A SUPERVOXEL-BASED APPROACH FOR LEAVES SEGMENTATION OF POTATO PLANTS FROM POINT CLOUDS

This paper proposes a method for automatic 3D point cloud segmentation of leaves on individual potato (Solanum tuberosum) plants from UAV-based images. Conventional methods based on visual inspection require significant field work and human effort. The proposed approach uses remote sensing techniques as well as the Voxel Cloud Connectivity Segmentation (VCCS) technique to identify geometrical features and calculate structural attributes. The main component of the method is the SuperVoxel-based leaves segmentation from the tridimensional photogrametric point cloud. The approach is tested using UAV data from an experimental plot. Supervoxel segments are compared with a manual segmentation conducted by experts. The results show that the proposed method allows an accurate leaves segmentation, i.e. it is as good as conventional methods, but much more economic and timely.