Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

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

Abstract

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.

Citation

Gibbs, J., French, A., Murchie, E., Wells, D., Pound, M., & Pridmore, T. (2020). Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1907-1917. https://doi.org/10.1109/TCBB.2019.2896908

• 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
• DOI: https://doi.org/10.1109/TCBB.2019.2896908
• Public URL: https://nottingham-repository.worktribe.com/output/1502774
• Publisher URL: https://ieeexplore.ieee.org/document/8698802
• Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.