Viaud Gautier, Loudet Olivier, Cournède Paul-Henry
Laboratory MICS, CentraleSupélec, University of Paris-Saclay Châtenay-Malabry, France.
Institut Jean-Pierre Bourgin, INRA, AgroParisTech, CNRS, Université Paris-Saclay Versailles, France.
Front Plant Sci. 2017 Jan 11;7:2057. doi: 10.3389/fpls.2016.02057. eCollection 2016.
A promising method for characterizing the phenotype of a plant as an interaction between its genotype and its environment is to use refined organ-scale plant growth models that use the observation of architectural traits, such as leaf area, containing a lot of information on the whole history of the functioning of the plant. The Phenoscope, a high-throughput automated platform, allowed the acquisition of zenithal images of over twenty one days for 4 different genotypes. A novel image processing algorithm involving both segmentation and tracking of the plant leaves allows to extract areas of the latter. First, all the images in the series are segmented independently using a watershed-based approach. A second step based on ellipsoid-shaped leaves is then applied on the segments found to refine the segmentation. Taking into account all the segments at every time, the whole history of each leaf is reconstructed by choosing recursively through time the most probable segment achieving the best score, computed using some characteristics of the segment such as its orientation, its distance to the plant mass center and its area. These results are compared to manually extracted segments, showing a very good accordance in leaf rank and that they therefore provide low-biased data in large quantity for leaf areas. Such data can therefore be exploited to design an organ-scale plant model adapted from the existing GreenLab model for and subsequently parameterize it. This calibration of the model parameters should pave the way for differentiation between the Arabidopsis genotypes.
一种将植物表型表征为其基因型与环境之间相互作用的有前景的方法是使用精细的器官尺度植物生长模型，该模型利用对诸如叶面积等结构特征的观测，这些特征包含了关于植物整个功能历史的大量信息。“植物表型观测平台”（Phenoscope）是一个高通量自动化平台，它能够在二十一天多的时间里获取4种不同基因型植物的天顶图像。一种涉及植物叶片分割和跟踪的新型图像处理算法可以提取叶片的面积。首先，使用基于分水岭算法的方法对该系列中的所有图像进行独立分割。然后，对分割得到的部分应用基于椭圆形叶片的第二步处理，以细化分割。考虑到每个时刻的所有部分，通过在时间上递归选择得分最高的最可能部分来重建每片叶子的整个历史，得分是根据该部分的一些特征（如方向、到植物质心的距离和面积）计算得出的。将这些结果与手动提取的部分进行比较，结果显示在叶片排序方面非常吻合，因此它们为叶面积提供了大量低偏差的数据。这些数据因此可以被用于设计一个基于现有GreenLab模型改编的器官尺度植物模型，并随后对其进行参数化。模型参数的这种校准应该为区分拟南芥基因型铺平道路。
