Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached recall of 86.3% on the test sets for cherry tomato maturity segmentation.
- Taxon
- cherry tomato
Solanum lycopersicum var. cerasiformet0 - Outcome
- recalle0
- Value
- 86.3%e0
Verbatim
t0“cherry tomato”
e0“the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580
Other claims from this paper (8)
- Method / resource The improved MSW-YOLOv8n-Seg model reached bounding box precision of 90.8% on the test sets for cherry tomato maturity segmentation.
- Method / resource The improved MSW-YOLOv8n-Seg model reached mean average precision (mAP)@0.5 of 83.9% on the test sets for cherry tomato maturity segmentation.
- Method / resource The model size of MSW-YOLOv8n-Seg was 6.0 MB.
- Method / resource The improved model increased bounding box precision over YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN and Mask2Former by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0% respectively.
- Method / resource The improved model increased recall over YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN and Mask2Former by 7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1% respectively.
- Method / resource The improved model increased mAP@0.5 over YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN and Mask2Former by 10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4% respectively.
- Method / resource MSW-YOLOv8n-Seg had the highest inference speed, with FPS of up to 52.9.
- Method / resource MSW-YOLOv8n-Seg had a latency of only 18.2 ms.