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      Imaging technologies for plant high-throughput phenotyping: a review

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          Abstract

          Phenomics studies a variety of phenotypic plant traits and is the key to understanding genetic functions and environmental effects on plants. With the rapid development of genomics, many plant phenotyping platforms have been developed to study complex traits related to the growth, yield, and adaptation to biotic or abiotic stress, but the ability to acquire high-throughput phenotypic data has become the bottleneck in the study of plant genomics. In recent years, researchers around the world have conducted extensive experiments and research on high-throughput, image-based phenotyping techniques, including visible light imaging, fluorescence imaging, thermal imaging, spectral imaging, stereo imaging, and tomographic imaging. This paper considers imaging technologies developed in recent years for high-throughput phenotyping, reviews applications of these technologies in detecting and measuring plant morphological, physiological, and pathological traits, and compares their advantages and limitations.

          Most cited references63

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          Plant disease: a threat to global food security.

          A vast number of plant pathogens from viroids of a few hundred nucleotides to higher plants cause diseases in our crops. Their effects range from mild symptoms to catastrophes in which large areas planted to food crops are destroyed. Catastrophic plant disease exacerbates the current deficit of food supply in which at least 800 million people are inadequately fed. Plant pathogens are difficult to control because their populations are variable in time, space, and genotype. Most insidiously, they evolve, often overcoming the resistance that may have been the hard-won achievement of the plant breeder. In order to combat the losses they cause, it is necessary to define the problem and seek remedies. At the biological level, the requirements are for the speedy and accurate identification of the causal organism, accurate estimates of the severity of disease and its effect on yield, and identification of its virulence mechanisms. Disease may then be minimized by the reduction of the pathogen's inoculum, inhibition of its virulence mechanisms, and promotion of genetic diversity in the crop. Conventional plant breeding for resistance has an important role to play that can now be facilitated by marker-assisted selection. There is also a role for transgenic modification with genes that confer resistance. At the political level, there is a need to acknowledge that plant diseases threaten our food supplies and to devote adequate resources to their control.
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            Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance

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              Hyperspectral remote sensing of plant pigments.

              The dynamics of pigment concentrations are diagnostic of a range of plant physiological properties and processes. This paper appraises the developing technologies and analytical methods for quantifying pigments non-destructively and repeatedly across a range of spatial scales using hyperspectral remote sensing. Progress in deriving predictive relationships between various characteristics and transforms of hyperspectral reflectance data are evaluated and the roles of leaf and canopy radiative transfer models are reviewed. Requirements are identified for more extensive intercomparisons of different approaches and for further work on the strategies for interpreting canopy scale data. The paper examines the prospects for extending research to the wider range of pigments in addition to chlorophyll, testing emerging methods of hyperspectral analysis and exploring the fusion of hyperspectral and LIDAR remote sensing. In spite of these opportunities for further development and the refinement of techniques, current evidence of an expanding range of applications in the ecophysiological, environmental, agricultural, and forestry sciences highlights the growing value of hyperspectral remote sensing of plant pigments.
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                Author and article information

                Contributors
                Journal
                Front. Agr. Sci. Eng.
                FASE
                CN10-1204/S
                Frontiers of Agricultural Science and Engineering
                Higher Education Press
                2095-7505
                2095-977X
                2018
                : 5
                : 4
                : 406-419
                Affiliations
                [1 ]. College of Mechanical and Electrical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
                [2 ]. Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66506, USA
                Author notes
                zhangn@ksu.edu
                Article
                10.15302/J-FASE-2018242
                7d40560e-9314-40f2-9b43-50ace624a03f

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/

                History
                : 2 March 2018
                : 10 September 2018
                Categories
                REVIEW

                Crops,Plant science & Botany,Agriculture,Animal science & Zoology,Biotechnology,Genetics
                pathological traits,morphological traits,high-throughput phenotyping,physiological traits,imaging technology

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