关键词: 果园导航, 深度学习, YOLOv5, 树干识别, 根点替换, 直线拟合

Abstract: Inter‑line mechanical autonomous navigation is helpful to improve fruit production efficiency and reduce labor cost. Trees are natural landmarks for navigating between lines and can provide cues for robots. In this paper, combining deep learning and least square method, a navigation line extraction method based on machine vision for inter‑line navigation scene is proposed. Firstly, the tree trunk image was collected in the actual environment, and the image was flipped and clipped to expand the tree trunk data set. Secondly, the YOLOv5 network model was constructed, and based on this model, the interline trunk identification was carried out. The method of replacing the middle point of the root with the middle point under the identification box was proposed, so as to determine the positioning basis point of the tree line fitting. Finally, the least square method was used to fit the single tree line and center line of tree line in orchard. The experimental results show that the average recognition accuracy of the YOLOv5 network detection model is 85.5%. In the proposed root point replacement positioning method, the average error of the distance between the midpoint of the bottom of the identification box, the linear pixel distance between the positioning base point and the actual root midpoint, is 5.1 pixels, and the selection error of the positioning base point is within the reliable range. The average lateral deviation of the navigation line in the center of the tree line is 5.8 pixels, which meets the requirements of inter‑line navigation.

Key words: orchard navigation, deep learning, YOLOv5, tree trunk recognition, root point replacement, linear fitting