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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Plant Sci. | doi: 10.3389/fpls.2019.01355

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

  • 1ETH Zürich, Switzerland

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 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.

Keywords: High-throughput phenotyping (HTP), field-based phenotyping (FBP), Feature engineering, Feature Selection, Spectral vegetation index (SVI)

Received: 21 May 2019; Accepted: 02 Oct 2019.

Copyright: © 2019 Anderegg, Hund, Karisto and Mikaberidze. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Jonas Anderegg, ETH Zürich, Zurich, Switzerland,