Method / resource 289,819 claims

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.

Taxon

cherry tomatoSolanum lycopersicum var. cerasiformet0

Outcome

recalle0

Direction

increasede0

Value

7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1%e0

Comparator

YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNNe1

Verbatim

t0

“cherry tomato”

e0

“the recall increased by 7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1%”

e1

“YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN”

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.

Taxon

cherry tomatoSolanum lycopersicum var. cerasiformet0

Outcome

mAP@0.5e0

Direction

increasede0

Value

10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4%e0

Comparator

YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNNe1

Verbatim

t0

“cherry tomato”

e0

“the mAP@0.5 increased by 10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4%”

e1

“YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN”

Claim · model-written summary

MSW-YOLOv8n-Seg had the highest inference speed, with FPS of up to 52.9.

Taxon

cherry tomatoSolanum lycopersicum var. cerasiformet0

Outcome

FPSe0

Direction

higheste0

Value

52.9e0

Verbatim

t0

“cherry tomato”

e0

“the MSW-YOLOv8n-Seg has the highest inference speed, with FPS of up to 52.9”

Claim · model-written summary

MSW-YOLOv8n-Seg had a latency of only 18.2 ms.

Taxon

cherry tomatoSolanum lycopersicum var. cerasiformet0

Outcome

latencye0

Value

18.2mse0

Verbatim

t0

“cherry tomato”

e0

“latency of only 18.2ms”

Claim · model-written summary

The 3K array and GBS-based SNPs performed similarly for training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield in oat.

Taxon

oatAvena sativat0

Outcome

performancee0

Direction

similare0

Comparator

GBS-based SNPse0

Verbatim

e0

“similar performance of both the 3K array and GBS-based SNPs in terms of training population optimization, forward prediction, and univariate and multivariate genomic prediction of forage yield”

t0

“biomass yield in oat”

Claim · model-written summary

GBLUP, Bayes-B and random forest models gave similar predictive ability for dry matter yield across harvest-year combinations and both marker platforms.

Taxon

oatAvena sativat0

Outcome

predictive ability for dry matter yield (DMY)e0

Direction

similare0

Comparator

Bayes-Be0

Verbatim

e0

“The genomic best linear unbiased prediction (GBLUP), Bayes-B, and random forest models gave similar predictive ability for dry matter yield (DMY)”

t0

“biomass yield in oat”

Claim · model-written summary

Multivariate models with secondary traits gave more than twofold higher predictive abilities than univariate models.

Taxon

oatAvena sativat0

Outcome

predictive abilitiese0

Direction

increasese0

Value

more than twofolde0

Comparator

univariate modelse0

Verbatim

e0

“The multivariate models involving various combinations of secondary traits (simple breeders' field notes and data) resulted in more than twofold increases in predictive abilities compared to the univariate models”

t0

“biomass yield in oat”

Claim · model-written summary

The overlap between top 25% observed and predicted genotypes was higher for multivariate GBLUP models (30.10%-66.99%) than univariate models (27.18%-51.46%).

Taxon

oatAvena sativat0

Outcome

overlap percentagee0

Direction

highere0

Value

30.10%-66.99%e0

Qualifier

27.18%-51.46%e0

Comparator

univariate modelse0

Verbatim

e0

“a higher overlap percentage (30.10%-66.99%) for multivariate GBLUP models compared to the univariate models (27.18%-51.46%)”

t0

“biomass yield in oat”

Claim · model-written summary

The point-based UAV counting method gave its highest accuracy, within ±12% of ground-truth with R 2 = 0.81, when imagery was collected 14 to 20 DAP at 15.2-m altitude.

Taxon

soybeanGlycine maxt0

Outcome

accuracye1

Direction

higheste1

Value

within ±12% of ground‐truth; R 2 = 0.81e1

Qualifier

between 14 and 20 DAP at 15.2‐m altitudee1

Verbatim

t0

“stand counts in soybean”

e1

“The point‐based method provided the highest accuracy (within ±12% of ground‐truth; R 2 = 0.81) when imagery was collected between 14 and 20 DAP at 15.2‐m altitude”

Claim · model-written summary

On-ground calibration areas improved accuracy by an average of 28% and up to 48% for the line-based approach in narrow rows.

Taxon

soybeanGlycine maxt0

Treatment

on‐ground calibration arease1

Outcome

accuracye1

Direction

improvede1

Value

an average of 28% and up to 48%e1

Verbatim

t0

“stand counts in soybean”

e1

“Incorporating on‐ground calibration areas improved accuracy by an average of 28% and up to 48% for the line‐based approach in narrow rows”