Claim · model-written summary
Daily clustering and filtering of climatic variables, especially precipitation rate, were favourable for these models.
- Taxon
- wheat
Triticum aestivume0 - Treatment
- daily clustering and filtering of climatic variablese4
- Outcome
- these types of modelse4
- Direction
- favorablee4
- Qualifier
- especially precipitation ratee4
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleComparatorSettingValue
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”
e4“daily clustering and filtering of climatic variables, especially precipitation rate, were favorable in these types of models”
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.
- Recommendation NDVI, biomass and harvest index are the most suitable indices for improving the accuracy of wheat yield prediction.
- Observation RF and XGBoost-based models accurately predict wheat yield.
- 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.