Abstract

Roots, considered as the ”hidden half of the plant”, are essential to a plant’s health and pro- ductivity. Understanding root architecture has the potential to enhance efforts towards im- proving crop yield. In this dissertation we develop geometric approaches to non-destructively characterize the full architecture of the root system from 3D imaging while making com- putational advances in topological optimization. First, we develop a global optimization algorithm to remove topological noise, with applications in both root imaging and com- puter graphics. Second, we use our topology simplification algorithm, other methods from computer graphics, and customized algorithms to develop a high-throughput pipeline for computing hierarchy and fine-grained architectural traits from 3D imaging of maize roots. Finally, we develop an algorithm for consistently simplifying the topology of nested shapes, with a motivating application in temporal root system analysis. Along the way, we con- tribute to the computer graphics community a pair of topological simplification algorithms both for repairing a single 3D shape and for repairing a sequence of nested shapes.

Committee Chair

Tao Ju

Committee Members

Tao Ju, Christopher Topp, Ulugbek Kamilov, Ayan Chakrabarti,

Degree

Doctor of Philosophy (PhD)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Dissertation

Date of Award

Summer 8-15-2022

Language

English (en)

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