Illumination-invariant vegetation detection for a vision sensor-based agricultural applications

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In this paper, we propose a novel method, illumination-invariant vegetation detection (IVD), to improve many aspects of agriculture for vision-based autonomous machines or robots. The proposed method derives new color feature functions from simultaneously modeling the spectral properties of the color camera and scene illumination. An experiment in which an image sample dataset was acquired under nature illumination, including various intensities, weather conditions, shadows and reflections, was performed. The results show that the proposed method (IVD) yields the highest performance with the lowest error and standard deviation and is superior to six typical methods. Our method has multiple strengths, including computational simplicity and uniformly high-accuracy image segmentation. © 2021 Institute of Advanced Engineering and Science. All rights reserved.
Publisher
Institute of Advanced Engineering and Science
Issue Date
2021
Language
English
Article Type
Article
Citation

International Journal of Electrical and Computer Engineering, v.11, no.2, pp.1284 - 1292

ISSN
2088-8708
DOI
10.11591/ijece.v11i2.pp1284-1292
URI
http://hdl.handle.net/10203/290992
Appears in Collection
ME-Journal Papers(저널논문)
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