  This paper discusses the potential of applying deep learning techniques for
plant classification and its usage for citizen science in large-scale
biodiversity monitoring. We show that plant classification using near
state-of-the-art convolutional network architectures like ResNet50 achieves
significant improvements in accuracy compared to the most widespread plant
classification application in test sets composed of thousands of different
species labels. We find that the predictions can be confidently used as a
baseline classification in citizen science communities like iNaturalist (or its
Spanish fork, Natusfera) which in turn can share their data with biodiversity
portals like GBIF.
