Claim · model-written summary
Row spacing and plant removal patterns had minimal effects on UAV prediction error.
- Taxon
- soybean
Glycine maxt0 - Treatment
- Row spacing and plant removal patternse1
- Outcome
- prediction errore1
- Direction
- minimal effectse1
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleComparatorSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“stand counts in soybean”
e1“Row spacing and plant removal patterns had minimal effects on prediction error”
Title
Salem Ermish; Rachel Vann; Jason Ward; et al. Agronomy Journal 2026. doi:10.1002/agj2.70303
Other claims from this paper (7)
- Method / resource The point-based UAV counting method gave its highest accuracy, within ±12% of ground-truth with R 2 = 0.81, when imagery was collected 14 to 20 DAP at 15.2-m altitude.
- Association UAV count accuracy declined beyond 27 DAP as canopy overlap increased.
- Observation The line-based method remained more stable across altitudes and later growth stages.
- Observation The line-based method consistently overestimated plant counts, particularly in dense and narrow row canopies.
- Method / resource On-ground calibration areas improved accuracy by an average of 28% and up to 48% for the line-based approach in narrow rows.
- Observation Short repeating gaps were poorly detected by the line-based method.
- Recommendation The authors conclude that UAV-based plant counts in soybean are feasible and dependable when flights are timed during early vegetative growth and supported by calibration.