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    <identifier>10.57760/sciencedb.17930</identifier>
    <datestamp>2024-12-09T22:12:22Z</datestamp>
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  <dc:date>2024-12-09</dc:date>
  <dc:title>YGC-YOLO: An efficient and accurate detection network for young green citrus thinning detection in natural environments</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.17930</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Accurate detection of young green citrus in natural environments is crucial for intelligent thinning and yield estimation. However, challenges such as similar color to canopy leaves, small size, and environmental complexity often affect detection performance. To address this, an improved YOLOv8 model, called young green citrus-YOLO (YGC-YOLO), is proposed.</dc:description>
  <dc:subject>Object detection; Neural network; Young green citrus; YOLOv8n</dc:subject>
  <dc:creator>Zhenlun Chen</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
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  <dc:publisher>Science Data Bank</dc:publisher>
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