Color image segmentation with genetic algorithm for in-field weed sensing

Citation
L. Tang et al., Color image segmentation with genetic algorithm for in-field weed sensing, T ASAE, 43(4), 2000, pp. 1019-1027
Citations number
20
Categorie Soggetti
Agriculture/Agronomy
Journal title
TRANSACTIONS OF THE ASAE
ISSN journal
00012351 → ACNP
Volume
43
Issue
4
Year of publication
2000
Pages
1019 - 1027
Database
ISI
SICI code
0001-2351(200007/08)43:4<1019:CISWGA>2.0.ZU;2-P
Abstract
This study was undertaken to develop machine vision-based weed detection te chnology for outdoor natural lighting conditions. Supervised color image se gmentation using a binary-coded genetic algorithm (GA) identifying a region in Hue-Saturation-Intensity (HSI) color space (GAHSI) for outdoor field we ed sensing was successfully implemented. Images from two extreme intensity lighting conditions, those under sunny and cloudy sky conditions, were mosa icked to explore the possibility of using GAHSI to locate a plant region in color space when these two extremes were presented simultaneously. The GAH SI result provided evidence for the existence and separability of such a re gion. In the experiment, GAHSI performance was measured by comparing the GA HSI-segmented image with a corresponding hand-segmented reference image. Wh en compared with cluster analysis-based segmentation results, the GAHSI ach ieved equivalent performance.