Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Kaitlin M. Gold*, Philip A. Townsend, Ittai Herrmann, Amanda J. Gevens

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

58 Scopus citations

Abstract

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms. Here, we tested differences in spectral response of four potato cultivars, including two cultivars with a shared genotypic background except for a single copy of a resistance gene, to inoculation with Phytophthora infestans clonal lineage US-23 using three statistical approaches: random forest discrimination (RF), partial least squares discrimination analysis (PLS-DA), and normalized difference spectral index (NDSI). We find that cultivar, or plant genotype, has a significant impact on spectral reflectance of plants undergoing P. infestans infection. The spectral response of four potato cultivars to infection by Phytophthora infestans clonal lineage US-23 was highly variable, yet with important shared characteristics that facilitated discrimination. Early disease physiology was found to be variable across cultivars as well using non-destructively derived PLS-regression trait models. This work lays the foundation to better understand host-pathogen interactions across a variety of genotypic backgrounds, and establishes that host genotype has a significant impact on spectral reflectance, and hence on biochemical and physiological traits, of plants undergoing pathogen infection.

Original languageAmerican English
Article number110316
JournalPlant Science
Volume295
DOIs
StatePublished - Jun 2020

Bibliographical note

Publisher Copyright:
© 2019 Elsevier B.V.

Keywords

  • Cultivar discrimination
  • Hyperspectral
  • Machine learning
  • Phytophthorainfestans
  • Reflectance spectroscopy

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