Premise of the study: A species distribution model computed with automatically identified plant observations was developed and evaluated to contribute to future ecological studies.
Methods: We used deep learning techniques to automatically identify opportunistic plant observations made by citizens through a popular mobile application. We compared species distribution modeling of invasive alien plants based on these data to inventories made by experts.
Results: The trained models have a reasonable predictive effectiveness for some species, but they are biased by the massive presence of cultivated specimens.
Discussion: The method proposed here allows for fine-grained and regular monitoring of some species of interest based on opportunistic observations. More in-depth investigation of the typology of the observations and the sampling bias should help improve the approach in the future.
Keywords: automated species identification; citizen science; crowdsourcing; deep learning; invasive alien species; species distribution modeling.