In order to solve the problems of serious illumination interference for image processing and the poor robustness of conventional navigation line detection algorithms in agricultural navigation robot based on machine vision, the methods of crop recognition and navigation line extraction in natural environment are studied. Cg component of YCrCg color model is selected for subsequent image processing to reduce the adverse effects of light change on image segmentation and navigation line extraction. The fuzzy C-means clustering method (FCM) based on two-dimensional histogram is used for Cg component segmentation, so as to identify the green crop. According to the characteristics of crop rows in mage, a method of crop line detection based on linear scanning is designed. Pixel on image bottom and top edge are selected as two endpoints of a straight line, by moving the endpoints location result in different slope lines, the line containing the most target points is chosen as the crop centerline, and then obtain the navigation line. The experimental results show that image segmentation based on YCgCr color model can effectively identify the crops under different illumination conditions. Further more, the time consumption for single image of 640 pixel×480 pixel is about 16.5 ms. The linear scanning algorithm can quickly and accurately find the navigation line. Compared with Hough transform and least square algorithm, the designed algorithm has the advantages of high speed and good robustness.