Biod.AI.versity Observation & Integration (Bio.AI)
Dr. Jana Waeldchen
Our goal is to simplify, accelerate and increasingly automate global biodiversity monitoring through automated species identification in citizen science projects and remote sensing. Our specifically developed AI technology for species identification provides large amounts of data for this, and targeted science communication additionally contributes to raising public awareness for the protection of biodiversity.
Focus areas of the Bio.AI research group:
The Bio.AI research group combines three focus areas that each complement each other:
- Automated species identification
- Biodiversity monitoring
- Citizen Science/Science communication
Automated species identification
Within this research focus, we develop and use innovative methods of artificial intelligence to automate species identification. As part of our research activities, the Flora Incognita app was developed. To date, the app can identify more than 16.000 wild plants of Central Europe simply by taking photos in the field, resulting in many millions of plant observations including geolocation. By 2022, the app had been installed more than 5 million times.
Building on this successful framework, we are working on the automatic identification of other life forms such as phytoplankton, fungi and butterflies.
Michael Rzanny et al. (2022)
Image-based automated recognition of 31 Poaceae species: The most relevant perspectives
Frontiers in Plant Science,12: 804140. doi: 10.3389/fpls.2021.804140
Roelvan Klink et al. (2022)
Emerging technologies revolutionise insect ecology and monitoring
Trends in Ecology & Evolution. doi: 10.1016/j.tree.2022.06.001
Michael Rzanny et al. (2019)
Flowers, leaves or both? How to obtain suitable images for automated plant identification
Plant Methods 15, 77. doi: 10.1186/s13007-019-0462-4
Susanne Dunker et al. (2018)
Combining high‑throughput imaging flow cytometry and deep learning for efficient species and life‑cycle stage identification of phytoplankton
BMC Ecology 18, 51. doi: 10.1186/s12898-018-0209-5
The Bio.AI research group aims to simplify, accelerate and increasingly automate global biodiversity monitoring. Therefore, in this research area, we are currently focusing on the question of the extent to which automated species identification in combination with Citizen Science and remote sensing can contribute to biodiversity monitoring. The steadily growing number of plant occurrence data via the Flora Incognita app already provides us with important information:
- When and where do which species flower?
- How much do the characteristics of a plant species vary?
- How do the composition of plant communities and the locations of plants change in connection with climate change and the type of underlying land use?
Negin Katal & Michael Rzanny et al. (2023)
Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring.
Bridging the gap: how to adopt opportunistic plant observations for phenology monitoring. Front. Plant Sci. 14:1150956. https://doi.org/10.3389/fpls.2023.1150956
Miguel D. Mahecha et al. (2021)
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients
Ecography (Editors‘ Choice). doi: 10.1111/ecog.05492
Citizen Science & Science communication
Biodiversity monitoring data are often collected by trained persons, and usually only reach a small temporal and spatial coverage. Another limiting factor is the growing public loss in species knowledge. With the developement of automated species idenfication tools it is now possible that people without any species knowledge can collect biodiversity data. Those rarely follow a strict protocol regarding where, when, and how to record a species - resulting in a set of unstructured data.
Within the Bio.AI research group, we run two Citizen Science projects to collect plant species occurences: The Flora Incognita project, which collects opportunistic plant observations, and the Flora Capture project, where Citizen Scientists record plant findings in a set of predefined perspectives, resulting in a set of structured images.
In order to reach as many people as possible to use our apps and collect plant occurrence data, we invest significant resources into science communication:
- Social Media channels (Twitter, Instagram, Facebook, Youtube)
- Flora Incognita website and blog
- Exhibitions and events (MS Wissenschaft, Long Night of Science)
- Traditional PR like interviews, podcasts, articles
Patrick Mäder et al. (2021)
The Flora Incognita app – Interactive plant species identification
Methods in Ecology and Evolution. 12: 1335– 1342; doi: 10.1111/2041-210X.13611
David Boho et al. (2020)
Flora Capture: a citizen science application for collecting structured plant observations,
BMC Bioinformatics 21, 576; doi:10.1186/s12859-020-03920-9