Abstract
The digitisation of plant collections is bringing large quantities of information into accessible electronic databases. However, in recent decades, traditional taxonomic work in collections has declined, meaning that more specimens are only determined to family or genus, particularly when lacking key identification structures. If unaddressed, large-scale digitisation risks widening the gap between well-studied species and those lacking data.</p><p>Hyperspectral reflectance and computer vision are two emerging approaches for identifying species, but these have yet to be cross-compared for herbarium-based taxonomy. Using Lithocarpus species as a case study, we compared classification accuracy obtained from leaf reflectance spectra with computer vision (implemented via Pl@ntNet), a RGB (red, green, blue) image-based approach known to work well on specimens presenting reproductive structures. In the spectral approach, we assessed how much data are needed to optimise classification accuracy, how many species could be discriminated between, and whether close relatives were more frequently confounded.</p><p>We found that Lithocarpus herbarium specimens were accurately identified to species from relatively small spectral datasets. Despite not incorporating reproductive structures, this was only 14% less accurate than Pl@ntNet.</p><p>We suggest these rapid, nondestructive leaf reflectance measurements, paired with computer vision, could fill identification gaps in collections, particularly for specimens lacking reproductive features.</p></div>