Segmentation of crop organs through region growing in 3D space

Abstract

The segmentation of crop organs from 3D laser point clouds is an important prerequisite work of crop phenotypic parameters in non-destructive measurement. This paper respectively selected the 3D point cloud data of the rapeseed plant with leaf stage and pod stage as the research materials. A novel normal vector-based method for segmentation of the 3D point cloud is presented. First, a 3D scanner, HandyScan 300, was used to obtain 3D point cloud data. Second, using the voxel-based grid method, the original point cloud data were down-sampled at the premise of keeping the shape of point cloud unchanged. Third, according to the characteristics of the point cloud, the two conditions of the normal vector difference and the Euclidean distance between each point could be merged into two necessary conditions of the current class. Finally, the nearest point was searched with a set of labeled point cloud growth and through each point cloud of European radius until the collection of point cloud and the adjacent candidate was in accordance with the current conditions of the finished classification process. Results showed that the angle difference threshold of the normal vector was [0.91, 0.95]. The segmentation effect of the point cloud data of the leaves of the rapeseed plant was the best, which avoided the problem of misclassification and the appearance of over-segmentation. The angle difference threshold of the normal vector was [0.88, 0.91]. The segmentation effect of the point cloud data of the pod of the rapeseed plant was the best, and the accuracy rate reached 97%. Therefore, the validity and feasibility of the method was verified. Accurate segmentation of the plant organ is another foundation for the nondestructive measurement of the phenotypic parameters in the later stage.