Claim · model-written summary
MRD-YOLO outperformed existing methods in balanced accuracy and computational efficiency.
- Outcome
- balanced accuracy and computational efficiencye0
- Direction
- outperformse0
- Comparator
- existing methodse0
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Verbatim
e0“It comprehensively outperforms existing methods in terms of balanced accuracy and computational efficiency.”
Title
Xuebin Jing; Yuanhao Wang; Dongxi Li; et al. Plant Methods 2024. doi:10.1186/s13007-024-01259-3
Other claims from this paper (6)
- Method / resource A greenhouse-sourced melon ripeness dataset was created with occlusion, overlap and lighting variation.
- Method / resource MRD-YOLO achieved 97.4% mean Average Precision on the melon dataset.
- Method / resource MRD-YOLO used 4.8 G FLOPs, 58.5% of the YOLOv8n baseline.
- Method / resource MRD-YOLO used 2.06 M parameters, 68.4% of the YOLOv8n baseline.
- Method / resource MRD-YOLO retained real-time GPU inference and exceptional CPU inference speed.
- Method / resource MRD-YOLO reached 85.9% mean Average Precision on two unseen Roboflow melon datasets.