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      A Review of Imaging Techniques for Plant Phenotyping

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          Abstract

          Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

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          Future scenarios for plant phenotyping.

          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.
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            Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation

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              PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit.

              The high-throughput phenotypic analysis of Arabidopsis thaliana collections requires methodological progress and automation. Methods to impose stable and reproducible soil water deficits are presented and were used to analyse plant responses to water stress. Several potential complications and methodological difficulties were identified, including the spatial and temporal variability of micrometeorological conditions within a growth chamber, the difference in soil water depletion rates between accessions and the differences in developmental stage of accessions the same time after sowing. Solutions were found. Nine accessions were grown in four experiments in a rigorously controlled growth-chamber equipped with an automated system to control soil water content and take pictures of individual plants. One accession, An1, was unaffected by water deficit in terms of leaf number, leaf area, root growth and transpiration rate per unit leaf area. Methods developed here will help identify quantitative trait loci and genes involved in plant tolerance to water deficit.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                November 2014
                24 October 2014
                : 14
                : 11
                : 20078-20111
                Affiliations
                [1 ] School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China; E-Mail: hudiepianpianlilei@ 123456hotmail.com
                [2 ] Center for Precision & Automated Agricultural Systems, Washington State University, 24106 N. Bunn Rd., Prosser, WA 99350, USA; E-Mail: qinzhang@ 123456wsu.edu
                [3 ] School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai 200240, China
                Author notes

                External Editor: Gonzalo Pajares Martinsanz

                [* ] Authors to whom correspondence should be addressed; E-Mail: danfenggrace@ 123456gmail.com ; Tel.: +86-21-3420-6943.
                Article
                sensors-14-20078
                10.3390/s141120078
                4279472
                25347588
                94493d84-74a4-4c9f-a9aa-fea233c2be2a
                © 2014 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                : 08 August 2014
                : 09 October 2014
                : 10 October 2014
                Categories
                Review

                Biomedical engineering
                phenotyping phenotype,fluorescence imaging,thermal infrared imaging,visible light imaging,imaging spectroscopy,three dimensional imaging

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