Claim · model-written summary
YOLOv5s-T spikelet detection and counting performance did not decline versus original YOLOv5s.
- Outcome
- spikelet detection counting effecte0
- Direction
- not reducede0
- Comparator
- original YOLOv5s algorithme0
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Verbatim
e0“Compared with the original YOLOv5s algorithm, the spikelet detection counting effect of YOLOv5s-T was not reduced”
Title
YOLOv5s-T: A Lightweight Small Object Detection Method for Wheat Spikelet Counting
Lei Shi; Jiayue Sun; Yuanbo Dang; et al. Agriculture 2023. doi:10.3390/agriculture13040872
Other claims from this paper (10)
- Method / resource Predicted and true flowering stage spikelet counts had R2 of 0.97.
- Method / resource Predicted and true filling stage spikelet counts had R2 of 0.85.
- Method / resource Predicted and true mature stage spikelet counts had R2 of 0.78.
- Method / resource Across all three stages, R2 was 0.87.
- Method / resource Across all stages, RMSE was 0.70.
- Method / resource Model size decreased 36.8% to 9.1 M.
- Method / resource Training GPU memory use decreased by 0.82 GB.
- Method / resource Inference time decreased by 2.3 ms.
- Method / resource Processing time decreased by 10 ms.
- Method / resource Calculation amount also decreased.