Camargo Anyela, Papadopoulou Dimitra, Spyropoulou Zoi, Vlachonasios Konstantinos, Doonan John H, Gay Alan P
Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Gogerddan, Aberystwyth, Ceredigion, United Kingdom.
Aristotle University of Thessaloniki, Faculty of Science, School of Biology, Department of Botany, Thessaloniki, Greece.
PLoS One. 2014 May 7;9(5):e96889. doi: 10.1371/journal.pone.0096889. eCollection 2014.
Computer-vision based measurements of phenotypic variation have implications for crop improvement and food security because they are intrinsically objective. It should be possible therefore to use such approaches to select robust genotypes. However, plants are morphologically complex and identification of meaningful traits from automatically acquired image data is not straightforward. Bespoke algorithms can be designed to capture and/or quantitate specific features but this approach is inflexible and is not generally applicable to a wide range of traits. In this paper, we have used industry-standard computer vision techniques to extract a wide range of features from images of genetically diverse Arabidopsis rosettes growing under non-stimulated conditions, and then used statistical analysis to identify those features that provide good discrimination between ecotypes. This analysis indicates that almost all the observed shape variation can be described by 5 principal components. We describe an easily implemented pipeline including image segmentation, feature extraction and statistical analysis. This pipeline provides a cost-effective and inherently scalable method to parameterise and analyse variation in rosette shape. The acquisition of images does not require any specialised equipment and the computer routines for image processing and data analysis have been implemented using open source software. Source code for data analysis is written using the R package. The equations to calculate image descriptors have been also provided.
基于计算机视觉的表型变异测量对于作物改良和粮食安全具有重要意义，因为它们本质上是客观的。因此，应该有可能使用这种方法来选择健壮的基因型。然而，植物在形态上很复杂，从自动获取的图像数据中识别有意义的性状并非易事。可以设计定制算法来捕获和/或量化特定特征，但这种方法缺乏灵活性，通常不适用于广泛的性状。在本文中，我们使用行业标准的计算机视觉技术从在非刺激条件下生长的遗传多样性拟南芥莲座叶的图像中提取了广泛的特征，然后使用统计分析来识别那些能够很好地区分生态型的特征。该分析表明，几乎所有观察到的形状变异都可以用5个主成分来描述。我们描述了一个易于实现的流程，包括图像分割、特征提取和统计分析。这个流程提供了一种经济高效且本质上可扩展的方法来参数化和分析莲座叶形状的变异。图像采集不需要任何专门设备，并且图像处理和数据分析的计算机程序已使用开源软件实现。数据分析的源代码使用R包编写。还提供了计算图像描述符的公式。
