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      Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms

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          Abstract

          The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses a common set of problems, including data acquisition and analysis. In this review, we give an overview of the main systems developed to acquire images. We give an in-depth analysis of image processing with its major issues and the algorithms that are being used or emerging as useful to obtain data out of images in an automatic fashion.

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          Increased plant growth in the northern high latitudes from 1981 to 1991

          R. Myneni, C. D. Keeling, C J Tucker … (1997)
          Article 0 comments Cited 974 times – based on 0 reviews     Review now
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            Watersheds in digital spaces: an efficient algorithm based on immersion simulations

            Andrea Vincent, P. Soille (1991)
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              Future scenarios for plant phenotyping.

              Fabio Fiorani, Ulrich Schurr (2013)
              With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.
              Article 0 comments Cited 316 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Gigascience
                Journal ID (iso-abbrev): Gigascience
                Journal ID (publisher-id): gigascience
                Title: GigaScience
                Publisher: Oxford University Press
                ISSN (Electronic): 2047-217X
                Publication date Collection: November 2017
                Publication date (Electronic): 03 October 2017
                Publication date PMC-release: 03 October 2017
                Volume: 6
                Issue: 11
                Pages: 1-18
                Affiliations
                [1 ]Genetics, ETSIA, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
                [2 ]Genetics, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena 30202, Spain
                Author notes
                [* ]Correspondence address. Marcos Egea-Cortines, Genetics, Instituto de Biotecnología Vegetal, Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n. Cartagena 30202, Spain; Tel: +34868071075; Fax: +34868071079; E-mail: marcos.egea@ 123456upct.es
                Article
                Publisher ID: gix092
                DOI: 10.1093/gigascience/gix092
                PMC ID: 5737281
                PubMed ID: 29048559
                SO-VID: 907f6b2b-fac9-42e2-9d21-02e7f99ef538
                Copyright © © The Author 2017. Published by Oxford University Press.
                License:

                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.

                History
                Date accepted : 17 September 2017
                Date received : 27 February 2017
                Date revision received : 20 June 2017
                Page count
                Pages: 18
                Funding
                Funded by: FEDER 10.13039/501100002924
                Award ID: BFU-2013-45 148-R
                Funded by: Fundación Séneca 10.13039/100007801
                Award ID: 19 398/PI/14
                Funded by: FEDER ViSelTR
                Award ID: TIN2012–39 279
                Categories
                Subject: Review

                Keywords: algorithms,artificial vision,deep learning,hyperspectral cameras,machine learning,segmentation
                Data availability:
                Keywords: algorithms, artificial vision, deep learning, hyperspectral cameras, machine learning, segmentation

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