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Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Gibbs, Jonathon; French, Andrew; Murchie, Erik; Wells, Darren; Pound, Michael; Pridmore, Tony

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Professor of Computer Science

Professor of Applied Plant Physiology

Principal Research Fellow

Professor of Computer Science


Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction.

Journal Article Type Article
Acceptance Date Jan 20, 2019
Online Publication Date Apr 25, 2019
Publication Date Dec 1, 2020
Deposit Date Jan 29, 2019
Publicly Available Date Jan 29, 2019
Journal IEEE/ACM Transactions on Computational Biology and Bioinformatics
Print ISSN 1545-5963
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 17
Issue 6
Pages 1907-1917
Public URL
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