Claim · model-written summary
The authors established an image dataset of 13,600 images representing 17 wheat varieties at the early growth stage.
- Taxon
- wheat
Triticum aestivume0 - Organ / stage
- early growth stagee0
- Outcome
- image datasete0
- Value
- 13,600 imagese0
Not reportedMaterialGeneCompoundInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Verbatim
e0“We established a comprehensive image dataset of 13,600 images representing 17 wheat varieties at the early growth stage”
Title
Multi branch model based on cross scale feature fusion for wheat seedling variety recognition
Zhang Wenbo; Zhang Ziyang; Xi Chengyu; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101785
Other claims from this paper (2)
- Method / resource The SeedlingNet deep learning model classified wheat seedling varieties with 99.26% accuracy, outperforming traditional machine learning methods and mainstream deep learning models.
- Intervention effect Ablation studies confirmed a significant impact of the KCA module and the multi-scale fusion structure on the model's performance.