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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

CPD-YOLO generalization showed a 0.55 % mAP decrease.

Outcome
mAPe0
Direction
decreasee0
Value
0.55 %e0

Not reportedComparator

Verbatim

e0

“the CPD-YOLO model had superior generalization capability, with a 1.30 % decrease in F1-score and a 0.55 % decrease in mAP.”

✓ located verbatim in the source · abstract, characters 1287–1410
t0

“cotton pests and diseases”

✓ located verbatim in the source · title, characters 48–73

Title

CPD-YOLO: A cross-platform detection method for cotton pests and diseases using UAV and smartphone imaging

Guowei Yu; Benxue Ma; Ruoyu Zhang; et al. Industrial Crops and Products 2025. doi:10.1016/j.indcrop.2025.121515

Other claims from this paper (7)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv8n-RML shows a 34.5% decrease in GFLOPS for strawberry ripeness detection.

Taxon
strawberryFragaria ×ananassae0
Outcome
GFLOPSe2
Direction
decreasee2
Value
34.5%e2

Verbatim

e0

“specifically tailored for strawberry harvesting”

✓ located verbatim in the source · abstract, characters 79–126
e2

“The YOLOv8n-RML model demonstrates a 48.6% reduction in parameters and a 34.5% decrease in GFLOPS”

✓ located verbatim in the source · abstract, characters 1002–1099

Title

A Lightweight Detection Algorithm for Strawberry Ripeness

Gaofeng Xu; Zhengcai Wang IEEE Access 2025. doi:10.1109/access.2025.3627896

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

Claim · model-written summary

CPD-YOLO generalization showed a 1.30 % F1-score decrease.

Outcome
F1-scoree0
Direction
decreasee0
Value
1.30 %e0

Not reportedComparator

Verbatim

e0

“the CPD-YOLO model had superior generalization capability, with a 1.30 % decrease in F1-score and a 0.55 % decrease in mAP.”

✓ located verbatim in the source · abstract, characters 1287–1410
t0

“cotton pests and diseases”

✓ located verbatim in the source · title, characters 48–73

Title

CPD-YOLO: A cross-platform detection method for cotton pests and diseases using UAV and smartphone imaging

Guowei Yu; Benxue Ma; Ruoyu Zhang; et al. Industrial Crops and Products 2025. doi:10.1016/j.indcrop.2025.121515

Other claims from this paper (7)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

YOLOv5_E accuracy shows a 0.9% marginal decrease compared with the original YOLOv5.

Taxon
pineappleAnanas comosuse0
Outcome
accuracye5
Direction
marginal decreasee5
Value
0.9%e5
Comparator
the original YOLOv5e5

Verbatim

e0

“During pineapple cultivation”

✓ located verbatim in the source · abstract, characters 9–37
e5

“Compared to the original YOLOv5, YOLOv5_E shows a 0.9% marginal decrease in accuracy”

✓ located verbatim in the source · abstract, characters 1033–1117

Title

A-pruning: a lightweight pineapple flower counting network based on filter pruning

Guoyan Yu; Ruilin Cai; Yingtong Luo; et al. Complex & Intelligent Systems 2023. doi:10.1007/s40747-023-01261-7

Other claims from this paper (10)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Models with one and four augmentation techniques showed a decrease in performance.

Taxon
Tea Leaft0
Outcome
performancee0
Direction
decreasee0
Comparator
one and four augmentation techniquese0

Verbatim

t0

“Tea Leaf Disease Classification”

✓ located verbatim in the source · title, characters 83–114
e0

“models with one and four augmentation techniques experience a decrease in performance”

✓ located verbatim in the source · abstract, characters 889–974

Title

Comparative Analysis of Data Augmentation Techniques on MobileNetV2 Performance in Tea Leaf Disease Classification

Fitri Mintarsih; Dewi Khairani; M. Farhan 2024 12th International Conference on Cyber and IT Service Management (CITSM) 2024. doi:10.1109/citsm64103.2024.10775557

Other claims from this paper (5)
Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

All four models had a slight accuracy decrease on workstations and mobile devices.

Taxon
Plantt0
Outcome
accuracye1
Direction
decreasee0
Comparator
workstation and mobile devicese0

Verbatim

e0

“the four models have a slight accuracy decrease on both workstation and mobile devices”

✓ located verbatim in the source · abstract, characters 765–851
e1

“Mason model has a highest accuracy of 90.54%”

✓ located verbatim in the source · abstract, characters 567–611
t0

“Plant Diseases”

✓ located verbatim in the source · title, characters 40–54

Title

Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device

Teddy Aristan; Gede Putra Kusuma Revue d intelligence artificielle 2023. doi:10.18280/ria.370221

Other claims from this paper (7)
Method / resourceauthor interpretationoriginal resultpositivemeasured

Claim · model-written summary

Feature importance suggests one camera may suffice without a significant accuracy decrease.

Outcome
accuracye0
Direction
without significant decreasee0
Comparator
one camerae0

Verbatim

e0

“Feature weight importance suggests that one camera may be sufficient for prediction without significant decrease in accuracy”

✓ located verbatim in the source · abstract, characters 991–1115
t0

“Corn Ear Height Prediction”

✓ located verbatim in the source · title, characters 10–36

Title

Automated Corn Ear Height Prediction Using Video-Based Deep Learning

Johnson Wong; Hao Sha; Mohammad Al Hasan; et al. 2020 IEEE International Conference on Big Data (Big Data) 2020. doi:10.1109/bigdata50022.2020.9378115

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

Claim · model-written summary

Model parameters fell from 9,413,574 to 2,297,334, a 75.6% decrease.

Outcome
number of model parameterse0
Direction
reducede0
Value
2,297,334e0
Qualifier
a decrease of 75.6%e0
Comparator
YOLOv11se0

Not reportedTaxon

Verbatim

e0

“number of model parameters has been substantially reduced from 9,413,574 in YOLOv11s to 2,297,334 (a decrease of 75.6%)”

✓ located verbatim in the source · abstract, characters 1931–2050

Title

YOLO-MECD: Citrus Detection Algorithm Based on YOLOv11

Yue Liao; Lerong Li; Huiqiang Xiao; et al. Agronomy 2025. doi:10.3390/agronomy15030687

Other claims from this paper (15)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Photodiode LEDIF tracked the drought-associated decrease in photochemical quenching (R2 = 0.69).

Taxon
Polygala myrtifoliaPolygala myrtifoliat0
Outcome
photochemical quenchinge0
Direction
decreasee0
Value
R2 = 0.69e0

Verbatim

e0

“LEDIFphotodiode closely tracked drought responses associated with a decrease in photochemical quenching (R2 = 0.69)”

✓ located verbatim in the source · abstract, characters 1701–1816
t0

“Polygala myrtifolia”

✓ located verbatim in the source · abstract, characters 985–1004

Title

Tracking canopy chlorophyll fluorescence with a low-cost light emitting diode platform

Logan E G Brissette; Christopher Y S Wong; Devin P McHugh; et al. AoB Plants 2023. doi:10.1093/aobpla/plad069

Other claims from this paper (3)
Method / resourceauthor interpretationoriginal resultpositiveprojected

Claim · model-written summary

The authors project direct operating costs will decrease up to 40% with AMS-7.

Taxon
FRUIT TREESt0
Outcome
direct operating costse0
Direction
decreasee0
Value
40 %e0

Verbatim

e0

“a decrease in direct operating costs up to 40 %”

✓ located verbatim in the source · abstract, characters 1983–2030
t0

“FRUIT TREES”

✓ located verbatim in the source · title, characters 34–45

Title

MECHANIZATION OF CROWN PRUNING OF FRUIT TREES

G. I. Kadykalo POMICULTURE & SMALL FRUITS CULTURE IN RUSSIA 2020. doi:10.31676/2073-4948-2020-63-260-267

Other claims from this paper (7)

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