab2017 FIGS set was constructed using evaluation data from more than 5590 durum wheat accessions and machine learning algorithms including Random Forest, support vector machine and kernel nearest neighbors, and using daily climatic data aligned to onset to capture durum wheat physiological stage. The metrics from the machine learning were medium to high (Accuracy = 0.75 and Kappa = 0.35). 210 accessions were then selected based on higher predictive probability. FIGSICDWScab2017 is the name of the FIGS subset including the crop, trait name and the year of construction. |
|---|