Claim · model-written summary
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.
- Outcome
- performance across all evaluation metricse1
- Direction
- beste1
- Comparator
- Eff U-Net, ResNeSt50, ConvNeXt, and ViTe0
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“maize ear harvesters”
e0“We compare the performance of FIBA U-Net with Eff U-Net, ResNeSt50, ConvNeXt, and ViT”
e1“FIBA U-Net achieves the best performance across all evaluation metrics”
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 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.
- Method / resource Damage detection rates at the bottom of the grain collection box differed from actual damage rates by only 0.42% under full coverage.