Background Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Conclusions Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

中文翻译：

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使用 X 射线显微计算机断层扫描对小麦籽粒性状进行无损、高含量分析。

背景 小麦是温带气候中种植最广泛的作物之一，可用作食物和动物饲料。为了满足不断变化的气候中预计的人口增长需求，小麦产量需要大幅增加。穗状花序和谷物性状是最终产量的关键决定因素，而谷物均匀度是商业上所需的性状，但它们的分析很费力，并且通常需要破坏性收获。当前的挑战之一是开发一种能够处理大量种群的准确、非破坏性的穗和谷物性状分析方法。结果 在本研究中，我们描述了一种稳健方法的开发，用于从 X 射线微计算机断层扫描 (μCT) 获得的图像中准确提取和测量尖峰和晶粒形态参数。开发的图像分析管道可自动识别 μCT 图像中感兴趣的植物材料，执行图像分析并提取形态数据。作为原理证明，这种综合方法被用于分析在两种不同水情下遭受高温的小麦植物种群的尖峰。温度对穗高和粒数有负面影响，穗中部是受影响最大的区域。数据还证实，在轻度胁迫下，增加的谷物体积与减少的谷物数量相关。结论 能够以非破坏性方式快速测量植物表型对于促进我们对基因功能和环境影响的理解至关重要。我们报告了一种图像分析管道的开发，该管道能够准确可靠地从作物中提取穗状花序和谷物性状，而不会丢失位置信息。这种方法被应用于小麦穗的分析，可以很容易地应用于其他经济上重要的作物物种。