Claim · model-written summary
The workflow had MAE of 317.21 kg/ha.
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Verbatim
e0“MAE 317.21 kg/ha”
Title
Md Didarul Islam; Liping Di; Faisal Mueen Qamer; et al. Remote Sensing 2023. doi:10.3390/rs15092374
Other claims from this paper (7)
- Method / resource NDVI alone was insufficient for accurate rice yield estimation.
- Method / resource Adding meteorological variables markedly improved yield estimation accuracy.
- Association Rainfall, soil moisture and evapotranspiration, along with NDVI, were strongly associated with rice yield.
- Method / resource Stacked tree regression models outperformed linear regression and individual ML models in yield accuracy.
- Observation Yield estimation accuracy varied among districts.
- Method / resource The workflow reached 92% average yield estimation accuracy.
- Method / resource The workflow had RMSE of 328.06 kg/ha.