Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.
Identifieur interne : 000140 ( Main/Exploration ); précédent : 000139; suivant : 000141Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.
Auteurs : Kaitlin M. Gold [États-Unis] ; Philip A. Townsend [États-Unis] ; Ittai Herrmann [Israël] ; Amanda J. Gevens [États-Unis]Source :
- Plant science : an international journal of experimental plant biology [ 1873-2259 ] ; 2020.
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.
DOI: 10.1016/j.plantsci.2019.110316
PubMed: 32534618
Affiliations:
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Townsend</name><affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, Department of Forestry and Wildlife Ecology, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Wisconsin-Madison, Department of Forestry and Wildlife Ecology</wicri:regionArea><wicri:noRegion>Department of Forestry and Wildlife Ecology</wicri:noRegion></affiliation></author><author><name sortKey="Herrmann, Ittai" sort="Herrmann, Ittai" uniqKey="Herrmann I" first="Ittai" last="Herrmann">Ittai Herrmann</name><affiliation wicri:level="1"><nlm:affiliation>The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel.</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. 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Gold</name><affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, Department of Plant Pathology, United States. Electronic address: kmorey@wisc.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Wisconsin-Madison, Department of Plant Pathology</wicri:regionArea><wicri:noRegion>Department of Plant Pathology</wicri:noRegion></affiliation></author><author><name sortKey="Townsend, Philip A" sort="Townsend, Philip A" uniqKey="Townsend P" first="Philip A" last="Townsend">Philip A. Townsend</name><affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, Department of Forestry and Wildlife Ecology, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Wisconsin-Madison, Department of Forestry and Wildlife Ecology</wicri:regionArea><wicri:noRegion>Department of Forestry and Wildlife Ecology</wicri:noRegion></affiliation></author><author><name sortKey="Herrmann, Ittai" sort="Herrmann, Ittai" uniqKey="Herrmann I" first="Ittai" last="Herrmann">Ittai Herrmann</name><affiliation wicri:level="1"><nlm:affiliation>The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel.</nlm:affiliation><country xml:lang="fr">Israël</country><wicri:regionArea>The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001</wicri:regionArea><wicri:noRegion>Rehovot 7610001</wicri:noRegion></affiliation></author><author><name sortKey="Gevens, Amanda J" sort="Gevens, Amanda J" uniqKey="Gevens A" first="Amanda J" last="Gevens">Amanda J. Gevens</name><affiliation wicri:level="1"><nlm:affiliation>University of Wisconsin-Madison, Department of Plant Pathology, United States.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>University of Wisconsin-Madison, Department of Plant Pathology</wicri:regionArea><wicri:noRegion>Department of Plant Pathology</wicri:noRegion></affiliation></author></analytic><series><title level="j">Plant science : an international journal of experimental plant biology</title><idno type="eISSN">1873-2259</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">32534618</PMID><DateRevised><Year>2020</Year><Month>09</Month><Day>30</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-2259</ISSN><JournalIssue CitedMedium="Internet"><Volume>295</Volume><PubDate><Year>2020</Year><Month>Jun</Month></PubDate></JournalIssue><Title>Plant science : an international journal of experimental plant biology</Title><ISOAbbreviation>Plant Sci</ISOAbbreviation></Journal><ArticleTitle>Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning.</ArticleTitle><Pagination><MedlinePgn>110316</MedlinePgn></Pagination><ELocationID EIdType="pii" ValidYN="Y">S0168-9452(19)30891-X</ELocationID><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.plantsci.2019.110316</ELocationID><Abstract><AbstractText>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.</AbstractText><CopyrightInformation>Copyright © 2019 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gold</LastName><ForeName>Kaitlin M</ForeName><Initials>KM</Initials><AffiliationInfo><Affiliation>University of Wisconsin-Madison, Department of Plant Pathology, United States. Electronic address: kmorey@wisc.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Townsend</LastName><ForeName>Philip A</ForeName><Initials>PA</Initials><AffiliationInfo><Affiliation>University of Wisconsin-Madison, Department of Forestry and Wildlife Ecology, United States.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Herrmann</LastName><ForeName>Ittai</ForeName><Initials>I</Initials><AffiliationInfo><Affiliation>The Robert H. Smith Institute for Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 7610001, Israel.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gevens</LastName><ForeName>Amanda J</ForeName><Initials>AJ</Initials><AffiliationInfo><Affiliation>University of Wisconsin-Madison, Department of Plant Pathology, United States.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>11</Month><Day>13</Day></ArticleDate></Article><MedlineJournalInfo><Country>Ireland</Country><MedlineTA>Plant Sci</MedlineTA><NlmUniqueID>9882015</NlmUniqueID><ISSNLinking>0168-9452</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Cultivar discrimination</Keyword><Keyword MajorTopicYN="N">Hyperspectral</Keyword><Keyword MajorTopicYN="N">Machine learning</Keyword><Keyword MajorTopicYN="N">Phytophthorainfestans</Keyword><Keyword MajorTopicYN="N">Reflectance spectroscopy</Keyword></KeywordList><CoiStatement>Declaration of Competing Interest The authors declare no competing interests.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>06</Month><Day>28</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>10</Month><Day>10</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>10</Month><Day>14</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>6</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32534618</ArticleId><ArticleId IdType="pii">S0168-9452(19)30891-X</ArticleId><ArticleId IdType="doi">10.1016/j.plantsci.2019.110316</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Israël</li><li>États-Unis</li></country></list><tree><country name="États-Unis"><noRegion><name sortKey="Gold, Kaitlin M" sort="Gold, Kaitlin M" uniqKey="Gold K" first="Kaitlin M" last="Gold">Kaitlin M. 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