  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.
