Abstract
Producing quantitative and reliable measures of crop disease is essential for resistance breeding, but is challenging and time consuming using traditional phenotyping methods. Hyperspectral remote sensing has shown potential for the detection of plant diseases, but its utility for phenotyping large and diverse populations of plants under field conditions requires further evaluation. In this study, we collected canopy hyperspectral data from 335 wheat varieties using a spectroradiometer, and we investigated the use of canopy reflectance for detecting the Septoria tritici blotch (STB) disease and for quantifying the severity of infection. Canopy- and leaf-level infection metrics of STB based on traditional visual assessments and automated analyses of leaf images were used as ground truth data. Results showed (i) that canopy reflectance and the selected spectral indices show promise for quantifying STB infections, and (ii) that the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices. Moreover, partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics. The PLS discriminant analysis (PLSDA) model calibrated based on the spectral data of four reference varieties was able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60). Finally, the PLSDA model predictions allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment. This study demonstrates the great potential of using canopy hyperspectral remote sensing to improve foliar disease assessment and to facilitate plant breeding for disease resistance.
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Claims 5
Claim · model-written summary
The normalized difference water index performed best among spectral indices for detecting Septoria tritici blotch.
- Taxon
- wheat
Triticum aestivume0 - Interactor
- Septoria tritici
Zymoseptoria triticie2pathogen - Outcome
- detecting STBe1
- Direction
- best performancee1
- Comparator
- other spectral indicese1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we collected canopy hyperspectral data from 335 wheat varieties”
e1“the normalized difference water index (NDWI) showed the best performance in detecting STB compared to other spectral indices”
e2“for detecting the Septoria tritici blotch (STB) disease”
Claim · model-written summary
Canopy reflectance and spectral indices show promise for quantifying Septoria tritici blotch infection.
- Taxon
- wheatTriticum aestivume0
- Interactor
- Septoria triticiZymoseptoria triticie3pathogen
- Outcome
- quantifying STB infectionse2
- Qualifier
- show promisee2
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we collected canopy hyperspectral data from 335 wheat varieties”
e2“canopy reflectance and the selected spectral indices show promise for quantifying STB infections”
e3“for detecting the Septoria tritici blotch (STB) disease”
Claim · model-written summary
PLS regression improved the prediction of Septoria tritici blotch metrics.
- Taxon
- wheatTriticum aestivume0
- Interactor
- Septoria triticiZymoseptoria triticie4pathogen
- Outcome
- prediction of STB metricse3
- Direction
- an improvemente3
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we collected canopy hyperspectral data from 335 wheat varieties”
e3“partial least squares (PLS) regression models allowed for an improvement in the prediction of STB metrics”
e4“for detecting the Septoria tritici blotch (STB) disease”
Claim · model-written summary
The PLS discriminant analysis classified diseased and healthy wheat canopies with 93% accuracy (Kappa = 0.60).
- Taxon
- wheatTriticum aestivume0
- Outcome
- accuracye5
- Value
- 93%e5
- Qualifier
- Kappa = 0.60e5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“we collected canopy hyperspectral data from 335 wheat varieties”
e5“able to discriminate between the diseased and healthy canopies among the 335 varieties with an accuracy of 93% (Kappa = 0.60)”
Claim · model-written summary
PLS model predictions identified wheat genotypes potentially more susceptible to Septoria tritici blotch, confirmed by visual assessment.
- Taxon
- wheatTriticum aestivume6
- Interactor
- Septoria triticiZymoseptoria triticie7pathogen
- Outcome
- identification of wheat genotypes that are potentially more susceptible to STBe6
- Comparator
- STB visual assessmente6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e6“allowed for the identification of wheat genotypes that are potentially more susceptible to STB, which was confirmed by the STB visual assessment”
e7“for detecting the Septoria tritici blotch (STB) disease”