The global loss of biodiversity is one of the most pressing environmental issues of our time. Effective conservation efforts require a precise understanding of the spatiotemporal patterns of biodiversity and how they change over time. However, biodiversity monitoring is labor-intensive, heavily relying on individual expertise to correctly identify species in the field. In-situ species identification is almost impossible for untrained people and challenging even for professionals. For these reasons, there has long been interest in developing automated species identification systems. Recent increases in data availability, along with major advances in machine learning algorithms, especially deep convolutional neural networks, have brought these systems to a 'production-ready' state. Automated species identification now has the potential to greatly enhance biodiversity and conservation research. The project partners at the Max Planck Institute for Biogeochemistry and Technische Universität Ilmenau have developed Flora Incognita, an app for automated plant identification. This talk will provide insights into the Flora Incognita project and discuss how automated plant identification can contribute to biodiversity monitoring.