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Realistic digital models of plant leaves are crucial to fluid dynamicssimulations of droplets for optimising agrochemical spray technologies. Thepresence 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 affectthe droplet evaporation, and thus the leaf's potential uptake of activeingredients. We show that these microstructures can be captured by implicitradial basis function partition of unity (RBFPU) surface reconstructions frommicro-CT scan datasets. However, scanning a whole leaf ($20\mathrm{cm^2}$) atmicron resolutions is infeasible due to both extremely large data storagerequirements and scanner time constraints. Instead, we micro-CT scan only asmall segment of a wheat leaf ($4\mathrm{mm^2}$). We fit a RBFPU implicitsurface to this segment, and an explicit RBFPU surface to a lower resolutionlaser scan of the whole leaf. Parameterising the leaf using a locallyorthogonal coordinate system, we then replicate the now resolved microstructuremany times across a larger, coarser, representation of the leaf surface thatcaptures important macroscale features, such as its size, shape, andorientation. The edge of one segment of the microstructure model is blendedinto its neighbour naturally by the partition of unity method. The result isone implicit surface reconstruction that captures the wheat leaf's features atboth the micro- and macro-scales.

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