Interdisciplinary project funded by the Swiss Data Science Center (ETH board, 2020-2022; awarded to Niklaus E. Zimmermann, and co-PIs Patrice Decombes, Dirk N. Karger and Damaris Zurell).
Recent advances in image classification have fostered the advent of user-friendly mobile apps helping amateurs to identify species from images with great potential for citizen science. These apps are working well for common species with distinct morphological features. However, they tend to perform badly for less common species and for groups of species with very similar morphological characteristics. Many Swiss plant species, for example, are not recognized well by the current apps. Here, we propose a novel approach combining machine learning-based image recognition with spatially explicit ecological and morphological meta-information for the identification of the c. 4’000 Swiss plant species from georeferenced pictures. This combination is expected to considerably improve species identification as new images are not only classified according to visual features but also with regards to ecological and geographical plausibility. This will greatly aid data acquisition by citizen scientists.