Claim · model-written summary
YOLOv8-VEW ran at 90 FPS, 24 FPS better than YOLOv8.
- Taxon
- Soybean
Glycine maxt0 - Outcome
- FPSe0
- Direction
- bettere0
- Value
- 90 FPSe0
- Qualifier
- 24 FPSe0
- Comparator
- YOLOv8 modele0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSetting
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the F1, mAP, and FPS (frames per second) of the YOLOv8-VEW model were 0.95, 96.9%, and 90 FPS, respectively, which were 0.05, 2.4%, and 24 FPS better than those of the YOLOv8 model.”
t0“Soybean Flower and Pod Detection”
Title
Field-Based Soybean Flower and Pod Detection Using an Improved YOLOv8-VEW Method
Kunpeng Zhao; Jinyang Li; Wenqiang Shi; et al. Agriculture 2024. doi:10.3390/agriculture14081423
Other claims from this paper (4)
- Method / resource YOLOv8-VEW F1 was 0.95, 0.05 above YOLOv8.
- Method / resource YOLOv8-VEW mAP was 96.9%, 2.4% better than YOLOv8.
- Method / resource Model flower counts matched manual counts with R2 0.98311.
- Method / resource Model pod counts matched manual counts with R2 0.98926.