Novel method for the visual navigation path detection of jujube harvester autopilot based on image processing

To realize automatic harvesting of the jujube, the jujube harvester was designed and manufactured. For achieving the jujube harvester autopilot, a novel algorithm for visual navigation path detection was proposed. The centerline of tree row lines was taken as the navigation path. The method included four main parts: image preprocessing, image segmentation, tree row lines access, and navigation path access. The methods of threshold segmentation, noise removal, and border smoothing were utilized on the image in Lab color space for the image segmentation. The least square method was employed to fit the tree row lines, and the centerline was obtained as the navigation path. Experimental results indicated that the average false detection rate was 3.98%, and the average detection speed was 41 fps. The algorithm meets the requirements of the jujube harvester autopilot in terms of accuracy and speed. It also can lay the foundation for accomplishing the jujube harvester vision-based autopilot.