October 1, 2021 Thesis Open Access

ELSABET MEKONEN

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  <identifier identifierType="DOI">10.20372/nadre:4700</identifier>
  <creators>
    <creator>
      <creatorName>ELSABET MEKONEN</creatorName>
    </creator>
  </creators>
  <titles>
    <title>DEVELOPING FALL ARMYWORM MAIZE INSECT PEST DETECTION MODEL USING MACHINE LEARNING APPROACH</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <dates>
    <date dateType="Issued">2021-10-01</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Thesis</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://nadre.ethernet.edu.et/record/4700</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.20372/nadre:4699</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/aastu</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://nadre.ethernet.edu.et/communities/zenodo</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="http://www.opendefinition.org/licenses/cc-by">Creative Commons Attribution</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Fall armyworm corn insect pests are gaining great attention in the agricultural industry due to their great devastation effect on maize production yield. However, the diversity of the maize insect pests in the agricultural field make difficulties in the identification of fall armyworm maize insect pests from other maize insect pests especially corn earworm and army cutworm at the larva stage. Besides the existing manual fall armyworm insect pest detection and monitoring system is time consuming, labor intensive and needs agricultural experts engagement. Nearly 30% of maize production yield loss is recorded in the country level nowadays. To combat this problem, there is a need to come up with an early automated detection and classification model. Therefore this study implemented a novel methods of Convolutional Neural Network model using keras (tensorflow backend) deep learning framework to detect and classify fall armyworm maize insect pests from other maize insect pests at larva stage using less computing power and time. Thus considering the detection and classification approaches, we have developed fall armyworm maize insect pest detection model that is very capable regardless of different limitations such as high and poor image quality, complex background or lightening condition, the variety in size, shape, angle and feature of fall armyworm maize insect pests&lt;/p&gt;</description>
  </descriptions>
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