Claim · model-written summary
HR-YOLOv8 used fewer parameters than baseline on the two crop datasets.
- Taxon
- strawberry
Fragaria ×ananassae0 - Outcome
- parameterse0
- Direction
- fewere0
- Comparator
- baseline modele0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
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Verbatim
e0“on the oilpalmuav dataset and strawberry ripeness dataset, our model has fewer parameters compared to the baseline model”
Title
HR-YOLOv8: A Crop Growth Status Object Detection Method Based on YOLOv8
Jin Zhang; Wenzhong Yang; Zhifeng Lu; et al. Electronics 2024. doi:10.3390/electronics13091620
Other claims from this paper (4)
- Method / resource HR-YOLOv8 improved average detection accuracy on the oilpalmuav dataset by 5.2% against baseline.
- Method / resource HR-YOLOv8 improved average detection accuracy on strawberry ripeness data by 0.6%.
- Method / resource The model had better overall performance than other mainstream models.
- Method / resource The method improved small object detection ability.