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. 2020 Jan 1;20(1):248.
doi: 10.3390/s20010248.

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

JeongHo Baek  1 , Eungyeong Lee  1 , Nyunhee Kim  1 , Song Lim Kim  1 , Inchan Choi  1 , Hyeonso Ji  1 , Yong Suk Chung  2 , Man-Soo Choi  3 , Jung-Kyung Moon  1 , Kyung-Hwan Kim  1
Affiliations

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

JeongHo Baek et al. Sensors (Basel). .

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.

Keywords: RGB image; breeding; seed color; seed morphology; seed traits.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow-chart for high-throughput soybean seed phenotype analysis. The picture using raw image file is divided into four. (A) Data creation; (B) Data processing; (C) Data analysis; (D) Options.
Figure 2
Figure 2
Device of soybean seed phenotype analysis used in high-throughput method. (a) top and side camera; (b) arrangement of soybean seeds; (c) actual measurement using Vernier calipers.
Figure 3
Figure 3
Distribution of morphological data measurement of 400 lines. It is a distribution chart that calculates the average area, perimeter, width, height, thickness, circularity, roundness, solidity of each lines.
Figure 4
Figure 4
Classification of morphological phenotype data of the Roundness. A red box (A, B, C, D, E) with an elongated shape has a roundness value of 0.4~0.5 and a green box (F, G, H, I, J) with a close to a circle shape has a roundness value of 0.8 or more.
Figure 5
Figure 5
Detecting specific morphology using the Solidity. Good seeds are A, B, C and bad seeds are D, E, F.
Figure 6
Figure 6
Grayscale graph analysis of cc2-003 line. A green box A shows the results C, D, E using the mean value and a red box B shows the results F, G, H using the mean value.
Figure 7
Figure 7
Quantitative color classifications of soybean seed using Red, Green, Blue (RGB) histogram. The figure shows each line that the seed image, the RGB histogram, and the color code of red box.
Figure 8
Figure 8
Correlation between actual measurement and image measurement of randomly selected 9 lines.
See this image and copyright information in PMC

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