Abstract
Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral–temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral–temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.
Highlights
Hyperspectral remote sensing has shown significant potential for the rapid, non-invasive assessment of crop diseases at different scales, ranging from single leaves (e.g., Mahlein et al, 2010; Ashourloo et al, 2014) to the canopy (e.g., Cao et al, 2013; Yu et al, 2018) to fields and regions (Wakie et al, 2016)
Artificial inoculations were effective in all plots, and the dataset was suitable for testing the feasibility of the classification of plots into healthy and diseased canopies based on reflectance spectra or spectral–temporal features
Large variation in the levels of Septoria tritici blotch (STB) could be observed among the inoculated plots, probably attributable to different levels of resistance, with the largest variation observed during late stay-green
Summary
Hyperspectral remote sensing has shown significant potential for the rapid, non-invasive assessment of crop diseases at different scales, ranging from single leaves (e.g., Mahlein et al, 2010; Ashourloo et al, 2014) to the canopy (e.g., Cao et al, 2013; Yu et al, 2018) to fields and regions (Wakie et al, 2016). To benefit crop breeding, new methods must accurately estimate phenotypes for large numbers of diverse genotypes under field conditions (Furbank and Tester, 2011; Araus and Cairns, 2014; Araus et al, 2018) This represents a significant challenge because genotypic diversity and contrasting environmental conditions are major sources of variation in spectral reflectance. Identified spectral features and corresponding thresholds or calibration curves are not sufficiently robust (i.e., universally applicable) for use in resistance breeding Due to such difficulties, high throughput phenotyping of disease resistance under field conditions using hyperspectral reflectance is still elusive (Araus et al, 2018)
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