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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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    "keywords": [
      "Wheat rust", 
      "plant pathology", 
      "image analysis", 
      "K-mean algorithm"
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    "publication_date": "2019-12-19", 
    "creators": [
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        "affiliation": "John Innes Centre", 
        "name": "Vanessa Bueno-Sancho"
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        "affiliation": "John Innes Centre", 
        "name": "Pilar Corredor-Moreno"
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      {
        "affiliation": "John Innes Centre", 
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        "affiliation": "John Innes Centre", 
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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>"
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