Research on a Multi-Sensor Fusion-Based Method for Fruit-Tree Dripline Path Detection

To enable automatic extraction of high-precision paths for intelligent orchard operations, a path detection method targeting the fruit-tree dripline is proposed. The method integrates 2D-LiDAR, RTK-GNSS, and an electronic compass, achieving time synchronization, coordinate-frame construction, and extrinsic calibration. Point clouds are rotation-normalized via least-squares trajectory fitting; ground segmentation and statistical filtering suppress noise; segment-wise extremal edge points, together with an α-shape-based concave hull algorithm, fit and generate the dripline path; and inverse rotation restores the result to the orchard-local coordinate frame. Field experiments demonstrated that the method accurately extracts dripline paths in orchard environments; relative to manual measurements, the overall mean absolute error was 0.23 m and the root-mean-square error was 0.30 m. Across different travel speeds, the system exhibited good adaptability and stability, meeting the path-planning requirements of precision orchard operations.