Centre for Plant Integrative Biology, School of Biosciences, University of Nottingham, Sutton Bonington, LE12 5RD, United Kingdom.
Agrosphere, IBG3, Forschungszentrum Jülich, Jülich 52425, Germany.
Gigascience. 2017 Oct 1;6(10):1-7. doi: 10.1093/gigascience/gix084.
Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify the quantitative trait loci that had previously been discovered using a semi-automated method. We have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput of large-scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other areas of plant phenotyping.
植物根系的遗传分析需要大量提取结构特征的数据集。为了从根系图像中定量这些特征，研究人员通常不得不在自动化工具（容易出错且仅提取有限数量的结构特征）和半自动化工具（非常耗时）之间做出选择。我们训练了一个随机森林算法，从自动提取的图像描述符中推断结构特征。训练是在数据集的一个子集上进行的，然后应用于整个数据集。该策略使我们能够（i）将图像分析时间减少 73%，（ii）基于图像描述符提取有意义的结构特征。我们还表明，这些特征足以识别以前使用半自动方法发现的数量性状位点。我们已经表明，将半自动图像分析与机器学习算法相结合，具有提高大规模根系研究通量的能力。我们预计这种方法将能够对遗传研究中更复杂的根系进行量化。我们还认为，我们的方法可以扩展到植物表型的其他领域。
