Search claims · PlantClaims

Observationobservedoriginal resultmeasured

Claim · model-written summary

Half of the soybean cultivars (FPS Júpiter, FPS Urano, FPS Antares, FPS Netuno and CD 250) had high germination rates and seed vigor

Taxon: soybeanGlycine maxe1
Material: FPS Júpiter, FPS Urano, FPS Antares, FPS Netuno and CD 250e3
Outcome: germination rates and seed vigore3
Qualifier: highe3

Verbatim

e1
“physiological quality of soybean seeds stored in cold chamber and under environmental conditions”
✓ located verbatim in the source · abstract, characters 62–158

e3
“Half of the cultivars (FPS Júpiter, FPS Urano, FPS Antares, FPS Netuno and CD 250) presented high germination rates and seed vigor”
✓ located verbatim in the source · abstract, characters 794–924

Title

Genetic diversity in soybean seed quality under different storage conditions

Daniele Piano Rosa; Danúbia Aparecida Costa Nobre; Diego Santos Oliveira; et al. Semina Ciências Agrárias 2017. doi:10.5433/1679-0359.2017v38n1p57

Other claims from this paper (4)

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)

Association Yield per vine, berries per cluster, cluster weight, clusters per shoot, and cluster number differed by clone and rootstock.
Association Berry weight differed by clone but not rootstock.
Association All yield components varied across the four vintages.
Observation FPS 8 had the highest yield and yield components.
Observation FPS 6 had the lowest yield and yield components.
Association Rootstock 110R yielded more than 5C.
Association Rootstock 110R had more clusters than 5C.
Association Rootstock 110R had more berries per cluster than 5C.
Association Berries per cluster was the yield component most strongly correlated with yield differences.
Association Pruning weight was inversely related to yield.
Observation FPS 8 had the lowest pruning weight.
Observation FPS 6 had the highest pruning weight.
Observation FPS 8 had the highest Ravaz index.
Observation FPS 6 had the lowest Ravaz index.

Observationobservedoriginal resultnegativemeasured

Claim · model-written summary

FPS 8 yielded 35% less than FPS 1, 3, and 6.

Taxon: Merlott0
Material: FPS 8e0
Outcome: yieldede0
Direction: lesse0
Value: 35%e0
Comparator: FPS 1, 3, and 6e0

Verbatim

e0
“FPS 8 yielded 35% less than clones FPS 1, 3, and 6.”
✓ located verbatim in the source · abstract, characters 654–705

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 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.

Taxon: TomatoSolanum lycopersicumt0
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 / resource YOLOV8s attained 92.5% mean average precision for detecting diseased tomato leaves, exceeding YOLOV5 at 89.1% and Faster R-CNN at 77.5%.
Method / resource YOLOV8s was considerably smaller than the comparison models.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

The pipeline reported 67.65 FPS for 67.65 FPS.

Taxon: cut chrysanthemumt0
Outcome: 67.65 FPSe0
Value: 67.65 FPSe0

Verbatim

e0
“fast inference time (14.78 ms per image), and 67.65 FPS”
✓ located verbatim in the source · abstract, characters 682–737

t0
“cut chrysanthemum”
✓ located verbatim in the source · title, characters 81–98

Title

Deep learning-based approach for extracting inflorescence morphology features in cut chrysanthemum

Shanpeng Xu; Jingshan Lu; Yin Wu; et al. Smart Agricultural Technology 2025. doi:10.1016/j.atech.2025.101265

Other claims from this paper (10)

Method / resource The pipeline reported 95.24% accuracy for flower type classification.
Method / resource The pipeline reported 1.26M parameters for parameters.
Method / resource The pipeline reported 0.15 GFLOPs for 0.15 GFLOPs.
Method / resource The pipeline reported 14.78 ms per image for inference time.
Method / resource The pipeline reported reduction of over 95% for parameters.
Method / resource The pipeline reported 0.934 for average Dice similarity coefficient (DSC).
Method / resource The pipeline reported 6.605 mm for mean squared errors (MSE).
Method / resource The pipeline reported 2.034 mm for mean squared errors (MSE).
Method / resource The pipeline reported approaching 0.98 for coefficients of determination (R2).
Method / resource The pipeline reported above 0.87 for F1-scores.

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 / resource YOLO11-GME reached 96.2% mean average precision.
Method / resource YOLO11-GME improved mAP by 8.6% over YOLOv5n.
Method / resource YOLO11-GME improved mAP by 7% over YOLOv8n.
Method / resource YOLO11-GME improved mAP by 5.8% over YOLOv10n.
Method / resource YOLO11-GME improved mAP by 5.9% over YOLOv11.
Method / resource Normal walnut precision increased by 8.7%.
Method / resource Fissure precision increased by 5.3%.
Method / resource Inferior category precision increased by 3.7%.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

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

Taxon: riceOryza sativat0
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)

Method / resource The detector reached an mAP@0.5 of 95.3%, outperforming rival detectors by 1.5–32.6%.

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)

Observation Yield and four yield components differed significantly among clones.
Observation FPS 4 and French clones 96 and 352 had the highest yields.
Observation FPS 72 had the lowest yield.
Observation Other clones showed no great differences in yield.
Mechanism Very low berry weight contributed to low FPS 72 yield.
Mechanism Fewer berries per cluster contributed to low FPS 72 yield.
Observation Yield components of other clones differed significantly over four years.
Association Vegetative growth was greatly influenced by clone and year.
Observation FPS 72 had the highest pruning weights.
Observation Clone 118 had the lowest pruning weights.
Observation Yield-to-pruning weight ratios were lowest for FPS 72.
Observation Yield-to-pruning weight ratios were highest for FPS 4.

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 / resource The model used 6.04 M parameters.
Method / resource The model ran at 60.14 frames per second.
Method / resource Mean intersection over union was 81.77%.
Method / resource F1 score was 89.12%.
Method / resource Against original DeepLabV3+, mIoU improved by 10.78 percentage points.
Method / resource Parameter count fell from 54.71 M to 6.04 M.
Method / resource Cultivar mIoU ranged from 75.36% to 85.26%.
Method / resource Cultivar F1 scores ranged from 84.71% to 91.62%.
Method / resource Residual errors mainly came from illumination, occlusion and color contrast.
Method / resource Two dimensional localization had an 85.17% cultivar weighted success rate on 400 field images.
Method / resource DBSCAN based three dimensional processing achieved 92.00% plucking point accuracy in occluded and overlapping shoots.

“FPS”

Claim types

Direction of the findings

Over time

Claim type

Research area

Polarity

Statistics