Plant Trait Segmentation for Plant Growth Monitoring

Abstract

3D point cloud segmentation is an important step for plant phenotyping applications. The segmentation should be able to separate the various plant components such as leaves and stem robustly to enable traits to be measured. Also, it is important for the segmentation method to work on range of plant architectures with good accuracy and computation time. In this paper, we propose a segmentation method using Euclidean distance to segment the point cloud generated using a structure-from-motion algorithm. The proposed algorithm requires no prior information about the point cloud. Experimental results illustrate that our proposed method can effectively segment the plant point cloud irrespective of its architecture and growth stage. The proposed method has outperformed the standard methods in terms of computation time and segmentation quality.