A light blue banner on which numerous circles are depicted in which various flowers can be seen. The three flowers that make up the home screen of the Flora Incognita app are placed in the centre of the banner.

Biod.AI.versity Observation & Integration (Bio.AI)

Dr. Jana Wäldchen

Our mission

The research group focuses on three main areas:

a) Automated and integrative species identification
b) Biodiversity monitoring and ecosystem functioning
c) Citizen Science


Automated species identification

One focus of the "Biod.AI.versity Observation & Integration" research group is the advancement of automated species identification (Wäldchen and Mäder, 2018).
This ongoing research endeavor has led to the development of pioneering methodologies that bridge the fields of computer science and botany. The research group has placed particular emphasis on systematically evaluating different image perspectives specific to plant species (Rzanny et al. 2022, 2019, 2017). This approach enables a more comprehensive understanding of plant morphology and characteristics, significantly enhancing the accuracy of automated species identification systems. A notable outcome of this research is the creation of the Flora Incognita App, designed for interactive automated identification of vascular plant species, encompassing over 16,000 species (Mäder et al. 2021).
Moreover, the group is actively working on methodologies to combine DNA and image data for
integrative species classification (Kösters et. al. in prep, Karbstein et al. 2023). In collaboration with iDiv and UFZ Leipzig, the group is also developing a monitoring approach for species-specific analysis of phytoplankton samples (Dunker et al. 2018).

Key Publications:


Biodiversity and ecosystem functioning research

Building on the extensive dataset from the Flora Incognita app generated by citizen scientists, the group's research has expanded, focusing on analyzing opportunistic plant occurrence data.  In a first analysis we were able to uncover macroecological patterns with Flora Incognita observation data from a single vegetation season (Mahecha et al., 2020).
Furthermore, we have investigated the extent to which opportunistic data can complement systematic phenological monitoring (Katal and Rzanny et al., 2023). Our Europe-wide study has revealed that opportunistic citizen science plant observations unveil spatial and temporal gradients in phenology (Rzanny et al., 2024). These investigations underscore the invaluable insights that opportunistic data can furnish, thereby enriching our comprehension of ecological and phenological patterns on a broader scale.
In the newly established PollenNet project, we will predict the pollen distribution of allergenic plants. These areas of study are crucial for advancing our knowledge of ecological dynamics, biodiversity conservation, and the broader impacts of plant species on ecosystems and human health.

Key publications


Citizen Science, Education and Science Communication

Successful scientific work goes beyond the scientific community and extends to knowledge and technology transfer to society. The Flora Incognita project, in particular, has garnered public attention and engagement.  Using the Flora Incognita App, we explore methods of effective knowledge transfer in various forms (badges, stories, and more) (Bebber und Wäldchen, 2024, Wäldchen et al., 2022). From the very beginning of the project, the dissemination of the results was carefully planned. As a result of these efforts, the work has gained widespread recognition and international visibility through television, press coverage, and active participation on social media platforms. This publicity has not only facilitated broad dissemination but has also generated significant interest in scientific endeavors. In the new project „Forest Doctor“, we will expand our communication strategies toward forest dieback and tree diseases and discuss the possibilities of a Citizen Science project for recording damage to woods.

Key publications

Key Public Events

Since the start of the project, we have been engaging in 1:1 dialogue with users and interested parties at public events, such as:

  • interactive exhibit on board of the museum vessel MS Science
  • urban botany excursion “More than Weeds” (Krautschau)
  • interactive exhibition at re:publica conference
  • workshops for teachers 
  • Long Nights of Science


Flora Incognita++  Citizens record plant diversity

Flora Incognita++  Citizens record plant diversity

In order to make change/loss of biodiversity visible, species knowledge is key - not only among experts, but for anyone. We developed the mobile app “Flora Incognita” that leverages modern computer vision techniques such as deep neural networks with a "connected data" approach, using site information (e.g. phenology, location, date and time) and plant morphological traits for semi-automatic species identification. The collected data are used to answer questions of plant species monitoring and phenology. 
The app was released in 2018 and has reached 7M downloads by 2023. It is freely available for iOS and in the Google Play Store. More information can be found on the Flora Incognita website.
NaturaIncognita-creating a workflow platform for AI-based species identification
To address the loss of biodiversity, data on the state and change of biodiversity is needed across many life forms. In this “AI lighthouse” project, the existing frameworks for automated species identification via the "Flora Incognita" project are to be expanded to realise automatic identification for other species groups.
The project's workflow platform will provide a species identification service based on the latest machine learning methods. Pilot projects already include phytoplankton, butterflies and funghi.  more
AI4Biodiv - Artificial intelligence in biodiversity research

AI4Biodiv - Artificial intelligence in biodiversity research

The project is about creating different benchmark data sets, further developing training algorithms for automatic species identification, interpreting the results and using contextual information in the recognition process. In the area of species and population monitoring, training algorithms and network architectures are applied and further developed specifically for remote sensing data. In modelling, AI-based models are developed that address both the spatial and temporal context at different scales.
The aim of the project is to develop a framework for sustainable weed management using autonomous weed detection, AI-based identification of weed species, and geo-referenced weed distribution maps taking site-specific characteristics of the individual field into account. Based on these maps, management strategies for weed control on the respective fields will be developed and tested under field conditions. more


Journal Article (24)

Journal Article
Tristan Nauber, Ladislav Hodač, Jana Wäldchen, and Patrick Mäder, "Parametrization of biological assumptions to simulate growth of tree branching architectures," Tree Physiology (2024).
Journal Article
Michael Rzanny, Patrick Mäder, Hans Christian Wittich, David Boho, and Jana Wäldchen, "Opportunistic plant observations reveal spatial and temporal gradients in phenology," npj Biodiversity 3, 5 (2024).
Journal Article
John Paul Bradican, Salvatore Tomasello, Francesco Boscutti, Kevin Karbstein, and Elvira Hörand, "Phylogenomics of southern european taxa in the Ranunculus auricomus species complex: The apple doesn’t fall far from the tree," Plants 12 (21), 3664 (2023).
Journal Article
Negin Katal, Michael Rzanny, Patrick Mäder, Christine Römermann, Hans Christian Wittich, David Boho, Talie Musavi, and Jana Wäldchen, "Bridging the gap: How to adopt opportunistic plant observations for phenology monitoring," Frontiers in Plant Science 14, 1150956 (2023).
Journal Article
Kevin Karbstein, Christine Römermann, Frank Hellwig, and Kathleen Prinz, "Population size affected by environmental variability impacts genetics, traits, and plant performance in Trifolium montanum L.," Ecology and Evolution 13 (8), e10376 (2023).
Journal Article
Jana Wäldchen, Hans Christian Wittich, Michael Rzanny, Alice Fritz, and Patrick Mäder, "Towards more effective identification keys: A study of people identifying plant species characters," People and Nature 4 (6), 1603-1615 (2022).
Journal Article
Roel van Klink, Tom August, Yves Bas, Paul Bodesheim, Aletta Bonn, Frode Fossøy, Toke T. Høye, Eelke Jongejans, Myles H.M. Menz, Andreia Miraldo, Tomas Roslin, Helen E. Roy, Ireneusz Ruczyński, Dmitry Schigel, Livia Schäffler, Julie K. Sheard, Cecilie Svenningsen, Georg F. Tschan, Jana Wäldchen, Vera M.A. Zizka, Jens Åström, and Diana E. Bowler, "Emerging technologies revolutionise insect ecology and monitoring," Trends in Ecology and Evolution 37 (10), 872-885 (2022).
Journal Article
Kevin Karbstein, Salvatore Tomasello, Ladislav Hodac, Natascha Wagner, Pia Marinček, Birthe Hilkka Barke, Claudia Paetzold, and Elvira Hörandl, "Untying Gordian knots: unraveling reticulate polyploid plant evolution by genomic data using the large Ranunculus auricomus species complex," New Phytologist 235 (5), 2081-2098 (2022).
Journal Article
Negin Katal, Michael Rzanny, Patrick Mäder, and Jana Wäldchen, "Deep learning in plant phenological research: A systematic literature review," Frontiers in Plant Science 13, 805738 (2022).
Journal Article
Bernhard Schmid, Martin Schmitz, Michael Rzanny, Michael Scherer-Lorenzen, Peter N. Mwangi, Wolfgang W. Weisser, Andrew Hector, Roland Schmid, and Dan F. B. Flynn, "Removing subordinate species in a biodiversity experiment to mimic observational field studies," Grassland Research 1 (1), 53-62 (2022).
Journal Article
Michael Rzanny, Hans Christian Wittich, Patrick Mäder, Alice Deggelmann, David Boho, and Jana Wäldchen, "Image-based automated recognition of 31 Poaceae species: The most relevant perspectives," Frontiers in Plant Science 12, 804140 (2022).
Journal Article
Jaak Pärtel, Meelis Pärtel, and Jana Wäldchen, "Plant image identification application demonstrates high accuracy in Northern Europe," AOB PLANTS 13 (4), plab050 (2021).
Journal Article
Patrick Mäder, David Boho, Michael Rzanny, Marco Seeland, Hans Christian Wittich, Alice Deggelmann, and Jana Wäldchen, "The Flora Incognita app – interactive plant species identfication," Methods in Ecology and Evolution 12 (7), 1335-1342 (2021).
Journal Article
David Boho, Michael Rzanny, Jana Wäldchen, Fabian Nitsche, Alice Deggelmann, Hans-Christian Wittich, Marco Seeland, and Patrick Mäder, "Flora Capture: a citizen science application for collecting structured plant observations," BMC Bioinformatics 21, 576 (2020).
Journal Article
Michael Rzanny, Patrick Mäder, Alice Deggelmann, Minqian Chen, and Jana Wäldchen, "Flowers, leaves or both? How to obtain suitable images for automated plant identification," Plant Methods 15, 77 (2019).
Journal Article
Marco Seeland, Michael Rzanny, David Boho, Jana Wäldchen, and Patrick Mäder, "Image-based classification of plant genus and family for trained and untrained plant species," BMC Bioinformatics 20, 4 (2019).
Journal Article
Susanne Dunker, David Boho, Jana Wäldchen, and Patrick Mäder, "Combining high‑throughput imaging flow cytometry and deep learning for efficient species and life‑cycle stage identification of phytoplankton," BMC Ecology 18, 51 (2018).
Journal Article
Jana Wäldchen and Patrick Mäder, "Machine learning for image based species identification," Methods in Ecology and Evolution 9 (11), 2216-2225 (2018).
Journal Article
Hans Christian Wittich, Marco Seeland, Jana Wäldchen, Michael Rzanny, and Patrick Mäder, "Recommending plant taxa for supporting on-site species identification," BMC Bioinformatics 19, 190 (2018).
Journal Article
Jana Wäldchen, Michael Rzanny, Marco Seeland, and Patrick Mäder, "Automated plant species identification—Trends and future directions," PLoS Computational Biology 14 (4), e1005993 (2018).
Journal Article
Jana Wäldchen and Patrick Mäder, "Plant species identification using computer vision: A systematic literature review," Archives of Computational Methods in Engineering 25 (2), 507-543 (2018).
Journal Article
Michael Rzanny, Marco Seeland, Jana Wäldchen, and Patrick Mäder, "Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain," Plant Methods 13, 97 (2017).
Journal Article
Marco Seeland, Michael Rzanny, Nedal Alaqraa, Jana Wäldchen, and Patrick Mäder, "Plant species classification using flower images—A comparative study of local feature representations," PLoS One 12 (2), e0170629 (2017).
Journal Article
Jana Wäldchen, Angelika Thuille, Marco Seeland, Michael Rzanny, Ernst Detlef Schulze, David Boho, Nedal Alaqraa, Martin Hofmann, and Patrick Mäder, "Flora Incognita – Halbautomatische Bestimmung der Pflanzenarten Thüringens mit dem Smartphone," Landschaftspflege und Naturschutz in Thüringen 53 (3), 121-125 (2016).

Conference Paper (1)

Conference Paper
Marco Seeland, Michael Rzanny, Nedal Alaqraa, Angelika Thuille, David Boho, Jana Wäldchen, and Patrick Mäder, "Description of flower colors for image based plant species classification", in 22nd German Color Workshop (FWS), Ilmenau, Germany, edited by K.-H. Franke (2016), pp. 145-154.

Thesis - Master (1)

Thesis - Master
Viktoriya Demchyk, Darstellung von Biodiversität in Social Media anhand des Praxisbeispiels der Pflanzenbestimmungsapp „Flora Incognita“, Master Thesis, Friedrich Schiller University Jena, 2023.
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