Claim · model-written summary
The pipeline reported 14.78 ms per image for inference time.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier
Verbatim
e0“fast inference time (14.78 ms per image), and 67.65 FPS”
t0“cut chrysanthemum”
Title
Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum
Shanpeng Xu; Jingshan Lu; Yin Wu; et al. Smart Agricultural Technology 2025. doi:10.1016/j.atech.2025.101265
Other claims from this paper (10)
- Method / resource The pipeline reported 95.24% accuracy for flower type classification.
- Method / resource The pipeline reported 1.26M parameters for parameters.
- Method / resource The pipeline reported 0.15 GFLOPs for 0.15 GFLOPs.
- Method / resource The pipeline reported 67.65 FPS for 67.65 FPS.
- Method / resource The pipeline reported reduction of over 95% for parameters.
- Method / resource The pipeline reported 0.934 for average Dice similarity coefficient (DSC).
- Method / resource The pipeline reported 6.605 mm for mean squared errors (MSE).
- Method / resource The pipeline reported 2.034 mm for mean squared errors (MSE).
- Method / resource The pipeline reported approaching 0.98 for coefficients of determination (R2).
- Method / resource The pipeline reported above 0.87 for F1-scores.