The orchards usually have rough terrain，dense tree canopy and weeds. It is hard to use GNSS for autonomous navigation in orchard due to signal occlusion，multipath effect，and radio frequency interference. To achieve autonomous navigation in orchard，a visual navigation method based on multiple images at different shooting angles is proposed in this paper. A dynamic image capturing device is designed for camera installation and multiple images can be shot at different angles. Firstly，the obtained orchard images are classified into sky and soil detection stage. Each image is transformed to HSV space and initially segmented into sky，canopy and soil regions by median filtering and morphological processing. Secondly，the sky and soil regions are extracted by the maximum connected region algorithm，and the region edges are detected and filtered by the Canny operator. Thirdly，the navigation line in the current frame is extracted by fitting the region coordinate points. Then the dynamic weighted filtering algorithm is used to extract the navigation line for the soil and sky detection stage，respectively，and the navigation line for the sky detection stage is mirrored to the soil region. Finally，the Kalman filter algorithm is used to fuse and extract the final navigation path. The test results on 200 images show that the accuracy of visual navigation path fitting is 95. 5%，and single frame image processing costs 60 ms，which meets the real-time and robustness requirements of navigation. The visual navigation experiments in Camellia oleifera orchard show that when the driving speed is 0. 6 m/s，the maximum tracking offset of visual navigation in weed-free and weedy environments is 0. 14 m and 0. 24 m，respectively，and the RMSE is 30 mm and 55 mm，respectively.

Identifying the maturity of asparagus is a crucial step for machine-assisted harvesting of asparagus in complex environments. This study proposes an innovative method to evaluate the height and diameter of asparagus stems, combining an enhanced YOLOv5 detection algorithm with point cloud data. In this method, first, the YOLOv5 model was improved, enabling efficient recognition and detection of asparagus in complex environments. Subsequently, a RealSense L515 radar camera was deployed to capture both the original RGB images and the point cloud information. The improved YOLOv5 algorithm was then employed to detect asparagus instances within the RGB images, with the pixel positions of the detection frames mapped onto the point cloud dataset to extract comprehensive 3D point cloud details of the asparagus. Finally, noise was reduced through statistical filtering and Euclidean clustering, and asparagus height was determined using the oriented bounding box methodology. Slices, each with a thickness of 10 mm, were extracted at designated measurement points, and the asparagus diameter was calculated by assessing the disparity between the maximum and minimum coordinates perpendicular to the growth direction of the asparagus. Experimental results showed that the mean average precision, precision, and recall of the improved YOLOv5 model increased by 4.85%, 5.09%, and 3.4%, reaching 98.21%, 97.11%, and 95.33%, respectively, which are higher than those of the YOLOv5 prototype network. Therefore, the proposed method could effectively detect asparagus. The algorithm exhibited a mean absolute error of 1.08 cm, a mean absolute percentage error of 4.06%, and a root mean square error of 1.60 cm in its estimation of asparagus height. For asparagus diameter estimation, the algorithm achieved a mean absolute error of 0.86 mm, a mean absolute percentage error of 7.98%, and a root mean square error of 1.23 mm. These results confirm that the proposed method can estimate the height and diameter of asparagus stems accurately, thereby providing invaluable technical support for machine harvesting of asparagus.