Claim · model-written summary
Multiple regression using NDVI and CI improved prediction accuracy and determination coefficient.
- Taxon
- potato
Solanum tuberosume0 - 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.

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.”
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)
- Intervention effect 168 kg N ha −1 produced the maximum potato yield.
- Association Indices at the 16th and 20th leaf stages correlated significantly with tuber yield.
- Method / resource Exponential and linear models fit the data better.
- Association Soil organic matter content significantly increased potato yield.
- Association Soil organic matter content did not affect prediction models.
- Recommendation The authors identify the 18th and 20th leaf stages as best for sensor prediction.