Advisor: Dr. Patrick Shipman

Committee:  Dr. Jennifer Mueller, Dr. Joe von Fischer

Title: Nonlinear Dynamics and Machine Learning Classification of Plant
Pigment Patterns

Abstract: Plants exhibit a variety of vibrant colors that are both
beautiful and functional. They owe their reds, purples, and blues to a
class of pigments called anthocyanins. Many plants possess spatial
variation in their anthocyanin concentration and color, which manifest
as diverse patterns on their leaves and flowers. Flower patterns can
influence interactions with pollinators, who may have innate preferences
for certain patterns and can learn to distinguish between them. Recent
work has identified the genes and proteins involved in activation and
inhibition of anthocyanin synthesis in some species of Mimulus and
showed that their dynamics can be described with a two-component
diffusion model. In this thesis, we combine numerical simulations of
this model with machine-learning algorithms to classify patterns based
on a parameter value that influences the pattern spot size and density.
A key challenge is to successfully classify using 2-dimensional spot
data, which would permit the classification of real petal data from
photos. Our approach makes use of the Voronoi mountain function to
construct a 3-dimensional surface from the 2-dimensional data.
Classification is very successful with simulated data, and it produces
plausible results for real Mimulus petals.