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利用从二维图像获得的四边形模型评估七个六倍体小麦物种和一个人工双二倍体的穗多样性

Evaluation of the Spike Diversity of Seven Hexaploid Wheat Species and an Artificial Amphidiploid Using a Quadrangle Model Obtained from 2D Images.

作者信息

Komyshev Evgenii G, Genaev Mikhail A, Kruchinina Yuliya V, Koval Vasily S, Goncharov Nikolay P, Afonnikov Dmitry A

机构信息

Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia.

Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia.

出版信息

Plants (Basel). 2024 Sep 30;13(19):2736. doi: 10.3390/plants13192736.

Abstract

The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2 = 6 = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.

摘要

穗形和形态测量特征是栽培谷物的关键特征之一,与它们的生产力相关。这些特征常被用于六倍体小麦物种的植物分类学和真伪鉴定。手动测量穗特征既繁琐又不精确。最近,本研究的作者开发了一种利用二维图像分析进行小麦穗形态测量的方法。在此,该方法被应用于研究7个六倍体(2n = 6x = 42)小麦物种的190株植株以及一个人工双二倍体的穗大小和形状变化。分析了5个手动估计的穗特征和从数字图像分析中获得的26个特征。基于图像的特征描述了穗基部、中部和顶部的特征以及常见参数(圆形度、圆度、周长等)。手动测量和图像分析对相似特征的估计显示出高度相关性,这表明数字穗表型分析具有实际重要性。展示了穗特征在分类为不同类型(斯佩尔特小麦、普通小麦和紧凑型)以及物种或双二倍体方面的效用。还证明了所获得的估计值能够识别不同物种之间或同一物种内不同种质之间显著不同的穗特征。目前的工作表明,使用基于数字图像分析获得的特征的方法进行小麦穗形状分析是有用的。

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