Abstract: Abstract: In a corn field with an unstructured environment, for the images collection by monocular vision, conventional path recognition algorithms are difficult to guarantee their robustness due to illumination variation, background reflection, shadow noise, and so on. In addition, the increase of the calculation amount of the algorithms caused by the complicated background information of the corn field environment affects the quickness and the real-timeliness of the corn harvester autonomous navigation, which leads to the difficulty of meeting the requirement for the operation efficiency of the corn field harvester and impedes the practical application of the mobile robot technology in agricultural production. The significant premise of visual navigation for a corn harvester is the precise and rapid detection of the navigation route. Unfortunately, some interferences will affect the extraction of the navigation route and the judgment for the edge of the corn field due to the corn shadow and weeds. The vision navigation has many technical advantages of adapting to the complicated field operating environment, has wide detection range, and has rich and complete information. This paper put forward the detection algorithms of the operation routes of the corn harvester and the judgement of the end of the corn field by analyzing the different color features of the visual navigation image. Here we studied the region of interest where lies the target straight line. To improve the rapidity of the navigation path recognition and meet the real-time requirements of autonomous navigation operations, for the color characteristic of the corn field environment, the clustering segmentation of the image was performed based on the analysis of red (R), green (G) and blue (B) components to achieve the respective clusters of the path and the corn field information. We used these color components to extract the target features of the inner and outside of the corn field respectively, and smooth the image using the moving average method with the set length. First, to remove the shadow interference of the corn columns, we compared the G component and the B component of a color image and smoothed the image using the moving average method. For the first frame, we used the distribution of the G component to find the alternate point; for other frames, we connected them with the previous frame to calculate the skew angles of the target line. Then, we extracted the navigation routes based on the Hough transform passing a known point. At last, it would be determined whether the corn harvester reached the end automatically according to the continuous mutation of the R component. The test proved that the processing time of detecting line was 50.1 ms per frame and the detection of the end was accurate and reliable, which could meet the demand of the real operation of a corn harvester in the field. It can be concluded that the navigation routes are extracted and the edge of the corn field is judged accurately, which satisfy the requirement of the practical application of the corn harvester in the field. In addition, the work can also provide the reference for the vision navigation routes' detection of sorghum and some high pole crops with mechanized harvesting.