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      High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis

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          Abstract

          Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.

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          SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis.

          Seed shape and size are among the most important agronomic traits because they affect yield and market price. To obtain accurate seed size data, a large number of measurements are needed because there is little difference in size among seeds from one plant. To promote genetic analysis and selection for seed shape in plant breeding, efficient, reliable, high-throughput seed phenotyping methods are required. We developed SmartGrain software for high-throughput measurement of seed shape. This software uses a new image analysis method to reduce the time taken in the preparation of seeds and in image capture. Outlines of seeds are automatically recognized from digital images, and several shape parameters, such as seed length, width, area, and perimeter length, are calculated. To validate the software, we performed a quantitative trait locus (QTL) analysis for rice (Oryza sativa) seed shape using backcrossed inbred lines derived from a cross between japonica cultivars Koshihikari and Nipponbare, which showed small differences in seed shape. SmartGrain removed areas of awns and pedicels automatically, and several QTLs were detected for six shape parameters. The allelic effect of a QTL for seed length detected on chromosome 11 was confirmed in advanced backcross progeny; the cv Nipponbare allele increased seed length and, thus, seed weight. High-throughput measurement with SmartGrain reduced sampling error and made it possible to distinguish between lines with small differences in seed shape. SmartGrain could accurately recognize seed not only of rice but also of several other species, including Arabidopsis (Arabidopsis thaliana). The software is free to researchers.
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            A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding

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              Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap.

              Plants are affected by complex genome×environment×management interactions which determine phenotypic plasticity as a result of the variability of genetic components. Whereas great advances have been made in the cost-efficient and high-throughput analyses of genetic information and non-invasive phenotyping, the large-scale analyses of the underlying physiological mechanisms lag behind. The external phenotype is determined by the sum of the complex interactions of metabolic pathways and intracellular regulatory networks that is reflected in an internal, physiological, and biochemical phenotype. These various scales of dynamic physiological responses need to be considered, and genotyping and external phenotyping should be linked to the physiology at the cellular and tissue level. A high-dimensional physiological phenotyping across scales is needed that integrates the precise characterization of the internal phenotype into high-throughput phenotyping of whole plants and canopies. By this means, complex traits can be broken down into individual components of physiological traits. Since the higher resolution of physiological phenotyping by 'wet chemistry' is inherently limited in throughput, high-throughput non-invasive phenotyping needs to be validated and verified across scales to be used as proxy for the underlying processes. Armed with this interdisciplinary and multidimensional phenomics approach, plant physiology, non-invasive phenotyping, and functional genomics will complement each other, ultimately enabling the in silico assessment of responses under defined environments with advanced crop models. This will allow generation of robust physiological predictors also for complex traits to bridge the knowledge gap between genotype and phenotype for applications in breeding, precision farming, and basic research.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                01 January 2020
                January 2020
                : 20
                : 1
                : 248
                Affiliations
                [1 ]National Institute of Agricultural Sciences, Rural Development Administration (RDA), Jeonju 54874, Korea; firstleon@ 123456korea.kr (J.B.); wowlek44@ 123456korea.kr (E.L.); knh702@ 123456korea.kr (N.K.); greenksl5405@ 123456korea.kr (S.L.K.); inchchoi@ 123456korea.kr (I.C.); jhs77@ 123456korea.kr (H.J.); moonjk2@ 123456korea.kr (J.-K.M.)
                [2 ]Faculty of Bioscience and Industry, College of Applied Life Science, SARI, Jeju National University, Jeju 63243, Korea; yschung@ 123456jejunu.ac.kr
                [3 ]National Institute of Crop Sciences, Rural Development Administration (RDA), Wanju-gun 55365, Korea; mschoi73@ 123456korea.kr
                Author notes
                [* ]Correspondence: biopiakim@ 123456korea.kr ; Tel.: +82-63-238-4658
                [†]

                These authors also contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-3121-7600
                Article
                sensors-20-00248
                10.3390/s20010248
                6982885
                31906262
                13fb3e7c-88d3-4c04-9ea4-866853216c37
                © 2020 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 (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 October 2019
                : 28 November 2019
                Categories
                Article

                Biomedical engineering
                rgb image,seed morphology,seed color,seed traits,breeding
                Biomedical engineering
                rgb image, seed morphology, seed color, seed traits, breeding

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