View: List Claim type Research area Organism Taxon as written Decade Paper organism_group: Green plants ✕ organism_family: Solanaceae ✕ Organism: Solanum lycopersicum ✕
Observation observed original result measured Claim · model-written summary
Tomato served as an intermediate host for PVY, with outcomes varying by virus strain.
outcomes varying by virus strain e0 Verbatim
t0 “two isolates of potato virus Y ”
✓ located verbatim in the source · title, characters 53–83 e0 “Tomato served as an intermediate host, with outcomes varying by virus strain ”
✓ located verbatim in the source · abstract, characters 958–1034 Other claims from this paper (9) Observation observed original result measured Claim · model-written summary
Tomato acted as a sink host for some PVY lineages, resulting in unsuccessful infection in the next passage.
unsuccessful infection in the next passage e1 Verbatim
t0 “two isolates of potato virus Y ”
✓ located verbatim in the source · title, characters 53–83 e0 “Tomato served as an intermediate host ”
✓ located verbatim in the source · abstract, characters 958–995 e1 “acted as a sink host for some lineages, resulting in unsuccessful infection in the next passage ”
✓ located verbatim in the source · abstract, characters 1040–1135 Other claims from this paper (9) Method / resource observed original result positive measured Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached bounding box precision of 90.8% on the test sets for cherry tomato maturity segmentation.
bounding box precision e0 Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 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 ”
✓ located verbatim in the source · abstract, characters 1393–1544 Other claims from this paper (8) Method / resource The improved MSW-YOLOv8n-Seg model reached recall of 86.3% 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. Method / resource observed original result positive measured Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached recall of 86.3% on the test sets for cherry tomato maturity segmentation.
Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 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 ”
✓ located verbatim in the source · abstract, characters 1393–1544 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. Method / resource observed original result positive measured Claim · model-written summary
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.
mean average precision (mAP)@0.5 e0 Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 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 ”
✓ located verbatim in the source · abstract, characters 1393–1544 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 recall of 86.3% 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. Method / resource observed original result positive measured Claim · model-written summary
The model size of MSW-YOLOv8n-Seg was 6.0 MB.
Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 e0 “the model size was 6.0 MB ”
✓ located verbatim in the source · abstract, characters 1550–1575 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 recall of 86.3% 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 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. Method / resource observed original result positive measured Claim · model-written summary
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.
bounding box precision e0 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0% e0 YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN e1 Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 e0 “the bounding box precision increased by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0% ”
✓ located verbatim in the source · abstract, characters 1720–1799 e1 “YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN ”
✓ located verbatim in the source · abstract, characters 1591–1653 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 recall of 86.3% 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 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. Method / resource observed original result positive measured Claim · model-written summary
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.
7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1% e0 YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN e1 Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 e0 “the recall increased by 7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1% ”
✓ located verbatim in the source · abstract, characters 1801–1864 e1 “YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN ”
✓ located verbatim in the source · abstract, characters 1591–1653 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 recall of 86.3% 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 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. Method / resource observed original result positive measured Claim · model-written summary
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.
10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4% e0 YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN e1 Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 e0 “the mAP@0.5 increased by 10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4% ”
✓ located verbatim in the source · abstract, characters 1870–1935 e1 “YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN ”
✓ located verbatim in the source · abstract, characters 1591–1653 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 recall of 86.3% 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 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. Method / resource observed original result positive measured Claim · model-written summary
MSW-YOLOv8n-Seg had the highest inference speed, with FPS of up to 52.9.
Verbatim
t0 “cherry tomato ”
✓ located verbatim in the source · title, characters 70–83 e0 “the MSW-YOLOv8n-Seg has the highest inference speed, with FPS of up to 52.9 ”
✓ located verbatim in the source · abstract, characters 1979–2054 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 recall of 86.3% 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 a latency of only 18.2 ms. Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.
Solanum lycopersicume0
Potato virus Yt0pathogen
Solanum lycopersicum var. cerasiforme