Wang Qiuran, Barbariol Tommaso, Susto Gian Antonio, Bonato Bianca, Guerra Silvia, Castiello Umberto
Department of General Psychology, University of Padova, 35132 Padova, Italy.
Department of Information Engineering, University of Padova, 35131 Padova, Italy.
Plants (Basel). 2023 Feb 20;12(4):965. doi: 10.3390/plants12040965.
Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support demonstrate greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled during support searching remains unclear. To fill this gap, here we tested whether simulation-based machine learning methods can capture differences in movement patterns nested in actual kinematical data. We compared machine learning classifiers with the aim of generating models that learn to discriminate between circumnutation patterns related to the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features at the level of the junction underneath the tendrils that seems to be a superior indicator for discerning the presence/absence of the support by the plant. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.
攀缘植物需要外部支撑来垂直生长并增加光照获取。找到合适支撑的攀缘植物比那些匍匐生长的表现出更好的性能和适应性。寻找支撑的特征是振荡运动（即回旋转头运动），植物在生长过程中围绕中心轴旋转。许多研究已经阐明了回旋转头运动的机制细节，但在寻找支撑过程中这种现象是如何被控制的仍不清楚。为了填补这一空白，我们在此测试基于模拟的机器学习方法是否能够捕捉嵌套在实际运动学数据中的运动模式差异。我们比较了机器学习分类器，目的是生成能够学会区分与环境中支撑物存在与否相关的回旋转头模式的模型。结果表明，根据支撑物的存在与否，回旋转头模式存在差异，并且可以相当准确地学习和分类。我们还在卷须下方的连接处水平识别出独特的运动学特征，这似乎是植物辨别支撑物存在与否的一个更优指标。总体而言，机器学习方法似乎是理解植物运动的有力工具。
