CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
A photometric stereo-based 3D imaging system using computer vision and deep learning for tracking plant growth
Authors
Ahmad
Aich
+100 more
Allan
Apelt
Apelt
Araus
Araus
Arora
Azhar
Babenko
Bernotas
Berry
Bianco
Biskup
Bours
Bunce
Burgess
Cang
Casal
Chelle
Chen
Chew
Coppens
Crawford
da Costa
De Vylder
de Wit
Dhondt
Dobrescu
Dornbusch
Dornbusch
Doyle
Edwards
Elliott
Furbank
Gibbs
Gommers
Gonzalez
Green
He
Henriques
Herrero-Huerta
Jacquemoud
Jansen
Jay
Karidas
Kozuka
Li
Liang
Lin
Long
Lucas
McClung
Meinke
Minervini
Mizoguchi
Nguyen
Otsu
Paulus
Paulus
Pierik
Pound
Pound
Pound
Pyl
Quint
Ren
Richards
Ruckelshausen
Sandalio
Sankaran
Scharr
Shakoor
Sharma
Singh
Smeulders
Smith
Smith
Tardieu
Tattaris
Tester
Thapa
Tippetts
Tomé
Ubbens
Underwood
Valmadre
van Zanten
Vile
Virlet
Vázquez-Arellano
Wiese
Woodham
Xiong
Yamori
Yang
Yoo
Zhang
Zhang
Zhang
Zhou
Zielinski
Publication date
1 January 2019
Publisher
'Oxford University Press (OUP)'
Doi
Cite
Abstract
© The Author(s) 2019. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Background: Tracking and predicting the growth performance of plants in different environments is critical for predicting the impact of global climate change. Automated approaches for image capture and analysis have allowed for substantial increases in the throughput of quantitative growth trait measurements compared with manual assessments. Recent work has focused on adopting computer vision and machine learning approaches to improve the accuracy of automated plant phenotyping. Here we present PS-Plant, a low-cost and portable 3D plant phenotyping platform based on an imaging technique novel to plant phenotyping called photometric stereo (PS). Results: We calibrated PS-Plant to track the model plant Arabidopsis thaliana throughout the day-night (diel) cycle and investigated growth architecture under a variety of conditions to illustrate the dramatic effect of the environment on plant phenotype. We developed bespoke computer vision algorithms and assessed available deep neural network architectures to automate the segmentation of rosettes and individual leaves, and extract basic and more advanced traits from PS-derived data, including the tracking of 3D plant growth and diel leaf hyponastic movement. Furthermore, we have produced the first PS training data set, which includes 221 manually annotated Arabidopsis rosettes that were used for training and data analysis (1,768 images in total). A full protocol is provided, including all software components and an additional test data set. Conclusions: PS-Plant is a powerful new phenotyping tool for plant research that provides robust data at high temporal and spatial resolutions. The system is well-suited for small- and large-scale research and will help to accelerate bridging of the phenotype-to-genotype gap
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Crossref
See this paper in CORE
Go to the repository landing page
Download from data provider
Last time updated on 25/10/2020
UWE Bristol Research Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:uwe-repository.worktribe.c...
Last time updated on 08/06/2020
Edinburgh Research Explorer
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:pure.ed.ac.uk:publications...
Last time updated on 11/05/2020