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Method / resourceauthor interpretationoriginal resultpositivesimulated

Claim · model-written summary

The method displays the transmission path of stripe rust at farmland scale.

Outcome
transmission path of stripe ruste0
Comparator
level of farmlande0

Verbatim

e0

“displays the transmission path of stripe rust at the level of farmland”

✓ located verbatim in the source · abstract, characters 602–672
e1

“method of wheat stripe rust”

✓ located verbatim in the source · abstract, characters 40–67

Title

DTMCA: A Digital Twin Model for Stripe Rust Based on Cellular Automata

Bi Peng; Qili Chen; WenBai Chen 2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS) 2023. doi:10.1109/ccis59572.2023.10263187

Other claims from this paper (2)
Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

The dataset contains 208 stripe rust affected leaf images.

Interactor
stripe ruste0pathogen
Outcome
stripe rust affected leafe0
Value
208e0

Verbatim

t0

“Wheat Leaf Dataset”

✓ located verbatim in the source · title, characters 0–18
e0

“The dataset contains 208 stripe rust affected leaf, 102 healthy leaf and 97 septoria affected leaf disease-infected image pictures”

✓ located verbatim in the source · abstract, characters 1455–1585

Title

Wheat Leaf Dataset

Getachew, Hawi Data Archiving and Networked Services (DANS) 2021. doi:10.17632/wgd66f8n6h.1

Other claims from this paper (4)
Method / resourceobservedoriginal resultbasis n/a

Claim · model-written summary

The study provides an oriented annotation dataset of field wheat stripe rust images.

Outcome
oriented annotation dataset of in-field wheat stripe rust imagese0
Setting
in-field wheat stripe rust imagese0

Not reportedComparatorValue

Verbatim

e0

“we have built an oriented annotation dataset of in-field wheat stripe rust images, called the wheat stripe rust dataset 2021 (WSRD2021)”

✓ located verbatim in the source · abstract, characters 834–969

Title

WSRD-Net: A Convolutional Neural Network-Based Arbitrary-Oriented Wheat Stripe Rust Detection Method

Haiyun Liu; Lin Jiao; Rujing Wang; et al. Frontiers in Plant Science 2022. doi:10.3389/fpls.2022.876069

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

Claim · model-written summary

The assay detected wheat stripe rust early.

Interactor
wheat stripe ruste0pathogen
Outcome
early detection of wheat stripe ruste0

Not reportedComparator

Verbatim

e0

“an early detection of wheat stripe rust”

✓ located verbatim in the source · abstract, characters 601–640
e1

“six wheat pathogenic fungi”

✓ located verbatim in the source · abstract, characters 570–596

Title

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Ting Zhang; Qingdong Zeng; Fan Ji; et al. Nature Communications 2023. doi:10.1038/s41467-023-39952-x

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

SSR markers capture a substantial part of the phenotypic variation caused by stripe rust.

Interactor
stripe ruste5pathogen
Outcome
phenotypic variation caused by stripe ruste5
Value
a substantial parte5

Verbatim

e5

“SSR markers used in the current study effectively capture a substantial part of the phenotypic variation caused by stripe rust”

✓ located verbatim in the source · abstract, characters 1038–1164

Title

Impact of Stripe Rust Resistance Alleles on Wheat Grain Yield Using Landraces and Improved Accessions

ElBasyoni IS Annals of Agricultural & Crop Sciences 2021. doi:10.26420/annagriccropsci.2021.1084

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

Claim · model-written summary

Prediction accuracy of the detected QTL for stripe rust resistance was low.

Taxon
Hybrid WheatTriticum aestivumt0
Interactor
stripe ruste0pathogen
Gene
detected QTLe0
Outcome
prediction accuracye0
Value
low for stripe ruste0

Verbatim

e0

“prediction accuracy of the detected QTL was moderate for leaf rust but low for stripe rust resistance”

✓ located verbatim in the source · abstract, characters 790–891
t0

“Hybrid Wheat”

✓ located verbatim in the source · title, characters 109–121

Title

Comparing the Potential of Marker-Assisted Selection and Genomic Prediction for Improving Rust Resistance in Hybrid Wheat

Ulrike Beukert; Patrick Thorwarth; Yusheng Zhao; et al. Frontiers in Plant Science 2020. doi:10.3389/fpls.2020.594113

Other claims from this paper (6)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The improved vision transformer achieved more precise detection of wheat stripe rust than previous methods.

Outcome
precise detection of wheat stripe ruste0
Comparator
previous methodse0

Verbatim

e0

“Using an improved vision transformer, we achieved precise detection of wheat stripe rust compared to previous methods”

✓ located verbatim in the source · abstract, characters 1917–2034

Title

An Enhanced Wheat Stripe Rust Segmentation Approach Using Vision Transformer Model

Nosheen Usman; Tauqir Ahmad; Faiza Iqbal; et al. International Journal of Computational Intelligence Systems 2025. doi:10.1007/s44196-025-00873-w

Other claims from this paper (4)
Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

At moderate incidence, the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components.

Interactor
Stripe Rustt0pathogen
Outcome
detection accuracy of SIFcanopy for wheat stripe ruste1
Comparator
NIRVP and SIFtot componentse1

Verbatim

t0

“Wheat Stripe Rust”

✓ located verbatim in the source · title, characters 85–102
e1

“the detection accuracy of SIFcanopy for wheat stripe rust was better than that of the NIRVP and SIFtot components”

✓ located verbatim in the source · abstract, characters 1525–1638

Title

Integrate the Canopy SIF and Its Derived Structural and Physiological Components for Wheat Stripe Rust Stress Monitoring

Xia Jing; Bingyu Li; Qixing Ye; et al. Remote Sensing 2022. doi:10.3390/rs14143427

Other claims from this paper (6)
Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Stripe Rust performance was 97.41%.

Outcome
Stripe Ruste0
Value
97.41%e0

Verbatim

e0

“Stripe Rust, Septoria Leaf Blotch, and Fusarium Leaf Blight being 97.41%, 97.45%, and 97.55%, respectively”

✓ located verbatim in the source · abstract, characters 655–761
t0

“Wheat”

✓ located verbatim in the source · title, characters 11–16

Title

AI-Powered Wheat Disease Detection: A CNN-Logistic Regression Framework

Khushdeep Kaur; Amrit Kumar Rai; Adamya Chhabra; et al. 2025 8th International Conference on Circuit, Power & Computing Technologies (ICCPCT) 2025. doi:10.1109/iccpct65132.2025.11176667

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

Claim · model-written summary

Genotypes combining stem and stripe rust resistance were identified as breeding donors.

Outcome
donor sources in breeding programse0
Value
combinations of stem rust and stripe rust resistancee0

Verbatim

e0

“Genotypes carrying combinations of stem rust and stripe rust resistance were identified for use as donor sources in breeding programs”

✓ located verbatim in the source · abstract, characters 1341–1474
t0

“Wheat”

✓ located verbatim in the source · title, characters 93–98

Title

Assessment of Genetic Diversity for Stem Rust and Stripe Rust Resistance in an International Wheat Nursery Using Phenotypic and Molecular Technologies

Bosco Chemayek; Urmil K. Bansal; Hanif Miah; et al. Uganda Journal of Agricultural Sciences 2021. doi:10.4314/ujas.v20i1.1

Other claims from this paper (6)

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