Abstract
Modern plant phenotyping requires tools that are robust to noise and missing data, while being able to efficiently process large numbers of plants. Here, we studied the skeletonization of plant architectures from 3D point clouds, which is critical for many downstream tasks, including analyses of plant shape, morphology, and branching angles. Specifically, we developed an algorithm to improve skeletonization at branch points (forks) by leveraging the geometric properties of cylinders around branch points. We tested this algorithm on a diverse set of high-resolution 3D point clouds of tomato and tobacco plants, grown in five environments and across multiple developmental timepoints. Compared to existing methods for 3D skeletonization, our method efficiently and more accurately estimated branching angles even in areas with noisy, missing, or non-uniformly sampled data. Our method is also applicable to inorganic datasets, such as scans of industrial pipes or urban scenes containing networks of complex cylindrical shapes.
Highlights
Sens. 2021, 13, 3802. https://doi.org/The skeleton of a 3D object is a thinned 1D representation that captures its basic geometry and shape
Assessing and quantifying changes in plant morphology is often used for measuring agricultural yields [20], analyzing stress responses and plant-environment interactions [21], and facilitating functional genomics studies [22]
We presented a fast method that improved the extraction of skeleton graphs from point clouds of plant shoot architectures
Summary
Sens. 2021, 13, 3802. https://doi.org/The skeleton of a 3D object is a thinned 1D representation (often in the form of a graph) that captures its basic geometry and shape. Fast and accurate determination of branch angles is important for many downstream tasks, including the study of plant growth and development [15], measuring light interception by leaves [16], and for inferring genotype-to-phenotype relationships [17], both in the lab and in the field [18,19]. Plant skeletonization is important in forestry [23], ecology [24], urban planning [25], and engineering [26]. All of these applications require methods for accurately skeletonizing shapes from 3D data
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