Claim · model-written summary
Computational cost fell 46.0% to 3.4 GFLOPs versus baseline.
- Taxon
- Kiwifruit
Actinidia deliciosat0 - Outcome
- computational coste0
- Direction
- reducede0
- Value
- 3.4 GFLOPse0
- Qualifier
- 46.0%e0
- Comparator
- baseline YOLOv11ne0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSetting
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The number of parameters, model weight, and computational cost are reduced to 1.5 M, 3.3 M, and 3.4 GFLOPs, representing decreases of 42.3%, 41.1%, and 46.0% compared to the baseline YOLOv11n, respectively”
t0“Kiwifruit Flower”
Title
A Lightweight Kiwifruit Flower Detection Method Based on an Improved YOLOv11n
Jianwei Zhang; Wei Hao; Xu Wang; et al. New Zealand Journal of Crop and Horticultural Science 2026. doi:10.1002/nzc2.70094
Other claims from this paper (9)
- Method / resource The improved model identified flowers at different opening degrees.
- Method / resource The model achieved 93.0% precision.
- Method / resource The model achieved 92.6% recall.
- Method / resource The model achieved 95.3% mean average precision.
- Method / resource Parameter count fell 42.3% to 1.5 M versus baseline.
- Method / resource Model weight fell 41.1% to 3.3 M versus baseline.
- Method / resource The model achieved 303.1 FPS.
- Method / resource Edge device latency was 24 ms.
- Method / resource Edge device detection accuracy was 90.1%.