Observationmodel-written summary

Support vector regression is generally suitable for deriving continuous wheat disease severity values, but uncertainty in the reference severity data is the crucial point.

A claim from Derivation of stress severities in wheat from hyperspectral data using support vector regression · 2010

Observationauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Support vector regression is generally suitable for deriving continuous wheat disease severity values, but uncertainty in the reference severity data is the crucial point.

Outcome
suitable for the derivation of continual disease severity valuese5
Qualifier
the crucial point is the uncertainty in the reference severity datae5

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValue

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Triticum aestivum
Triticum aestivum · TaxonRasbak · CC BY-SA 3.0 · Wikimedia Commons ↗

Verbatim

e2

“to derive the disease severity caused by leaf rust (Puccinina recondita) in wheat”

✓ located verbatim in the source · abstract, characters 815–896
e5

“The results show that the SVR is generally suitable for the derivation of continual disease severity values, but the crucial point is the uncertainty in the reference severity data, which is used to train the regression”

✓ located verbatim in the source · abstract, characters 1139–1358

Title

Derivation of stress severities in wheat from hyperspectral data using support vector regression

T Mewes; B Waske; J Franke; et al. 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing 2010. doi:10.1109/whispers.2010.5594921

Other claims from this paper (2)

Record rev-a0ec6ad323f81eb331c05a6afdb56eacea7f8994ec044397e82edbd6361220b1 · schema claims-v0.9 · position 3 in the model’s answer