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Color Image Segmentation with Genetic Algorithm for In-field Weed Sensing
Transactions of the ASAE
  • Lie Tang, University of Illinois at Urbana-Champaign
  • Lei F. Tian, University of Illinois at Urbana-Champaign
  • Brian L. Steward, Iowa State University
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Publication Version
Published Version
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This study was undertaken to develop machine vision-based weed detection technology for outdoor natural lighting conditions. Supervised color image segmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-Intensity (HSI) color space (GAHSI) for outdoor field weed sensing was successfully implemented. Images from two extreme intensity lighting conditions, those under sunny and cloudy sky conditions, were mosaicked to explore the possibility of using GAHSI to locate a plant region in color space when these two extremes were presented simultaneously. The GAHSI result provided evidence for the existence and separability of such a region. In the experiment, GAHSI performance was measured by comparing the GAHSI-segmented image with a corresponding handsegmented reference image. When compared with cluster analysis-based segmentation results, the GAHSI achieved equivalent performance.

This article is from Transactions of the ASAE, 43, no. 4 (2000): 1019–1027.

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American Society of Agricultural Engineers
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Citation Information
Lie Tang, Lei F. Tian and Brian L. Steward. "Color Image Segmentation with Genetic Algorithm for In-field Weed Sensing" Transactions of the ASAE Vol. 43 Iss. 4 (2000) p. 1019 - 1027
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