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Associationobservedoriginal resultpositivemeasured

Claim · model-written summary

In higher yield years FPS 8 had more soluble solids than FPS 4 or FPS 6.

Taxon
Malbece1
Material
FPS 8e0
Outcome
soluble solidse0
Direction
greatere0
Qualifier
In years of greater yielde0
Comparator
FPS 4 or 6e0

Verbatim

e0

“In years of greater yield, FPS 8 had greater soluble solids than FPS 4 or 6.”

✓ located verbatim in the source · abstract, characters 1214–1290
e1

“Malbec”

✓ located verbatim in the source · abstract, characters 16–22

Title

Viticultural Performance of Three Malbec Clones on Two Rootstocks in Oakville, Napa Valley, California

M. Jason Benz; Michael M. Anderson; Molly A. Williams; et al. American Journal of Enology and Viticulture 2007. doi:10.5344/ajev.2007.58.2.262

Other claims from this paper (14)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

YOLOV8s reached 121.5 FPS, faster than YOLOV5 at 102.7 FPS and Faster R-CNN at 11 FPS.

Outcome
frame ratee0
Direction
superiore0
Value
121.5 FPSe0
Qualifier
Faster R-CNN’s 11 FPSe0
Comparator
YOLOV5e0

Verbatim

e0

“The YOLOV8s model has a significantly superior frame rate, reaching 121.5 FPS, in contrast to YOLOV5’s 102.7 FPS and Faster R-CNN’s 11 FPS”

✓ located verbatim in the source · abstract, characters 963–1101
t0

“Tomato Leaves”

✓ located verbatim in the source · title, characters 58–71

Title

A Deep-Learning-Based Model for the Detection of Diseased Tomato Leaves

Akram Abdullah; Gehad Abdullah Amran; S. M. Ahanaf Tahmid; et al. Agronomy 2024. doi:10.3390/agronomy14071593

Other claims from this paper (2)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The frame rate was 43.92 FPS versus 51.02 FPS for the original model.

Taxon
walnutt0
Outcome
frame ratee0
Value
43.92 FPSe0
Qualifier
51.02 FPSe0
Comparator
original modele0

Verbatim

e0

“The frame rate remains at 43.92 FPS, approaching the original model’s 51.02 FPS.”

✓ located verbatim in the source · abstract, characters 1347–1427
t0

“Walnut”

✓ located verbatim in the source · title, characters 0–6

Title

Walnut Surface Defect Classification and Detection Model Based on Enhanced YOLO11n

Xinyi Ma; Zhongjia Hao; Shuangyin Liu; et al. Agriculture 2025. doi:10.3390/agriculture15151707

Other claims from this paper (8)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The detector ran at 154 FPS, outperforming rival detectors by 12–132 FPS.

Outcome
frame ratee2
Value
154 FPSe2
Qualifier
outperforming rivals by 12–132 FPSe2
Comparator
rivalse2

Verbatim

t0

“rice panicles”

✓ located verbatim in the source · title, characters 104–117
e2

“a frame rate of 154 FPS (outperforming rivals by 12–132 FPS)”

✓ located verbatim in the source · abstract, characters 1535–1595

Title

Dual attention guided context-aware feature learning for residual unfilled grains detection on threshed rice panicles

Yuhao Zhou; Xiao Feng; Shuqi Tang; et al. Artificial Intelligence in Agriculture 2025. doi:10.1016/j.aiia.2025.11.005

Other claims from this paper (1)
Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

FPS 4 and FPS 72 retained the highest titratable acidity at harvest.

Taxon
Chardonnaye1
Material
FPS 4 and FPS 72e0
Organ / stage
harveste0
Outcome
levels of titratable aciditye0
Direction
higheste0

Verbatim

e0

“FPS 4 and FPS 72 retained the highest levels of titratable acidity at harvest”

✓ located verbatim in the source · abstract, characters 1094–1171
e1

“Chardonnay (Vitis vinifera L.) clones”

✓ located verbatim in the source · abstract, characters 9–46

Title

Viticultural Evaluation of French and California Chardonnay Clones Grown for Production of Sparkling Wine

Michael M. Anderson; Rhonda J. Smith; Molly A. Williams; et al. American Journal of Enology and Viticulture 2008. doi:10.5344/ajev.2008.59.1.73

Other claims from this paper (12)
Observationobservedoriginal resultpositivemeasured

Claim · model-written summary

Yield to pruning weight ratios were lowest for FPS 8 and highest for FPS 1.

Taxon
Merlott0
Material
FPS 8e0
Outcome
Yield to pruning weight ratiose0
Direction
loweste0

Verbatim

e0

“Yield to pruning weight ratios were lowest for FPS 8 and highest for FPS 1.”

✓ located verbatim in the source · abstract, characters 1213–1288
t0

“Merlot”

✓ located verbatim in the source · title, characters 33–39

Title

Viticultural Performance of Five Merlot Clones in Oakville, Napa Valley

M. Jason Benz; Michael M. Anderson; Molly A. Williams; et al. American Journal of Enology and Viticulture 2006. doi:10.5344/ajev.2006.57.2.233

Other claims from this paper (8)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Inference speed rose from 30.36 FPS to 60.14 FPS.

Taxon
tea shootst0
Outcome
inference speede0
Direction
nearly doublese0
Value
60.14 FPSe0
Qualifier
30.36 FPSe0

Verbatim

e0

“nearly doubles inference speed from 30.36 FPS to 60.14 FPS”

✓ located verbatim in the source · abstract, characters 1390–1448
t0

“tea shoots”

✓ located verbatim in the source · title, characters 78–88

Title

Pluckable-zone segmentation and plucking-point localization for multicultivar tea shoots using an improved lightweight DeepLabV3+ and DBSCAN–assisted 3D clustering

Decheng Liu; Pengfei Wang; Zhi Zhang; et al. Information Processing in Agriculture 2026. doi:10.1016/j.inpa.2026.05.012

Other claims from this paper (11)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv8-VEW ran at 90 FPS, 24 FPS better than YOLOv8.

Taxon
SoybeanGlycine maxt0
Outcome
FPSe0
Direction
bettere0
Value
90 FPSe0
Qualifier
24 FPSe0
Comparator
YOLOv8 modele0

Verbatim

e0

“the F1, mAP, and FPS (frames per second) of the YOLOv8-VEW model were 0.95, 96.9%, and 90 FPS, respectively, which were 0.05, 2.4%, and 24 FPS better than those of the YOLOv8 model.”

✓ located verbatim in the source · abstract, characters 692–873
t0

“Soybean Flower and Pod Detection”

✓ located verbatim in the source · title, characters 12–44

Title

Field-Based Soybean Flower and Pod Detection Using an Improved YOLOv8-VEW Method

Kunpeng Zhao; Jinyang Li; Wenqiang Shi; et al. Agriculture 2024. doi:10.3390/agriculture14081423

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivesignificant (as stated)measured

Claim · model-written summary

On Nvidia Jetson Xavier NX, YOLOv8n-MCP achieved 49.5 FPS versus 37.6 FPS for YOLOv8n.

Taxon
strawberryFragaria ×ananassat0
Outcome
FPSe0
Direction
outperforminge0
Value
49.5 FPSe0
Qualifier
37.6 FPSe0
Comparator
original YOLOv8ne0
Setting
Nvidia Jetson Xavier NXe0

Verbatim

t0

“strawberry”

✓ located verbatim in the source · title, characters 36–46
e0

“Tests on Nvidia Jetson Xavier NX demonstrate that YOLOv8n-MCP achieves 49.5 FPS, significantly outperforming the original YOLOv8n’s 37.6 FPS”

✓ located verbatim in the source · abstract, characters 1125–1265

Title

Efficient instance segmentation for strawberry in greenhouses using YOLOv8n-MCP on edge devices

Xinhao Zhang; Guangpeng Zhang; Jiayi Wang; et al. Information Processing in Agriculture 2025. doi:10.1016/j.inpa.2025.07.001

Other claims from this paper (3)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

FCHE-YOLO inference speed rose to 47.5 FPS from 43.3 FPS.

Taxon
teat0
Outcome
inference speede0
Direction
increasese0
Value
47.5 FPSe0
Comparator
43.3 FPSe0

Verbatim

e0

“the inference speed of the model increases from 43.3 FPS to 47.5 FPS, with an increase of 9.0%”

✓ located verbatim in the source · abstract, characters 1349–1443
t0

“tea leaf”

✓ located verbatim in the source · title, characters 8–16

Title

Precise tea leaf disease detection using UAV low-altitude remote sensing and optimized YOLO11 model

Yaojun Zhang; Guiling Wu; Jianbo Shen PLoS ONE 2026. doi:10.1371/journal.pone.0342545

Other claims from this paper (4)

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.