1 Ocak 2024 Dergi makalesi Açık Erişim

Tunca, Emre; Koksal, Eyuep Selim; Ozturk, Elif; Akay, Hasan; Taner, Sakine letin

MARC21 XML

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Koksal, Eyuep Selim</subfield>
    <subfield code="u">Ondokuz Mayis Univ, Fac Agr, Dept Agr Struct &amp; Irrigat, Samsun, Turkiye</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Ozturk, Elif</subfield>
    <subfield code="u">Ondokuz Mayis Univ, Fac Agr, Dept Field Crops, Samsun, Turkiye</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Akay, Hasan</subfield>
    <subfield code="u">Ondokuz Mayis Univ, Fac Agr, Dept Field Crops, Samsun, Turkiye</subfield>
  </datafield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="a">Taner, Sakine letin</subfield>
    <subfield code="u">Ondokuz Mayis Univ, Fac Agr, Dept Agr Struct &amp; Irrigat, Samsun, Turkiye</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="c">10</subfield>
    <subfield code="p">PHYSICS AND CHEMISTRY OF THE EARTH</subfield>
    <subfield code="v">133</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="a">Creative Commons Attribution</subfield>
    <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.1016/j.pce.2023.103537</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Accurate leaf area index estimation in sorghum using high-resolution UAV data and machine learning models</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="a">Tunca, Emre</subfield>
    <subfield code="u">Duzce Univ, Fac Agr, Dept Biosyst Engn, TR-81620 Duzce, Turkiye</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="o">oai:aperta.ulakbim.gov.tr:273532</subfield>
    <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="2">opendefinition.org</subfield>
    <subfield code="a">cc-by</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2024-01-01</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="u">https://aperta.ulakbim.gov.trrecord/273532/files/bib-d40fd499-cd54-4323-bc76-ca24d54fd093.txt</subfield>
    <subfield code="z">md5:e5364ce760768e28dc7b9fd980827aa3</subfield>
    <subfield code="s">227</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <controlfield tag="005">20240608070212.0</controlfield>
  <controlfield tag="001">273532</controlfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;Accurate estimation of leaf area index (LAI) is essential for precision agriculture, yet traditional ground-based measurements are destructive, time-consuming, and limited in scale. This study aimed to address the need for rapid, non-destructive LAI monitoring over large areas by evaluating unmanned aerial vehicle (UAV) data and machine learning (ML) models. A field experiment with four irrigation treatments was conducted to obtain wide range of LAI values over two years. Multispectral and thermal UAV images were acquired throughout the growing season along with destructive LAI measurements. Five ML algorithms, including K-Nearest Neighbors (K-NN), Extra Trees Regressor (ETR), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR) were tested. Also, feature selection procedures were implemented to obtain useful information among the features used for the ML model. Results showed the K-NN model achieved the highest accuracy (R2 = 0.97, RMSE = 0.46, MAE = 0.197), followed by ETR. The analysis of feature selection revealed that the combination of Normalized Difference Vegetation Index (NDVI) and canopy height (NDVI x Hc) product had the highest importance, followed by Soil-Adjusted Vegetation Index (SAVI) and Green Normalized Difference Vegetation Index (GNDVI). Also, utilizing vegetation indices calculated from multiple spectral bands proved to be more effective than using individual bands alone. Overall, the study demonstrates that UAV data and ML techniques can estimate sorghum LAI precisely to support precision agriculture applications. Moreover, using low-cost UAVs equipped with multispectral sensors presents a cost-effective and reliable method for LAI estimation using ML models.&lt;/p&gt;</subfield>
  </datafield>
</record>