Automated identification of plants has improved consider-ably thanks to the recent progress in deep learning and the availabilityof training data. However, this profusion of data only concerns a few tensof thousands of species, while the planet has nearly 369K. The LifeCLEF2019 Plant Identification challenge (or ”PlantCLEF 2019”) was designedto evaluate automated identification on the flora of data deficient regions.It is based on a dataset of 10K species mainly focused on the Guianashield and the Northern Amazon rainforest, an area known to have oneof the greatest diversity of plants and animals in the world. As in theprevious edition, a comparison of the performance of the systems eval-uated with the best tropical flora experts was carried out. This paperpresents the resources and assessments of the challenge, summarizes theapproaches and systems employed by the participating research groups,and provides an analysis of the main outcomes.