Claim · model-written summary
The approach generated spatial rice yield maps capturing within-field variability.
- Taxon
- Rice
Oryza sativat0 - Outcome
- spatially explicit rice yield mapse0
- Value
- within-field variabilitye0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability”
t0“Rice Yield Prediction”
Title
Rubén Simeón; Alba Agenjos-Moreno; Constanza Rubio; et al. Agriculture 2026. doi:10.3390/agriculture16020201
Other claims from this paper (10)
- Method / resource XGBoost outperformed Random Forest for rice yield prediction.
- Method / resource For JSendra, XGBoost achieved R2 of 0.74 at 110 DAS.
- Method / resource For Bomba, XGBoost achieved R2 of 0.85 at 130 DAS.
- Method / resource For JSendra, XGBoost RMSE was 0.63 t·ha−1.
- Method / resource For Bomba, XGBoost RMSE was 0.28 t·ha−1.
- Method / resource Prediction accuracy increased as the season progressed.
- Method / resource Models using all spectral bands outperformed spectral-index configurations.
- Method / resource NIR reflectance made the dominant contribution to model prediction.
- Method / resource Prediction errors occurred at field edges and headlands.
- Method / resource Central pixels were predicted more accurately.