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RUST:一种用于快速测量谷物叶片锈病的强大且用户友好的脚本工具。

RUST: A Robust, User-Friendly Script Tool for Rapid Measurement of Rust Disease on Cereal Leaves.

作者信息

Gallego-Sánchez Luis M, Canales Francisco J, Montilla-Bascón Gracia, Prats Elena

机构信息

CSIC-Institute for Sustainable Agriculture, 14004 Cordoba, Spain.

出版信息

Plants (Basel). 2020 Sep 11;9(9):1182. doi: 10.3390/plants9091182.

DOI:10.3390/plants9091182
PMID:32932900
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7576472/
Abstract

Recently, phenotyping has become one of the main bottlenecks in plant breeding and fundamental plant science. This is particularly true for plant disease assessment, which has to deal with time-consuming evaluations and the subjectivity of visual assessments. In this work, we have developed an open source Robust, User-friendy Script Tool (RUST) for semi-automated evaluation of leaf rust diseases. RUST runs under the free Fiji imaging software (developed from ImageJ), which is a well-recognized software among the scientific community. The script enables the evaluation of leaf rust diseases using a color transformation tool and provides three different automation modes. The script opens images sequentially and records infection frequency (pustules per area) (semi-)automatically for high-throughput analysis. Furthermore, it can manage several scanned leaf segments in the same image, consecutively selecting the desired segments. The script has been validated with nearly 900 samples from 80 oat genotypes ranging from resistant to susceptible and from very light to heavily infected leaves showing a high accuracy with a Lin's concordance correlation coefficient of 0.99. The analysis show a high repeatability as indicated by the low variation coefficients obtained when repeating the measurement of the same samples. The script also has optional steps for calibration and training to ensure accuracy, even in low-resolution images. This script can evaluate efficiently hundreds of leaves facilitating the screening of novel sources of resistance to this important cereal disease.

摘要

最近,表型分析已成为植物育种和基础植物科学的主要瓶颈之一。对于植物病害评估而言尤其如此,因为它必须应对耗时的评估以及视觉评估的主观性。在这项工作中,我们开发了一种开源的稳健、用户友好脚本工具(RUST),用于叶锈病的半自动评估。RUST在免费的Fiji成像软件(由ImageJ开发)下运行,该软件在科学界是一款广为人知的软件。该脚本通过颜色转换工具实现叶锈病评估,并提供三种不同的自动化模式。该脚本按顺序打开图像,并(半)自动记录感染频率(每面积的脓疱数)以进行高通量分析。此外,它可以管理同一图像中的多个扫描叶片段,连续选择所需的段。该脚本已用来自80个燕麦基因型的近900个样本进行了验证,这些样本涵盖从抗性到感病以及从极轻度感染到重度感染的叶片,林氏一致性相关系数为0.99,显示出高精度。分析表明,重复测量同一样本时获得的低变异系数表明具有高重复性。该脚本还有用于校准和训练的可选步骤,即使在低分辨率图像中也能确保准确性。该脚本可以高效评估数百片叶子,有助于筛选对这种重要谷物病害的新型抗性来源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/83d0f180725f/plants-09-01182-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/957575a15cce/plants-09-01182-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/f3a8889fa002/plants-09-01182-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/1c689f8a77f0/plants-09-01182-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/8bf59007a99a/plants-09-01182-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/83d0f180725f/plants-09-01182-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/957575a15cce/plants-09-01182-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/f3a8889fa002/plants-09-01182-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/1c689f8a77f0/plants-09-01182-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/8bf59007a99a/plants-09-01182-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5015/7576472/83d0f180725f/plants-09-01182-g005.jpg

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