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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Shape features distinguish leaf rust, stripe rust and stem rust in wheat images.

Outcome
leaf rust, stripe rust and stem rust are identifiede0
Comparator
leaf rust, stripe rust and stem ruste0

Verbatim

e0

“according to the shape characteristics, leaf rust, stripe rust and stem rust are identified”

✓ located verbatim in the source · abstract, characters 731–822
t0

“wheat leaf”

✓ located verbatim in the source · title, characters 69–79

Title

Research on feature extraction and recognition of common diseases of wheat leaf

Wang Meil Computer Engineering and Applications Journal 2014

Other claims from this paper (2)
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 resultmeasured

Claim · model-written summary

The meteorological forecast model for wheat stripe rust was established.

Outcome
forecasting wheat stripe ruste0

Not reportedComparatorValue

Verbatim

e0

“The model corresponding to meteorological conditions for forecasting wheat stripe rust was successfully established”

✓ located verbatim in the source · abstract, characters 661–776

Title

Forecast Model for Occurrence Degree of Wheat Stripe Rust Using Meteorological Data

Wei-Chang Liu Anhui nongye kexue 2008

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

Claim · model-written summary

RBF SVM recognized wheat stripe rust with 100% test accuracy.

Outcome
recognition rates of wheat stripe ruste0
Value
100%e0
Qualifier
tested setse0

Verbatim

t0

“wheat stripe rust”

✓ located verbatim in the source · title, characters 21–38
e0

“the recognition rates of wheat stripe rust and wheat leaf rust were both 96.67% for the training sets,and 100% for the tested sets”

✓ located verbatim in the source · abstract, characters 803–933

Title

Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine

Haiguang Wang Zhongguo Nongye Daxue xuebao 2012

Other claims from this paper (4)
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

RBF SVM recognized wheat stripe rust with 96.67% training accuracy.

Outcome
recognition rates of wheat stripe ruste0
Value
96.67%e0
Qualifier
training setse0

Verbatim

t0

“wheat stripe rust”

✓ located verbatim in the source · title, characters 21–38
e0

“the recognition rates of wheat stripe rust and wheat leaf rust were both 96.67% for the training sets,and 100% for the tested sets”

✓ located verbatim in the source · abstract, characters 803–933

Title

Image recognition of wheat stripe rust and wheat leaf rust based on support vector machine

Haiguang Wang Zhongguo Nongye Daxue xuebao 2012

Other claims from this paper (4)
Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

SCAR markers were useful for selecting Thinopyrum stripe rust resistance.

Outcome
select stripe rust resistancee0
Qualifier
SCAR markerse0

Not reportedTaxonComparatorValue

Verbatim

e0

“The SCAR markers are useful to select stripe rust resistance from Th. intermedium.”

✓ located verbatim in the source · abstract, characters 1367–1449

Title

Identification, Characterization, and Evaluation of Novel Stripe Rust-Resistant Wheat–Thinopyrum intermedium Chromosome Translocation Lines

Xingwei Zheng; Caiguo Tang; Ran Han; et al. Plant Disease 2019. doi:10.1094/pdis-01-19-0001-re

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

Claim · model-written summary

An effective method for identifying stripe rust resistance was established.

Outcome
identification and evolution of resistance to stripe ruste0

Not reportedComparatorValue

Verbatim

e0

“An effective method was established for identification and evolution of resistance to stripe rust”

✓ located verbatim in the source · abstract, characters 1383–1480
t0

“Wheat”

✓ located verbatim in the source · title, characters 67–72

Title

Identification and Evaluation of Resistance to Stripe Rust in 1980 Wheat Landraces and Abroad Germplasm

Kang Zhen-sheng Zhongguo nongye Kexue 2012

Other claims from this paper (9)

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