Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection

The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evalu...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports Vol. 9; no. 1; p. 7610
Main Authors: Sakoda, Kazuma, Watanabe, Tomoya, Sukemura, Shun, Kobayashi, Shunzo, Nagasaki, Yuichi, Tanaka, Yu, Shiraiwa, Tatsuhiko
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 20.05.2019
Nature Publishing Group
Subjects:
631/449/1734
631/449/1870
631/449/2124
Algorithms
Alleles
Breeding - methods
Deep Learning
Genetic diversity
Genetic Variation - genetics
Genotype
Glycine max
Glycine max - genetics
High-Throughput Screening Assays - methods
Humanities and Social Sciences
multidisciplinary
Photosynthesis
Photosynthesis - genetics
Plant Leaves - genetics
Plant Stomata - genetics
Population genetics
Science
Science (multidisciplinary)
Sensors
Soybeans
Stomata
ISSN:2045-2322, 2045-2322
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The stomatal density (SD) can be a promising target to improve the leaf photosynthesis in soybeans ( Glycine max (L.) Merr). In a conventional SD evaluation, the counting process of the stomata during a manual operation can be time-consuming. We aimed to develop a high-throughput technique for evaluating the SD and elucidating the variation in the SD among various soybean accessions. The central leaflet of the first trifoliolate was sampled, and microscopic images of the leaflet replica were obtained among 90 soybean accessions. The Single Shot MultiBox Detector, an algorithm for an object detection based on deep learning, was introduced to develop an automatic detector of the stomata in the image. The developed detector successfully recognized the stomata in the microscopic image with high-throughput. Using this technique, the value of R 2 reached 0.90 when the manually and automatically measured SDs were compared in the 150 images. This technique discovered a variation in SD from 93 ± 3 to 166 ± 4 mm −2 among the 90 accessions. Our detector can be a powerful tool for a SD evaluation with a large-scale population in crop species, accelerating the identification of useful alleles related to the SD in future breeding programs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-44127-0