Dataset Information

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

ABSTRACT: It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

SUBMITTER: Wang H

PROVIDER: S-EPMC4851363 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

Wang Hui H Qin Feng F Ruan Liu L Wang Rui R Liu Qi Q Ma Zhanhong Z Li Xiaolong X Cheng Pei P Wang Haiguang H

PloS one 20160429 4

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares ( ...[more]

PMID: 27128464

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Project description:Leaf rust and stripe rust are important wheat diseases worldwide causing significant losses where susceptible varieties are grown. Resistant cultivars offer long-term control and reduce the use of hazardous chemicals, which can be detrimental to both human health and the environment. Land races have been a valuable resource for mining new genes for various abiotic and biotic stresses including wheat rusts. Afghan wheat landrace "KU3067" displayed high seedling infection type (IT) for leaf rust and low IT for stripe rust; however, it displayed high levels of field resistance for both rusts when tested for multiple seasons against the Mexican rust isolates. This study focused on identifying loci-conferring seedling resistance to stripe rust, and also loci-conferring adult plant resistance (APR) against the Mexican races of leaf rust and stripe rust. A backcrossed inbred line (BIL) population advanced to the BC1F5 generation derived from the cross of KU3067 and Apav (triple rust susceptible line) was used for both, inheritance and QTL mapping studies. The population and parents were genotyped with Diversity Arrays Technology-genotyping-by-sequencing (DArT-Seq) and phenotyped for leaf rust and stripe rust response at both seedling and adult plant stages during multiple seasons in Mexico with relevant pathotypes. Mapping results identified an all-stage resistance gene for stripe rust, temporarily designated as YrKU, on chromosome 7BL. In total, six QTL-conferring APR to leaf rust on 1AS, 2AL, 4DL, 6BL, 7AL, and 7BL, and four QTL for stripe rust resistance on 1BS, 2AL, 4DL, and 7BL were detected in the analyses. Among these, pleiotropic gene Lr67/Yr46 on 4DL with a significantly large effect is the first report in an Afghan landrace-conferring resistance to both leaf and stripe rusts. QLr.cim-7BL/YrKU showed pleiotropic resistance to both rusts and explained 7.5-17.2 and 12.6-19.3% of the phenotypic variance for leaf and stripe rusts, respectively. QYr.cim-1BS and QYr.cim-2AL detected in all stripe environments with phenotypic variance explained (PVE) 12.9-20.5 and 5.4-12.5%, and QLr.cim-6BL are likely to be new. These QTL and their closely linked markers will be useful for fine mapping and marker-assisted selection (MAS) in breeding for durable resistance to multiple rust diseases.

Project description:Among several important wheat foliar diseases, Stripe rust (YR), Leaf rust (LR), and Stem rust (SR) have always been an issue of concern to the farmers and wheat breeders. Evolution of virulent pathotypes of these rusts has posed frequent threats to an epidemic. Pyramiding rust-resistant genes are the most economical and environment-friendly approach in postponing this inevitable threat. To achieve durable long term resistance against the three rusts, an attempt in this study was made searching for novel sources of resistant alleles in a panel of 483 spring wheat genotypes. This is a unique and comprehensive study where evaluation of a diverse panel comprising wheat germplasm from various categories and adapted to different wheat agro-climatic zones was challenged with 18 pathotypes of the three rusts with simultaneous screening in field conditions. The panel was genotyped using 35K SNP array and evaluated for each rust at two locations for two consecutive crop seasons. High heritability estimates of disease response were observed between environments for each rust type. A significant effect of population structure in the panel was visible in the disease response. Using a compressed mixed linear model approach, 25 genomic regions were found associated with resistance for at least two rusts. Out of these, seven were associated with all the three rusts on chromosome groups 1 and 6 along with 2B. For resistance against YR, LR, and SR, there were 16, 18, and 27 QTL (quantitative trait loci) identified respectively, associated at least in two out of four environments. Several of these regions got annotated with resistance associated genes viz. NB-LRR, E3-ubiquitin protein ligase, ABC transporter protein, etc. Alien introgressed (on 1B and 3D) and pleiotropic (on 7D) resistance genes were captured in seedling and adult plant disease responses, respectively. The present study demonstrates the use of genome-wide association for identification of a large number of favorable alleles for leaf, stripe, and stem rust resistance for broadening the genetic base. Quick conversion of these QTL into user-friendly markers will accelerate the deployment of these resistance loci in wheat breeding programs.

Dataset Information

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

Publications

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

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