Claim · model-written summary
Models using all spectral bands outperformed spectral-index configurations.
- Taxon
- Rice
Oryza sativat0 - Outcome
- outperformede0
- Direction
- outperformede0
- Comparator
- configurations based only on spectral indicese0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“models using all spectral bands clearly outperformed configurations based only on spectral indices”
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 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.
- Method / resource The approach generated spatial rice yield maps capturing within-field variability.