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Agronomy 2018, 8(5), 71; https://doi.org/10.3390/agronomy8050071

Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots

1,†
,
2,†
and
1,3,*
1
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
2
School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA
3
Department of Food Science and Technology, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 11 April 2018 / Revised: 24 April 2018 / Accepted: 9 May 2018 / Published: 14 May 2018
(This article belongs to the Special Issue Precision Phenotyping in Plant Breeding)
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Abstract

Scanning technologies based on X-ray Computed Tomography (CT) have been widely used in many scientific fields including medicine, nanosciences and materials research. Considerable progress in recent years has been made in agronomic and plant science research thanks to X-ray CT technology. X-ray CT image-based phenotyping methods enable high-throughput and non-destructive measuring and inference of root systems, which makes downstream studies of complex mechanisms of plants during growth feasible. An impressive amount of plant CT scanning data has been collected, but how to analyze these data efficiently and accurately remains a challenge. We review statistical and computational approaches that have been or may be effective for the analysis of 3D CT images of plant roots. We describe and comment on different approaches to aspects of the analysis of plant roots based on images, namely, (1) root segmentation, i.e., the isolation of root from non-root matter; (2) root-system reconstruction; and (3) extraction of higher-level phenotypes. As many of these approaches are novel and have yet to be applied to this context, we limit ourselves to brief descriptions of the methodologies. With the rapid development and growing use of X-ray CT scanning technologies to generate large volumes of data relevant to root structure, it is timely to review existing and potential quantitative and computational approaches to the analysis of such data. Summaries of several computational tools are included in the Appendix. View Full-Text
Keywords: branch tracking; computed tomography; deep learning; plant phenotyping; root imaging branch tracking; computed tomography; deep learning; plant phenotyping; root imaging
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Xu, Z.; Valdes, C.; Clarke, J. Existing and Potential Statistical and Computational Approaches for the Analysis of 3D CT Images of Plant Roots. Agronomy 2018, 8, 71.

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