标题： 多尺度小麦叶片表面的数据融合：使用径向基函数单位分解方法的统一方法 显示英文标题

标题： Data fusion for a multi-scale model of a wheat leaf surface: a unifying approach using a radial basis function partition of unity method

摘要： 真实数字植物叶片模型对于优化农用化学品喷洒技术的液滴流体动力学模拟至关重要。 表面上的小特征（约100$\mathrm{\mu m}$的量级）如脊和毛发的存在及其性质已被证明会显著影响液滴蒸发，从而影响叶片对有效成分的吸收潜力。 我们表明，这些微结构可以通过从显微CT扫描数据集中得到的隐式径向基函数单位分解（RBFPU）表面重建来捕捉。 然而，由于极高的数据存储需求和扫描仪时间限制，以微米分辨率扫描整个叶片（$20\mathrm{cm^2}$）是不可行的。 相反，我们仅对一小段小麦叶片（$4\mathrm{mm^2}$）进行显微CT扫描。 我们对该片段拟合一个RBFPU隐式表面，并对整个叶片的低分辨率激光扫描拟合一个显式RBFPU表面。 使用局部正交坐标系对叶片进行参数化后，我们将在现在解析的微结构在更大的、粗糙的叶片表面表示上多次复制，该表示捕捉了重要的宏观尺度特征，如其大小、形状和方向。 通过单位分解方法，微结构模型的一个片段边缘自然地融入到其相邻片段中。 结果是一个隐式表面重建，能够同时捕捉小麦叶片的微观和宏观尺度特征。 显示英文摘要

摘要： Realistic digital models of plant leaves are crucial to fluid dynamics simulations of droplets for optimising agrochemical spray technologies. The presence and nature of small features (on the order of 100$\mathrm{\mu m}$) such as ridges and hairs on the surface have been shown to significantly affect the droplet evaporation, and thus the leaf's potential uptake of active ingredients. We show that these microstructures can be captured by implicit radial basis function partition of unity (RBFPU) surface reconstructions from micro-CT scan datasets. However, scanning a whole leaf ($20\mathrm{cm^2}$) at micron resolutions is infeasible due to both extremely large data storage requirements and scanner time constraints. Instead, we micro-CT scan only a small segment of a wheat leaf ($4\mathrm{mm^2}$). We fit a RBFPU implicit surface to this segment, and an explicit RBFPU surface to a lower resolution laser scan of the whole leaf. Parameterising the leaf using a locally orthogonal coordinate system, we then replicate the now resolved microstructure many times across a larger, coarser, representation of the leaf surface that captures important macroscale features, such as its size, shape, and orientation. The edge of one segment of the microstructure model is blended into its neighbour naturally by the partition of unity method. The result is one implicit surface reconstruction that captures the wheat leaf's features at both the micro- and macro-scales.