Identifying rice seedling bands based on slope virtualization clustering

Abstract In this paper, a method for identifying rice seedling band tracks by using LIDAR data is proposed, and the method provides navigation information for straight-line driving to minimize seedling injuries when paddy field management machinery enters a rice field. In this paper, the reconstruction of seedling band tracks is realized by constructing a three-dimensional (3D) point cloud image by collecting LIDAR data. Based on a two-dimensional servo platform, a mean-shift clustering algorithm is designed with slope virtualization, iterative zone clustering and linearization processes. The point cloud data of rice field on the 35th day of transplanting were simulated by Matlab. In the environment 2–3 m in the forward-looking direction and a standard plant spacing of 21 cm, the reconstructed seedling trajectory was compared with the real seedling trajectory. The maximum parallelism of the seedling belt trajectory was 45 mm, and the maximum median deviation was 7 mm. These values meet the needs of paddy field management machinery for achieving nondestructive seedling driving trajectories and verify the feasibility of the slope-blurred mean-shift clustering algorithm for seedling identification.