Infield rapeseed seedling images under natural illumination were studied with six colorvegetationindex segmentation approaches including excess red vegetative index (ExR),excess green vegetation index (ExG),excess green minus excess red (ExGR),normalized difference vegetation index (NDI),color index of vegetation extraction (CIVE) and combination of vegetation indices (COM).The thresholdingbased algorithms were used to extract infield rapeseed plant with shadow image.The segmentation of common RGB vegetation indices were objectively estimated with quantitative evaluation criteria.The results showed that the COM index is superior to the other 5 vegetation indices in the qualitative analysis,which can reduce the segmentation effect caused by the shadow and retain the complete blade profile in the local blade segmentation tests.In the quantitative analyses,the COM index provides the best segmentation accuracy,sensitivity and specificity of 94.1%,97.2% and 90.9%,respectively,with the corresponding standard deviations of 1.1,1.3 and 0.06.