Claim · model-written summary
Topography and vegetation index features (EVI2 at Farm A, elevation at Farm B) were the most prominent yield predictors.
- Outcome
- most prominent predictorse1
- Qualifier
- Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B)e1
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Corn (Zea mays L.) silage yield monitor data offer crucial insights into spatial and temporal yield variability”
e1“Results showed that the features differed among farms with a Two-Band Enhanced Vegetation Index, EVI2 (Farm A), and elevation (Farm B) emerging as the most prominent predictors”
Title
Exploring the Use of High-Resolution Satellite Images to Estimate Corn Silage Yield Within Field
Srinivasagan N. Subhashree; Manuel Marcaida; Shajahan Sunoj; et al. Remote Sensing 2024. doi:10.3390/rs16214081
Other claims from this paper (4)
- Method / resource Ensemble-based models exhibited superior predictive performance for corn silage yield.
- Method / resource XGBoost performed poorly on unseen fields or years, whereas the Extra Tree regressor was a more reliable model for predicting missing data.
- Method / resource The best model estimated missing field yield at 6.46 Mg/ha, which fell short of the 4.9 Mg/ha acceptable error threshold.
- Method / resource The best model estimated missing year yield at 5.51 Mg/ha, which fell short of the 4.9 Mg/ha acceptable error threshold.