  Herbaria worldwide are housing a treasure of 100s of millions of herbarium
specimens, which are increasingly being digitized in recent years and thereby
made more easily accessible to the scientific community. At the same time, deep
learning algorithms are rapidly improving pattern recognition from images and
these techniques are more and more being applied to biological objects. We are
using digital images of herbarium specimens in order to identify taxa and
traits of these collection objects by applying convolutional neural networks
(CNN). Images of the 1000 species most frequently documented by herbarium
specimens on GBIF have been downloaded and combined with morphological trait
data, preprocessed and divided into training and test datasets for species and
trait recognition. Good performance in both domains is promising to use this
approach in future tools supporting taxonomy and natural history collection
management.
