Large scale biodiversity monitoring is essential for sustain-able development (earth stewardship). With the recent ad-vances in computer vision, we see the emergence of more and more effective identification tools allowing to set-up large-scale data collection platforms such as the popular Pl@ntNet initiative. Although it still covers only a fraction of the world flora, this platform is already being used by more than 300K people who produce tens of thousands of validated plant ob-servations each year. Nevertheless, this explicitly shared and validated data is only the tip of the iceberg. The real po-tential relies on the millions of raw image queries submitted by the users of the mobile application but for which there is no human validation at all. Allowing the exploitation of such contents in a fully automatic way could scale up the world-wide collection of plant observations by several orders of magnitude. In this paper, we first survey existing auto-mated plant identification systems through a five-year syn-thesis of the PlantCLEF benchmark and an impact study of the Pl@ntNet platform. We then focus more specifically on the implicit monitoring scenario and discuss several new re-lated research challenges. Finally, we discuss the results of a preliminary experimental study focused on the implicit mon-itoring of invasive species in mobile search logs. We show that the results are very promising but that there is still some room for improvement before being able to automat-ically share such implicit observations within international biodiversity platforms.