Explore topological data analysis techniques for quantifying plant morphology in this 30-minute conference talk by Elizabeth Munch. Delve into the application of persistent homology as a powerful tool for measuring shape and structure in data, with a focus on image-based input. Learn about the Euclidean Distance Transform (EDT) as an intermediate step to enhance persistence results. Discover how these methods can be applied to analyze hurricane video data and X-Ray CT scans of plants. Examine the experimental design, image processing techniques, and traditional measures used in plant morphology studies. Investigate the Euler Characteristic Curve and its transform, hierarchical clustering, and principal component analysis for assessing plant structures. Gain insights into the importance of barley as an ancient crop and its significance in global agriculture. Understand how topological data analysis contributes to advancing our understanding of plant morphology and its potential applications in crop science.
Topological Data Analysis for Quantifying Plant Morphology
Applied Algebraic Topology Network via YouTube
Overview
Syllabus
Intro
Plant morphology
Main goal of TDA
Barley is one of the world's most ancient crops Remains the world's 4th most important grain crop behind rice, wheat, and corn
Experimental design
Image Processing
Traditional measures
Euler Characteristic Curve
Hierarchical clustering
PCA suggests a slight asymmetry
Euler Characteristic Transform
Computing ECT of seeds
Taught by
Applied Algebraic Topology Network
