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In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features

Anderegg, J., Hund, A., Karisto, P. and Mikaberidze, A. (2019) In-field detection and quantification of Septoria tritici blotch in diverse wheat germplasm using spectral-temporal features. Frontiers in Plant Science, 10. 1355. ISSN 1664-462X

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To link to this item DOI: 10.3389/fpls.2019.01355


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 whilst 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 >300 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.

Item Type:Article
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of Crop Science
ID Code:86585


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