2017 ICARDA Barley Net Form and Spot Blotch FIGS Subset

The FIGSICBNetFormSpotBlotch2017 set was constructed using evaluation data from more than 875 Barley accessions (http://dx.doi.org/10.1094/PHYTO-04-14-0107-R) 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 = 0.70 and Kappa = 0.25). 200 accessions were then selected based on higher predictive probability. FIGS_ICB_NetFormSpotBlotch_2017 is the name of the FIGS subset including the crop, trait name and the year of construction.

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Bibliographic Details
Main Authors: Kehel, Zakaria, Street, Kenneth, Amri, Ahmed
Other Authors: Francesco Bonechi (International Center for Agricultural Research in the Dry Areas - ICARDA)
Language:English
Published: MELDATA 2017
Subjects:Agricultural Sciences, barley, spotblotch, goal 2 zero hunger,
Online Access:https://hdl.handle.net/20.500.11766.1/J24MBP
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Description
Summary:The FIGSICBNetFormSpotBlotch2017 set was constructed using evaluation data from more than 875 Barley accessions (http://dx.doi.org/10.1094/PHYTO-04-14-0107-R) 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 = 0.70 and Kappa = 0.25). 200 accessions were then selected based on higher predictive probability. FIGS_ICB_NetFormSpotBlotch_2017 is the name of the FIGS subset including the crop, trait name and the year of construction.