Method / resourcemodel-written summary

Multiple regression using NDVI and CI improved prediction accuracy and determination coefficient.

A claim from In‐season potato yield prediction with active optical sensors · 2020

Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Multiple regression using NDVI and CI improved prediction accuracy and determination coefficient.

Outcome
accuracy of the prediction model and increase the determination coefficiente0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier

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

Solanum tuberosum
Solanum tuberosum · TaxonScott Bauer, USDA ARS · Public domain · Wikimedia Commons ↗

Verbatim

e0

“Multiple regression analysis (potato yield as a dependent variable and vegetation indices, NDVI and CI, as independent variables) could make a remarkable improvement to the accuracy of the prediction model and increase the determination coefficient.”

✓ located verbatim in the source · abstract, characters 1087–1336

Title

In‐season potato yield prediction with active optical sensors

Ahmed A. Zaeen; Lakesh Sharma; Ahmed Jasim; et al. Agrosystems Geosciences & Environment 2020. doi:10.1002/agg2.20024

Other claims from this paper (6)

Record rev-cb5e8dd257f4afa1033e4581bba10343b8b50dc6233242013c26236abfca3444 · schema claims-v0.9 · position 3 in the model’s answer