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      Review: New sensors and data-driven approaches—A path to next generation phenomics

      review-article
      a , h , b , c , d , e , f , g , *
      Plant Science
      Elsevier Ireland
      CGIAR, consultative group for international agricultural research, CMOS, complementary metal-oxide-semiconductor, DIY, do it yourself, EMI, electromagnetic induction, FAIR, findable, accessible, interoperable and reusable, HTP, high-throughput phenotyping, IPPN, international plant phenotyping network, IR, infrared, LiDAR, light detection and ranging, LIFT, laser-induced fluorescence transient, MIAPPE, minimum information about a plant phenotyping experiments, MWIR, mid-wavelength infrared, NDVI, normalised difference vegetation index, NIR, snear-infrared spectroscopy, RGB, red-green-blue, RUE, radiation use efficiency, SIF, sun-induced fluorescence, SWIR, short-wavelength infrared, UAV, unmanned aerial vehicle, WUE, water use efficiency, Imaging, IPPN, Metadata, Next generation phenomics, Plant phenotyping, Sensor development, Trait value

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          Highlights

          • Strategies for future high throughput, non-destructive and cost-efficient measurement of plant traits are highlighted.

          • Use of low-cost and DIY approaches in phenomics provides opportunities for rapid prototyping and sensor development.

          • Robust protocols, data harmonization and provenance are critical to allow data reuse and cross validation of phenotypes.

          • Below-ground phenotyping is a major bottleneck and new technologies allowing the measurement of root-related traits are needed.

          Abstract

          At the 4th International Plant Phenotyping Symposium meeting of the International Plant Phenotyping Network (IPPN) in 2016 at CIMMYT in Mexico, a workshop was convened to consider ways forward with sensors for phenotyping. The increasing number of field applications provides new challenges and requires specialised solutions. There are many traits vital to plant growth and development that demand phenotyping approaches that are still at early stages of development or elude current capabilities. Further, there is growing interest in low-cost sensor solutions, and mobile platforms that can be transported to the experiments, rather than the experiment coming to the platform. Various types of sensors are required to address diverse needs with respect to targets, precision and ease of operation and readout. Converting data into knowledge, and ensuring that those data (and the appropriate metadata) are stored in such a way that they will be sensible and available to others now and for future analysis is also vital. Here we are proposing mechanisms for “next generation phenomics” based on our learning in the past decade, current practice and discussions at the IPPN Symposium, to encourage further thinking and collaboration by plant scientists, physicists and engineering experts.

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          Most cited references83

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          Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data

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            Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera

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              Lidar Remote Sensing of the Canopy Structure and Biophysical Properties of Douglas-Fir Western Hemlock Forests

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                Author and article information

                Contributors
                Journal
                Plant Sci
                Plant Sci
                Plant Science
                Elsevier Ireland
                0168-9452
                1873-2259
                1 May 2019
                May 2019
                : 282
                : 2-10
                Affiliations
                [a ]Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
                [b ]LEPSE, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
                [c ]Arvalis, Institut du végétal, 45, voie Romaine 41240 Beauce la Romaine, France
                [d ]Forage Science, Grasslands Research Centre, AgResearch, Tennent Drive, Fitzherbert, Palmerston North 4410, New Zealand
                [e ]Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Cientificas (CSIC) Avenida Menéndez Pidal, Campus Alameda del Obispo, 14004 Córdoba, Spain
                [f ]Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), El Batán, Texcoco, México C.P. 56237, Mexico
                [g ]National Institute of Agricultural Botany (NIAB), Huntingdon Road, Cambridge, CB3 0LE, UK
                [h ]Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, Brno, Czech Republic
                Author notes
                [* ]Corresponding author. eric.ober@ 123456niab.com
                Article
                S0168-9452(17)31050-6
                10.1016/j.plantsci.2019.01.011
                6483971
                31003608
                c2e99971-21b8-4f7e-8bf6-8c4e9e81daf6
                © 2019 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 7 November 2017
                : 15 December 2018
                : 9 January 2019
                Categories
                Article

                Plant science & Botany
                cgiar, consultative group for international agricultural research,cmos, complementary metal-oxide-semiconductor,diy, do it yourself,emi, electromagnetic induction,fair, findable, accessible, interoperable and reusable,htp, high-throughput phenotyping,ippn, international plant phenotyping network,ir, infrared,lidar, light detection and ranging,lift, laser-induced fluorescence transient,miappe, minimum information about a plant phenotyping experiments,mwir, mid-wavelength infrared,ndvi, normalised difference vegetation index,nir, snear-infrared spectroscopy,rgb, red-green-blue,rue, radiation use efficiency,sif, sun-induced fluorescence,swir, short-wavelength infrared,uav, unmanned aerial vehicle,wue, water use efficiency,imaging,ippn,metadata,next generation phenomics,plant phenotyping,sensor development,trait value

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