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Analysis of Stomata Distribution Patterns for Quantification of the Foliar Plasticity of Tradescantia Zebrina

机译:贸易型叶片叶面可塑性量化的气孔分布模式分析

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Here we propose a method for the analysis of the stomata distribution patterns on the surface of plant leaves. We also investigate how light exposure during growth can affect stomata distribution and the plasticity of leaves. Understanding foliar plasticity (the ability of leaves to modify their structural organization to adapt to changing environmental resources) is a fundamental problem in Agricultural and Environmental Sciences. Most published work on quantification of stomata has concentrated on descriptions of their density per unit of leaf area, however density alone does not provide a complete description of the problem and leaves several unanswered questions (e.g. whether the stomata patterns change across various areas of the leaf, or how the patterns change under varying observational scales). We used two approaches here, to know, multiscale fractal dimension and complex networks, as a means to provide a description of the complexity of these distributions. In the experiments, we used 18 samples from the plant Tradescantia Zebrina grown under three different conditions (4 hours of artificial light each day, 24 hours of artificial light each day, and sunlight) for a total of 69 days. The network descriptors were capable of correctly discriminating the different conditions in 88% of cases, while the fractal descriptors discriminated 83% of the samples. This is a significant improvement over the correct classification rates achieved when using only stomata density (56% of the samples).
机译:在这里,我们提出了一种分析植物叶子表面上的气孔分布图案的方法。我们还研究了生长期间的曝光程度如何影响气孔分布和叶子的可塑性。了解叶面可塑性(叶片改变其结构组织适应改变环境资源的能力)是农业和环境科学的根本问题。大多数公布的气孔量化的工作都集中在其每单位叶面积密度的描述中,然而,单独的密度不提供对问题的完整描述,并且留下了几个未答复的问题(例如,气孔模式是否在叶子的各个区域上变化或者模式如何在不同的观察尺度下变化)。我们在这里使用了两种方法,知道多尺度分形维数和复杂网络,作为提供这些分布的复杂性的描述。在实验中,我们使用了18个来自植物Tradescantia Zebrina的样品在三种不同的条件下生长(每天4小时的人造光,每天24小时,以及阳光)共69天。网络描述符能够在88%的情况下正确地辨别不同的条件,而分形描述符在83%的样品中受到区分。这是在仅使用仅使用气孔密度(56%的样本)时实现的正确分类率的显着改进。

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