Claim · model-written summary
NDVI, biomass and harvest index are the most suitable indices for improving the accuracy of wheat yield prediction.
- Taxon
- wheat
Triticum aestivume0 - Outcome
- accuracy of wheat yield predictione3
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The results obtained prove the potential of RF and XGBoost-based models to accurately predict wheat yield”
e3“Normalized difference vegetation index (NDVI), biomass (BM), and harvest index (HI) are proposed as the most suitable indices for improving the accuracy of wheat yield prediction”
Title
Juan Carlos Moreno Sánchez; Héctor Gabriel Acosta Mesa; Adrián Trueba Espinosa; et al. Smart Agricultural Technology 2025. doi:10.1016/j.atech.2025.100791
Other claims from this paper (6)
- Method / resource The XGBoost model using NDVI, biomass and harvest index gave an RMSE of 28.5082 grams per square meter for wheat yield prediction.
- Method / resource The XGBoost model using NDVI, biomass and harvest index gave an R² of 0.9156 for wheat yield prediction.
- Observation RF and XGBoost-based models accurately predict wheat yield.
- Association Daily clustering and filtering of climatic variables, especially precipitation rate, were favourable for these models.
- Association The impact of climatic variables on wheat yield is less pronounced in irrigated systems.
- Recommendation Monitoring climatic and crop indicators is recommended to understand wheat crop conditions.