Claim · model-written summary
The model exceeded baseline Unet by over 10.5% in IoU.
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Verbatim
e0“surpassing the baseline Unet by over 10.5% in IoU.”
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 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 On the larger dataset the model maintained superior computational efficiency.
- Method / resource Interpretability analysis indicated that the backbone captures high-level lesion features.