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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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{
  "description": "<p>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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "John Innes Centre", 
      "@type": "Person", 
      "name": "Vanessa Bueno-Sancho"
    }, 
    {
      "affiliation": "John Innes Centre", 
      "@type": "Person", 
      "name": "Pilar Corredor-Moreno"
    }, 
    {
      "affiliation": "John Innes Centre", 
      "@type": "Person", 
      "name": "Ngonidzashe Kangara"
    }, 
    {
      "affiliation": "John Innes Centre", 
      "@type": "Person", 
      "name": "Diane G.O. Saunders"
    }
  ], 
  "headline": "K-PIE: using K-means algorithm for Percentage Infection symptoms Estimation", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2019-12-19", 
  "url": "https://zenodo.org/record/3584148", 
  "version": "Version 1.0", 
  "keywords": [
    "Wheat rust", 
    "plant pathology", 
    "image analysis", 
    "K-mean algorithm"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3584148", 
  "@id": "https://doi.org/10.5281/zenodo.3584148", 
  "@type": "ScholarlyArticle", 
  "name": "K-PIE: using K-means algorithm for Percentage Infection symptoms Estimation"
}
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