Technical note Open Access

K-PIE: using K-means algorithm for Percentage Infection symptoms Estimation

Vanessa Bueno-Sancho; Pilar Corredor-Moreno; Ngonidzashe Kangara; Diane G.O. Saunders


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  <identifier identifierType="DOI">10.5281/zenodo.3584148</identifier>
  <creators>
    <creator>
      <creatorName>Vanessa Bueno-Sancho</creatorName>
      <affiliation>John Innes Centre</affiliation>
    </creator>
    <creator>
      <creatorName>Pilar Corredor-Moreno</creatorName>
      <affiliation>John Innes Centre</affiliation>
    </creator>
    <creator>
      <creatorName>Ngonidzashe Kangara</creatorName>
      <affiliation>John Innes Centre</affiliation>
    </creator>
    <creator>
      <creatorName>Diane G.O. Saunders</creatorName>
      <affiliation>John Innes Centre</affiliation>
    </creator>
  </creators>
  <titles>
    <title>K-PIE: using K-means algorithm for Percentage Infection symptoms Estimation</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2019</publicationYear>
  <subjects>
    <subject>Wheat rust</subject>
    <subject>plant pathology</subject>
    <subject>image analysis</subject>
    <subject>K-mean algorithm</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2019-12-19</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Technical note</resourceType>
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    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3584148</alternateIdentifier>
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  <version>Version 1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The K-means algorithm is one of the most effective clustering methods that has been widely used in plant disease detection. Herein, we developed a script termed K-PIE (K-means algorithm for Percentage Infection symptoms Estimation) that utilises the k-means algorithm to analyse images of both yellow and stem rust infected wheat leaves to estimate the percentage of disease symptoms based on colour analysis.&lt;/p&gt;</description>
  </descriptions>
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    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/715638/">715638</awardNumber>
      <awardTitle>Decoding the molecular mechanisms driving host adaptation of yellow rust on cereal crops and grasses</awardTitle>
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    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/674964/">674964</awardNumber>
      <awardTitle>CEREALPATH - Training in innovative and integrated control of cereal diseases</awardTitle>
    </fundingReference>
    <fundingReference>
      <funderName>Research Councils UK</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000690</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/RCUK/BBSRC/BB%2FM011216%2F1/">BB/M011216/1</awardNumber>
      <awardTitle>The Norwich Research Park Biosciences Doctoral Training Partnership</awardTitle>
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