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Automated recovery of 3D models of plant shoots from multiple colour images

Pound, Michael P.; French, Andrew P.; Murchie, Erik H.; Pridmore, Tony P.

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Authors

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ANDREW FRENCH andrew.p.french@nottingham.ac.uk
Professor of Computer Science

Dr ERIK MURCHIE erik.murchie@nottingham.ac.uk
Professor of Applied Plant Physiology

TONY PRIDMORE tony.pridmore@nottingham.ac.uk
Professor of Computer Science



Abstract

Increased adoption of the systems approach to biological research has focussed attention on the use of quantitative models of biological objects. This includes a need for realistic 3D representations of plant shoots for quantification and modelling. Previous limitations in single or multi-view stereo algorithms have led to a reliance on volumetric methods or expensive hardware to record plant structure. We present a fully automatic approach to image-based 3D plant reconstruction that can be achieved using a single low-cost camera. The reconstructed plants are represented as a series of small planar sections that together model the more complex architecture of the leaf surfaces. The boundary of each leaf patch is refined using the level set method, optimising the model based on image information, curvature constraints and the position of neighbouring surfaces. The reconstruction process makes few assumptions about the nature of the plant material being reconstructed, and as such is applicable to a wide variety of plant species and topologies, and can be extended to canopy-scale imaging. We demonstrate the effectiveness of our approach on datasets of wheat and rice plants, as well as a novel virtual dataset that allows us to compute quantitative measures of reconstruction accuracy. The output is a 3D mesh structure that is suitable for modelling applications, in a format that can be imported in the majority of 3D graphics and software packages.

Citation

Pound, M. P., French, A. P., Murchie, E. H., & Pridmore, T. P. (2014). Automated recovery of 3D models of plant shoots from multiple colour images. Plant Physiology, 166(4), https://doi.org/10.1104/pp.114.248971

Journal Article Type Article
Publication Date Oct 1, 2014
Deposit Date Jun 25, 2015
Publicly Available Date Mar 28, 2024
Journal Plant Physiology
Print ISSN 0032-0889
Electronic ISSN 0032-0889
Publisher American Society of Plant Biologists
Peer Reviewed Peer Reviewed
Volume 166
Issue 4
DOI https://doi.org/10.1104/pp.114.248971
Public URL https://nottingham-repository.worktribe.com/output/994322
Publisher URL http://www.plantphysiol.org/content/166/4/1688.full

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