9 Ocak 2023 Konferans bildirisi Açık Erişim

Güzel Mustafa; Turan Bülent; Şin Bahadır; Baştürk Alper

DataCite XML

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  <identifier identifierType="DOI">10.48623/aperta.252239</identifier>
  <creators>
    <creator>
      <creatorName>Güzel Mustafa</creatorName>
      <affiliation>Tokat Gaziosmanpaşa Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Turan Bülent</creatorName>
      <affiliation>Tokat Gaziosmanpaşa Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Şin Bahadır</creatorName>
      <affiliation>Sakarya Uygulamalı Bilimler Üniversitesi</affiliation>
    </creator>
    <creator>
      <creatorName>Baştürk Alper</creatorName>
      <affiliation>Erciyes Üniversitesi</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Counting Of Weed Numbers In Farms By Deep Learning-Strongsort</title>
  </titles>
  <publisher>Aperta</publisher>
  <publicationYear>2023</publicationYear>
  <subjects>
    <subject>Weeds counting</subject>
    <subject>deep learning</subject>
    <subject>StrongSORT</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2023-01-09</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://aperta.ulakbim.gov.tr/record/252239</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.48623/aperta.252238</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivatives</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The knowledge of weed numbers is very helpful for many studies due to minimizing weed harm&lt;br&gt;
on the crops as well as knowing the weed species and classes. In this study, we used a deep learning&lt;br&gt;
architecture that was capable of detecting some weeds to count the weed numbers instead of classical&lt;br&gt;
manual weed counting methods. The pre-trained deep learning weight belongs to YOLOv5 which&lt;br&gt;
is used in this study, can detect 5 different phenological terms (cotyledon leaves period, 3-5 leaves&lt;br&gt;
period, pre-flowering period, flowering period, and fruit and seed setting period) of some harmful weeds&lt;br&gt;
(sherlock mustard-Sinapis arvensis L., creeping thistle-Cirsium arvense L. Scop, and forking larkspur-&lt;br&gt;
Consolida regalis Gray) in wheat production and other crops with 98% highest accuracy. StrongSORT&lt;br&gt;
with the OSNet tool is used as the multi-object tracker. The weeds successfully counted from any image&lt;br&gt;
resources (image, video, webcam, etc.) while avoiding recounting the same object by computer vision. It&lt;br&gt;
plays an important role in the studies aimed to understand weeds population spread, resistance gaining to&lt;br&gt;
herbicides by weeds, the economical threshold of weeds, etc. It also provides these parameters cheaper&lt;br&gt;
and faster than classical methods.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>Türkiye Bilimsel ve Teknolojik Araştirma Kurumu</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">https://doi.org/10.13039/501100004410</funderIdentifier>
      <awardNumber>120O888</awardNumber>
    </fundingReference>
  </fundingReferences>
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