Speaker
Description
One third of the world's population suffers from pollen-induced respiratory allergies. For those affected, knowing the local pollen loads and thus the local risk of allergy symptoms is of the utmost importance for their daily life and medication. However, real data on pollen abundance needs to combine knowledge of flowering times, pollen release and ultimately local pollen occurrence, and this kind of information is not widely available. The currently available pollen forecasting models, although undoubtedly helpful, are based on very limited data in terms of adequate real-time flowering information, pollen abundance, resolution, and considered species.
The PollenNet project aims to provide accurate predictions of local pollen loads for specific species and even the local risk of allergy symptoms. In order to address the lack of local data on the flowering of allergenic plants, we will use the real-time data that is provided by users of the Flora Incognita plant identification app. Thousands of geolocalised observations of allergenic plants per day enable both the estimation of their fine-grained local distribution and the recognition of their current flowering stages at a given location. A citizen science project within the app, which is updated regularly with pollen-related information, has already proven highly effective in motivating people to provide observations and images of the full range of flowering stages of plants, such as hazel (Corylus avellana) and birch (Betula pendula). These images allow the development of a species-specific flowering stage classifier. Next, the observed flowering stages will be coupled with data from pollen traps and local weather forecast models to provide precise and fine-grained predictions of local pollen loads.
| Status Group | Postdoctoral Researcher |
|---|---|
| Poster Presentation Option | Yes, I’m willing to present as a poster. |
