ITErRoot: High Throughput Segmentation of 2-Dimensional Root System Architecture
Date
2021-09-21
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
ORCID
0000-0001-7020-4314
Type
Thesis
Degree Level
Masters
Abstract
Root system architecture (RSA) analysis is a form of high-throughput plant phenotyping which has
recently benefited from the application of various deep learning techniques. A typical RSA pipeline includes
a segmentation step, where the root system is extracted from 2D images. The segmented image is then passed
to subsequent steps for processing, which result in some representation of the architectural properties of the
root system. This representation is then used for trait computation, which can be used to identify various
desirable properties of a plant’s RSA. Errors which arise at the segmentation stage can propagate themselves
throughout the remainder of the pipeline and impact results of trait analysis.
This work aims to design an iterative neural network architecture, called ITErRoot, which is particularly
well suited to the segmentation of root structure from 2D images in the presence of non-root objects. A novel
2D root image dataset is created along with a ground truth annotation tool designed to facilitate consistent
manual annotation of RSA. The proposed architecture is able to take advantage of the root structure to
obtain a high quality segmentation and is generalizable to root systems with thin roots, showing improved
quality over recent approaches to RSA segmentation. We provide rigorous analysis designed to identify the
strengths and weaknesses of the proposed model as well as to validate the effectiveness of the approach for
producing high-quality segmentations.
Description
Keywords
Iterative Neural Network, Root System Architecture Analysis, Root System Segmentation
Citation
Degree
Master of Science (M.Sc.)
Department
Computer Science
Program
Computer Science