Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection
Genetic Diversity in Stomatal Density among Soybeans Elucidated Using High-throughput Technique Based on an Algorithm for Object Detection
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DOI：
10.1038/s41598-019-44127-0
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发表时间：
2019-05-20
影响因子：
4.6
通讯作者：
Shiraiwa, Tatsuhiko
中科院分区：
文献类型：
--
作者：
Sakoda, Kazuma;Watanabe, Tomoya;Shiraiwa, Tatsuhiko
气孔密度（SD）是提高大豆叶片光合作用的一个有效指标。梅尔）。在传统的SD评估中，人工操作期间的气孔计数过程可能是耗时的。我们的目的是发展一种高通量的技术来评估SD和阐明SD在不同的大豆种质之间的变化。对90份大豆材料的第一片三小叶中央小叶进行了取样，并获得了小叶复制品的显微图像。介绍了基于深度学习的目标检测算法Single Shot MultiBox Detector，用于开发图像中气孔的自动检测器。所开发的检测器成功地识别气孔的显微图像与高通量。使用这种技术，当在150幅图像中比较手动和自动测量的SD时，R-2的值达到0.90。该技术发现90份材料的SD在93 +/-3到166 +/-4mm（-2）之间变化。我们的检测器可以是一个强大的工具，SD评估与大规模人口的作物物种，加速识别有用的等位基因相关的SD在未来的育种计划。
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 +/- 4mm(-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.