Automated extraction of three-dimensional cereal plant structures from two-dimensional orthographic images

The authors present a novel approach to automatically extract three-dimensional (3D) structures of cereal plants from 2D orthographic images. The idea of this approach is to use 2D skeletons to represent elongated shapes of plants in 2D images and then to generate 3D plant structures from 2D skeletons. However, existing skeletonisation algorithms generate artefacts because of boundary extremities and noise. Moreover, cereal plant leaves appear as broken segments in images because of leaf twists. In this approach, oriented Gaussian filters are used to obtain ridges from 2D images via guided skeletonisation, so that one can obtain smooth skeleton representation of 2D elongated shapes without redundant branches. For broken leaf segments, a cost function is proposed to identify whether the two segments belong to the same leaf and thus should be connected. For a given leaf tip of a cereal plant, the z -coordinators of two 2D side-view images are same. Based on this, it is easy to identify the same leaf tip in two 2D side-views. Thus, the authors are able to track skeletons of leaves from their tips to the plant roots. Experimental results show that the proposed approach is able to handle various issues such as broken segments and overlapping among plant parts, and is also able to automatically extract 3D structures of cereal plants.