Claim · model-written summary
Damage detection rates at the bottom of the grain collection box differed from actual damage rates by only 0.42% under full coverage.
- Outcome
- damage detection ratese0
- Value
- 0.42%e0
- Qualifier
- under full coveragee0
- Comparator
- actual damage ratese0
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize ear harvesters”
e0“The damage detection rates for the bottom of the grain collection box differed from the actual damage rates by only 0.31% under partial coverage and 0.42% under full coverage”
Title
Jun Fu; Chengyuan Zhang; Qiankun Fu; et al. Artificial Intelligence in Agriculture 2026. doi:10.1016/j.aiia.2026.02.005
Other claims from this paper (3)
- Method / resource FIBA U-Net achieved the best performance across all evaluation metrics when compared with Eff U-Net, ResNeSt50, ConvNeXt and ViT for segmenting maize ear damage.
- Method / resource The method reached an overall accuracy of up to 98.61% in field tests.
- Method / resource Damage detection rates at the bottom of the grain collection box differed from actual damage rates by only 0.31% under partial coverage.