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.
Highlights
Septoria tritici blotch (STB), caused by a fungal pathogen Zymoseptoria tritici, is one of the most damaging foliar diseases in wheat, causing crop yield losses and resulting in higher production costs due to fungicide applications (Fones and Gurr, 2015; Torriani et al, 2015)
In this study we investigated the feasibility of using canopy hyperspectral data to detect wheat STB infection in a population of 335 elite European wheat varieties
Our results demonstrated that hyperspectral indices used for the estimation of leaf chlorophyll and water status were negatively related to the canopy and leaf STB metrics, whereas the senescence related spectral indices were positively related
Summary
Septoria tritici blotch (STB), caused by a fungal pathogen Zymoseptoria tritici, is one of the most damaging foliar diseases in wheat, causing crop yield losses and resulting in higher production costs due to fungicide applications (Fones and Gurr, 2015; Torriani et al, 2015). There remains a lack of investigations and/or applications of these technologies to advance breeding by improving the efficiency of selection in large, genetically diverse plant populations under field conditions (Deery et al, 2014; Walter et al, 2015). Advances in breeding for disease resistance are often limited by a lack of available resistant varieties and can be constrained by deficiencies associated with conventional phenotyping methods—for instance, the precision, reproducibility, and throughput of conventional visual assessments (Steddom et al, 2005; O’Driscoll et al, 2014; Mahlein, 2016). To better meet the specific needs of crop breeders, remote, and proximal sensing techniques will need to generate accurate plant phenotypes in genetically diverse populations under field conditions (Furbank and Tester, 2011; Kirchgessner et al, 2017; Yang et al, 2017)
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