Images encode the shape of plant networks such as roots, tree crowns, or leaf venation.
Recently, the combination of imaging data with skeletonization algorithms became popular as a tool to extract the hierarchies and geometric measurements of plant networks.
The advent of digital imaging accelerated the development of computational methods to digitally reconstruct the shape of plant networks from imaging data.
The introduction of cheap digital imaging devices for the consumer market enabled the wide use of digital images to capture the shape of plant networks such as roots, tree crowns, or leaf venation.
In particular, roots system architecture (Lynch, 1995; de Dorlodot et al., 2007), tree crown architecture (Delagrange et al., 2004), or leaf vein structures (Roth-Nebelsick et al., 2001; Mason et al., 2013; Price et al., 2013) are examples of plant networks.
The use of skeletonization algorithms to obtain quantitative measurements of plant networks was first applied on tree crowns to estimate the radiative properties by simulating the foliage and fine branches from laser data (Cote et al., 2009) and to derive branch hierarchy information along with length and diameter distributions (Bucksch and Fleck, 2011; Delagrange et al., 2014).
2. Diameter measurements of the plant network of interest cannot be derived at all locations of the skeleton.
In recent years, the field of computer graphics developed several new skeleton concepts that make use of prior knowledge of the plant network.
In the case of a plant network, this physical process of capturing light is a projection of the plant network onto a plane.
To understand the potential problems, consider the Reeb graph of a vertical trunk of a plant network with a child branch emerging at a 90° angle from a parent branch (Fig. 6).
