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A patch-based approach to 3D plant shoot phenotyping

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


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Professor of Computer Science

John A. Fozard

Professor of Applied Plant Physiology

Professor of Computer Science


The emerging discipline of plant phenomics aims to measure key plant characteristics, or traits, though as yet the set of plant traits that should be measured by automated systems is not well defined. Methods capable of recovering generic representations of the 3D structure of plant shoots from images would provide a key technology underpinning quantification of a wide range of current and future physiological and morphological traits. We present a fully automatic approach to image-based 3D plant reconstruction which represents plants as series of small planar sections that together model the complex architecture of leaf surfaces. The initial boundary of each leaf patch is refined using a 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. As such it 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 real images of wheat and rice plants, an artificial plant with challenging architecture, as well as a novel virtual dataset that allows us to compute distance measures of reconstruction accuracy. We also illustrate the method’s potential to support the identification of individual leaves, and so the phenotyping of plant shoots, using a spectral clustering approach.


Pound, M. P., French, A. P., Fozard, J. A., Murchie, E. H., & Pridmore, T. P. (2016). A patch-based approach to 3D plant shoot phenotyping. Machine Vision and Applications, 27(5), 767-779.

Journal Article Type Article
Acceptance Date Feb 5, 2016
Online Publication Date Mar 31, 2016
Publication Date Mar 31, 2016
Deposit Date Jun 15, 2016
Publicly Available Date Jun 15, 2016
Journal Machine Vision and Applications
Print ISSN 0932-8092
Electronic ISSN 1432-1769
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 27
Issue 5
Pages 767-779
Keywords plant phenotyping, multi-view reconstruction, 3D, level sets
Public URL
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art_10.1007_s00138-016-0756-8.pdf (5.3 Mb)

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