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Blueberry Orchard Delineation with High-Resolution Imagery and Self-Organizing Map Neural Image Classification

Author

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  • Sudhanshu S Panda Author-Workplace- Institute of Environmental Spatial Analysis, University of North Georgia, USA
  • Gerrit Hoogenboom Author-Workplace- Agricultural and Biological Engineering, University of Florida, USA
  • Joel Paz Author-Workplace- Agricultural and Biological Engineering, University of Florida, USA

Abstract

Blueberry is the second most important fruit and nut crop in Georgia after pecans. The number of blueberry orchards for commercial production has increased in Georgia and other southeastern states during the past few years. Blueberry orchards are generally established in the area that has just been cleared from shrubs and forests. Most of the blueberry orchards in southeast Georgia are surrounded by pine forest. It is, therefore, difficult to distinguish blueberry bushes from other trees and shrubs with low resolution imagery such as 30 m Landsat TM or ETM. Many unmanaged blueberry orchards are generally also covered with tall grasses, making it more difficult to distinguish the bushes with low resolution imageries. The main objective of this study was to use advanced image processing techniques and high resolution multispectral imagery to distinguish blueberries orchards from other land-uses

Suggested Citation

  • Sudhanshu S Panda Author-Workplace- Institute of Environmental Spatial Analysis, University of North Georgia, USA & Gerrit Hoogenboom Author-Workplace- Agricultural and Biological Engineering, Univers, 2016. "Blueberry Orchard Delineation with High-Resolution Imagery and Self-Organizing Map Neural Image Classification," Agricultural Research & Technology: Open Access Journal, Juniper Publishers Inc., vol. 3(1), pages 08-18, December.
    • Handle: RePEc:adp:artoaj:v:3:y:2016:i:1:p:08-18
    DOI: 10.19080/ARTOAJ.2016.03.555602
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