  In this work, the use of t-SNE is proposed to embed 3D point clouds of plants
into 2D space for plant characterization. It is demonstrated that t-SNE
operates as a practical tool to flatten and visualize a complete 3D plant model
in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant
structures at various organizational levels. Aside from the promise of serving
as a visualization tool for plant scientists, t-SNE also provides a gateway for
processing 3D point clouds of plants using their embedded counterparts in 2D.
In this paper, simple methods were proposed to perform semantic segmentation
and instance segmentation via grouping the embedded 2D points. The evaluation
of these methods on a public 3D plant data set conveys the potential of t-SNE
for enabling of 2D implementation of various steps involved in automatic 3D
phenotyping pipelines.
