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.

翻译：真实感植物叶片数字模型对于优化农用化学喷雾技术的液滴流体动力学模拟至关重要。研究表明，叶片表面微米级（约100$\mathrm{\mu m}$）的脊状突起和绒毛等细微特征显著影响液滴蒸发过程，进而影响叶片对活性成分的吸收潜力。我们证明，通过显微CT扫描数据集采用隐式径向基函数单位分解（RBFPU）曲面重构可以捕捉这些微观结构。然而，在微米分辨率下扫描整个叶片（$20\mathrm{cm^2}$）因海量数据存储需求和扫描时间限制而不可行。为此，我们仅对小麦叶片小片段（$4\mathrm{mm^2}$）进行显微CT扫描，构建该片段的RBFPU隐式曲面，同时对整片叶片较低分辨率的激光扫描数据构建显式RBFPU曲面。通过局部正交坐标系参数化叶片，我们将已解析的微观结构在更大尺度、更粗略的叶片表面表征上多次复制，同时保留其尺寸、形状和朝向等重要宏观特征。通过单位分解法，微观结构模型相邻片段的边缘自然融合，最终形成能够同时捕捉小麦叶片微观和宏观特征的单一隐式曲面重构。