Claim · model-written summary
On the larger dataset the model maintained superior computational efficiency.
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingValueQualifier
Verbatim
e0“The proposed paradigm achieves the best overall detection performance while maintaining superior computational efficiency”
Title
Precise leaf damage detection across diverse species and environments via a large-scale vision model
Wei Chen; Hao Ruan; Peng Zhou; et al. Frontiers in Plant Science 2026. doi:10.3389/fpls.2026.1788926
Other claims from this paper (7)
- Method / resource The proposed model outperformed Unet, Unet++, and SwinUnet on coffee and black gram datasets.
- Method / resource On coffee leaves the model achieved 78.31% IoU.
- Method / resource On coffee leaves the model achieved 88.00% pixel accuracy.
- Method / resource The model exceeded baseline Unet by over 10.5% in IoU.
- Method / resource The architecture reduced inference time by approximately 93.6%, from 63.41s to 4.07s.
- Method / resource On the larger AMG HS dataset the model had the best overall detection performance.
- Method / resource Interpretability analysis indicated that the backbone captures high-level lesion features.