Journal article Open Access

Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV

Predrag Randelović; Vuk Ðorđević; Stanko Milić; Svetlana Balešević-Tubić; Kristina Petrović; Jegor Miladinović; Vojin Ðukić


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  <identifier identifierType="URL">https://zenodo.org/record/3978167</identifier>
  <creators>
    <creator>
      <creatorName>Predrag Randelović</creatorName>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Vuk Ðorđević</creatorName>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Stanko Milić</creatorName>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Svetlana Balešević-Tubić</creatorName>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Kristina Petrović</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6877-5521</nameIdentifier>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Jegor Miladinović</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4061-4037</nameIdentifier>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
    <creator>
      <creatorName>Vojin Ðukić</creatorName>
      <affiliation>Institute of Field and Vegetable Crops, Maksima Gorkog 30, Novi Sad 21000, Serbia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Prediction of Soybean Plant Density Using a Machine Learning Model and Vegetation Indices Extracted from RGB Images Taken with a UAV</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>soybean</subject>
    <subject>machine learning</subject>
    <subject>vegetation indices</subject>
    <subject>UAV</subject>
    <subject>RGB images</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-07-31</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Journal article</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3978167</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.3390/agronomy10081108</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/ecobreed</relatedIdentifier>
  </relatedIdentifiers>
  <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>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m&lt;sup&gt;2&lt;/sup&gt;). Model validation resulted in the correlation coefficient&amp;mdash;R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/771367/">771367</awardNumber>
      <awardTitle>Increasing the efficiency and competitiveness of organic crop breeding</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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