Let’s go! Collect Flora Incognita badges for a diverse range of plant observations!

Since 2018, nature lovers have been identifying and collecting wild flowering plants with our Flora Incognita app, and with the accompanied plant fact sheets, improved their species knowledge constantly. But, as you might know, Flora Incognita is more: Every confirmed observation (the green checkmark in the upper right corner after an identification) contributes to a vast, global collection of plant species observations with which our scientists research changes in biodiversity.

With our latest app release, we want to take this to a new level: We introduce the Flora Incognita badges! Each badge rewards the user for completing certain tasks: Collecting early-blooming flowers or grasses, the tree of the year, ferns or just using the app a couple of days in a row. You will find many challenges to solve – maybe you already have reached first goals?

You will find your collected badges in the profile section of the app. A tap on “Achievements” will lead you to an overview of all the tasks we have prepared for you. We hope this new feature will motivate you to look beyond the usual, take the extra path, examine plants even more closely. We’re looking forward to your feedback! A rating in the app store, a share on Twitter, Facebook or Instagram, or even a recommendation to a friend are ways to spread the news that we highly appreciate!

If you find anything peculiar or working unexpectedly, send us an e-mail. And now, check out if you’ve already earned some badges!

“The advancement of AI methods will create key momentum for environmental and biodiversity research”

Interview with Prof. Patrick Mäder about the interaction of computer science, biology and Big Data

As a young research group at TU Ilmenau, the Data-intensive Systems and Visualization Group (dAI.SY), with currently more than 20 scientists and many student collaborators led by Prof. Patrick Mäder, combines diverse expertise in computer science, engineering and related scientific fields. In addition to machine learning and reliable software, biodiversity informatics has emerged as a focus of the department in recent years. In this way, the research team aims to contribute to the preservation of biodiversity. UNIonline spoke with Prof. Mäder about research in this area.

With your research focus on biodiversity informatics, you are working at the interface of computer science, biology and big data, thus bringing together the topics of digitization and sustainability. What motivated you to conduct research in this area and specifically on the topic of biodiversity?

In 2006 and 2012, I participated in expeditions to Siberia lasting several weeks under the leadership of Prof. Christian Wirth, the founding director of the German Center for Integrative Biodiversity Research iDiv, and Prof. Ernst-Detlef Schulze, founding director of the Max Planck Institute for Biogeochemistry. Together we conducted ecological research under adventurous conditions that was very memorable and changed my awareness of biodiversity: Climate change is a major threat to humanity, and biodiversity loss goes hand in hand with it. We need to stop thinking in silos and bring together the expertise of different scientific fields to study these complex relationships, understand them, and develop foundations for solutions. Our Flora Incognita project is doing just that.

Until recently, biologists did not have access to very large amounts of data to analyze. However, this has changed in recent decades, allowing researchers like you to use these data to investigate ecological questions. What kind of data are you working with?

We work with very different types of data, primarily photos, but also location data, multispectral image data that capture more than the three color channels perceivable by humans and thus information not visible to the human eye, and point clouds , which arecollections of very many measurement points generatedby laser scanners. Probably best known are the observation data that we have been using for years for automatic plant identification in the Flora-Incognita app: Millions of users worldwide ensure every day that we can link image evidence of plants with their locations, enabling us to analyze and predict the distribution of species.

This information is supplemented by curated observations from selected experts via the Flora Capture app. But our research group is not limited to plants. We process microscopic image data for the detection of phytoplankton and insects. In addition, we use special multispectral image data for the automatic identification of pollen. And for evaluating forest stands, we use point clouds generated with LIDAR sensors, a method related to the Radar related method for optical distance and speed measurementWith such data, we can then, for example, make statements about the water quality or help make urban areas more bee-friendly.

As part of the interdisciplinary research group KI4Biodiv – Artificial Intelligence in Biodiversity Research, you would like to work with the Max Planck Institute for Biogeochemistry to further develop and improve these AI methods and technologies in order to monitor biodiversity in different habitats and landscapes efficiently, quickly and automatically. To what extent is biodiversity monitoring a particular challenge for you as a researcher?

To understand the challenges, we first need to look at what biodiversity monitoring means in the first place: it allows us to perceive and document changes in the spatiotemporal occurrence of species. This includes, of course, the distribution of species: Where is diversity declining, where is it increasing? But biodiversity monitoring also includes other things, such as phenology: when do plants bloom, when do they bear fruit, and when do autumn leaves turn colorful? Such monitoring data indicate changes in biodiversity, they are used to investigate the causes of these changes, and they indicate whether strategies and measures to protect biodiversity are working.

Of course, such monitoring also poses major challenges, especially in three areas: it is expensive, requires a lot of time, and requires excellent taxonomic knowledge. Thus, numerous methods and concepts are needed to conduct effective biodiversity monitoring and to overcome the above-mentioned challenges – which is why automated recording and evaluation methods are the focus of research.


Foto: TU Ilmenau/ari

Interview:  Technische Universität Ilmenau

Flora Incognita Update

Our new release brings you a modern and simplified user experience, and a whole bunch of new features to help you further enjoy your plant findings:

Identify Plants:

  • The new user interface is more modern and clear.
  • It’s now easier to capture and identify a plant in nature.
  • You can disable the autofocus with a new camera feature to make it much easier to take sharp photos of small objects.

Collect plant findings:

  • You can add pictures to an observation after the observation.
  • You can use your own keywords to sort and filter your plant findings more easily.
  • You can now view and filter your plant findings on a map.
  • You can more easily browse the general species list by using numerous filters.


  • Even without a personal profile, you can transfer your data to a new device.
  • You can save Flora Incognita pictures in your gallery.

Fact sheets:

  • We have added extensive information on invasive species in Central Europe.

Deep Learning in Plant Phenological Research: A Systematic Literature Review

Negin Katal conducted a systematic literature review to analyze all primary studies on deep learning approaches in plant phenology research. The paper presents major findings from selected 24 peer-reviewed studies published in the last five years (2016–2021). 

Research on plant phenology has become increasingly important because seasonal and interannual climatic variations strongly influence the timing of periodic events in plants. One of the most significant challenges is developing tools to analyze enormous amounts of data efficiently. Deep Neural Networks leverages image processing to understand patterns or periodic events in vegetation that support scientific research.


“[…]deep learning is primarily intended to simplify the very time-consuming and cost-intensive direct phenological measurements so far.”


Technological breakthroughs powered deep learning approaches for plant phenology within the past five years. Our recently published paper describes the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition. It also identifies and discusses research trends and highlights promising future directions. It is freely available here: https://www.frontiersin.org/articles/10.3389/fpls.2022.805738/full

To understand how scientific research on phenology is done, the different methods to retrieve phenological data need to be clear:

  • Individual based observations are for example human observations of plants, cameras installed below the canopy or even reviewing pressed, preserved plant specimens collected in herbaria over centuries and around the globe.
  • Near-surface measurements cover research plots on regional, continental, and global scales and are done for example via PhenoCams, near-surface digital cameras located at positions just above the canopy, or drones.
  • Satellite remote sensing is done via satelite indices, such as spectral vegetation indices (VIs), enhanced vegetation index (EVI), and more.

Overview of methods monitoring phenology.

Key findings:

The reviewed studies were conducted in eleven different countries (three in Europe; nine in North America, two in South America, six in Asian countries, and four in Australia and New Zealand) and across different vegetation types, i.e., grassland, forest, shrubland, and agricultural land. The vast majority of the primary studies examine phenological stages on single individuals. Ten studies explored phenology on a regional level. No single study operates on a global level. Therefore, deep learning is primarily intended to simplify the very time-consuming and cost-intensive direct phenological measurements so far.

In general, the main phenological stages are the breaking of leaf buds or initial growth, expansion of leaves or SOS, the appearance of flowers, appearance of fruits, senescence (coloring), leaf abscission, or EOS. More than half of the studies focused either on the expansion of leaves (SOS) or on the flowering time.

Across the primary studies, different methods were used to acquire training material for deep learning approaches. Twelve studies used images from digital repeat photography and analyzed those with deep learning methods. The publication shares in-depth information of the different types of digital photography suitable to provide those training data.

Furthermore, it categorizes, compares, and discusses deep learning methods applied in phenological monitoring. Classification and segmentation methods were found to be most frequently applied to all types of studies, and  proved to be very beneficial, mostly because they can eliminate (or support) tedious and error-prone manual tasks.

Future trends in phenology research with the use of deep learning

Machine learning methods need huge amounts of data to be trained. Therefore, increasing the absolute number of collected data is one of the key challenges – especially in regions or countries that lack traditional phenological observing networks so far. The paper describes methods and tools that will become important levers to support this kind of research, for example:

  • Installing cameras below the canopy that automatically take pictures and submit them over long periods of time are one way to tackle that.
  • PhenoCams prove to be a new and resourceful way to fuel further research: With indirect methods that track changes in images by deriving handcrafted features such as green or red chromatic coordinates from PhenoCam images and then applying algorithms to derive the timing of phenological events, such as SOS and EOS. We expect many more studies to appear in the future evaluating PhenoCam images beyond the vegetation color indices calculated so far.
  • Citizen Science data from plant identification apps such as Flora Incognita prove to be a long-term source of vegetational data. These images have a timestamp and location information and can thus provide important information about, e.g., flowering periods, similar to herbarium material.

We see that the study of phenology can easily and successfully exploit deep learning methods to speed up traditional gathering and evaluation of information. We, as a research team, are very proud to be a part of that and invite you to play a vital role – in using Flora Incognita to observe the diversity and change of biodiversity around you.

If you have any questions to us regarding our research, don’t hesitate to reach out! You can find Negin Katal on Researchgate and Twitter (@katalnegin), for example.



Katal, N., Rzanny, M., Mäder, P., & Wäldchen, J. (2022). Deep learning in plant phenological research: A systematic literature review. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.805738

Digital beekeeping to learn and play

With the “beeactive” app from the Würzburg Bee Research Association, children and young people can playfully learn more about bees and the biodiversity of various wild plants in their environment. The aim is to bring nature conservation and species protection into focus and to broaden their awareness of this. Flora Incognita provides the automatic identification of plant species as an important game content of “beeactive”.

The core principle of the app “beeactive” is the playful accompaniment of the virtual beekeeper Melli Fera and thus the establishment and care of digital bee colonies. Users can place individual beehives on an interactive map and search the real surroundings of the bee colony for flowering plants within a certain radius. The plants found are then automatically identified directly by the app. In this way, the virtual bee colony is supplied with pollen and nectar and the users can expand their knowledge of the species at the same time. The higher the number of plant species found, the better the digital bee colony develops and new hives may be added. In this way, users can also be motivated to sow their own bee-friendly flowering meadows. In addition, they support the creation of local and regional flowering maps with the numerous plant photos. The game is supplemented by a wide range of information on the ecology of honey bees and wild bees, as well as on the basics of ecological relationships. An integrated quiz allows direct application of the new knowledge and consolidates what has been learned.

For more information, please visit the following website: https://beeactive.app/.


The app “beeactive” was developed by Porf. Tautz and Florian Schimpf and financed with funds from the Bayerische Sparkassenstiftung. It is available free of charge in the Google Play Store and the Apple App Store.

Automated plant identification with the Flora Helvetica app

We are pleased to announce a successful collaboration with Haupt Verlag. Since version 2.3, the Flora Helvetica app includes a function for the automatic image-based identification of plants, which is provided as part of the Flora Incognita project. Thus, for the first time, it is possible with a single app to identify plants automatically as well as manually with the integrated identification key.

For more information about the Flora Helvetica app, please visit the following page:


How smartphones can help detect ecological change

Leipzig/Jena/Ilmenau. Mobile apps like Flora Incognita that allow automated identification of wild plants cannot only identify plant species, but also uncover large scale ecological patterns. These patterns are surprisingly similar to the ones derived from long-term inventory data of the German flora, even though they have been acquired over much shorter time periods and are influenced by user behaviour. This opens up new perspectives for rapid detection of biodiversity changes. These are the key results of a study led by a team of researchers from Central Germany, which has recently been published in Ecography.

With the help of Artificial Intelligence, plant species today can be classified with high accuracy. Smartphone applications leverage this technology to enable users to easily identify plant species in the field, giving laypersons access to biodiversity at their fingertips. Against the backdrop of climate change, habitat loss and land-use change, these applications may serve another use: by gathering information on the locations of identified plant species, valuable datasets are created, potentially providing researchers with information on changing environmental conditions.

But is this information reliable – as reliable as the information provided by data collected over long time periods? A team of researchers from the German Centre for Integrative Biodiversity Research (iDiv), the Remote Sensing Centre for Earth System Research (RSC4Earth) of Leipzig University (UL) and Helmholtz Centre for Environmental Research (UFZ), the Max Planck Institute for Biogeochemistry (MPI-BGC) and Technical University Ilmenau wanted to find an answer to this question. The researchers analysed data collected with the mobile app Flora Incognita between 2018 and 2019 in Germany and compared it to the FlorKart database of the German Federal Agency for Nature Conservation (BfN). This database contains long-term inventory data collected by over 5,000 floristic experts over a period of more than 70 years.

Mobile app uncovers macroecological patterns in Germany

The researchers report that the Flora Incognita data, collected over only two years, allowed them to uncover macroecological patterns in Germany similar to those derived from long-term inventory data of German flora. The data was therefore also a reflection of the effects of several environmental drivers on the distribution of different plant species.

However, directly comparing the two datasets revealed major differences between the Flora Incognita data and the long-term inventory data in regions with a low human population density. “Of course, how much data is collected in a region strongly depends on the number of smartphone users in that region,” said last author Dr. Jana Wäldchen from MPI-BGC, one of the developers of the mobile app. Deviations in the data were therefore more pronounces in rural areas, except for well-known tourist destinations such as the Zugspitze, Germany’s highest mountain, or Amrum, an island on the North Sea coast.

User behaviour also influences which plant species are recorded by the mobile app. “The plant observations carried out with the app reflect what users see and what they are interested in,” said Jana Wäldchen. Common and conspicuous species were recorded more often than rare and inconspicuous species. Nonetheless, the large quantity of plant observations still allows a reconstruction of familiar biogeographical patterns. For their study, the researchers had access to more than 900,000 data entries created within the first two years after the app had been launched.

Automated species recognition bears great potential

The study shows the potential of this kind of data collection for biodiversity and environmental research, which could soon be integrated in strategies for long-term inventories. “We are convinced that automated species recognition bears much greater potential than previously thought and that it can contribute to a rapid detection of biodiversity changes,” said first author Miguel Mahecha, professor at UL and iDiv Member. In the future, a growing number of users of apps like ‘Flora Incognita’ could help detect and analyse ecosystem changes worldwide in real time.

The Flora Incognita mobile app was developed jointly by the research groups of Dr. Jana Wäldchen at MPI-BGC and the group of Professor Patrick Mäder at TU Ilmenau. It is the first plant identification app in Germany using deep neural networks (deep learning) in this context. Fed by thousands of plant images, that have been identified by experts, it can already identify over 4,800 plant species.

“When we developed Flora Incognita, we realized there was a huge potential and growing interest in improved technologies for the detection of biodiversity data. As computer scientists we are happy to see how our technologies make an important contribution to biodiversity research,“ said co-author Patrick Mäder, professor at TU Ilmenau.

Original publication
Miguel D. Mahecha, Michael Rzanny, Guido Kraemer, Patrick Mäder, Marco Seeland, Jana Wäldchen (2021). Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography, DOI: 10.1111/ecog.0549

(Text: Kati Kietzmann, iDiv)

Efficient pollen identification – Interdisciplinary research team combines image-based particle analysis with artificial intelligence


From pollen forecasting, honey analysis and climate-related changes in plant-pollinator interactions, analysing pollen plays an important role in many areas of research. Microscopy is still the gold standard, but it is very time consuming and requires considerable expertise. In cooperation with Technische Universität (TU) Ilmenau, scientists from the Helmholtz Centre for Environmental Research (UFZ) and the German Centre for Integrative Biodiversity Research (iDiv) have now developed a method that allows them to efficiently automate the process of pollen analysis. Their study has been published in the specialist journal New Phytologist.

Pollen is produced in a flower’s stamens and consists of a multitude of minute pollen grains, which contain the plant’s male genetic material necessary for its reproduction. The pollen grains get caught in the tiny hairs of nectar-feeding insects as they brush past and are thus transported from flower to flower. Once there, in the ideal scenario, a pollen grain will cling to the sticky stigma of the same plant species, which may then result in fertilisation. “Although pollinating insects perform this pollen delivery service entirely incidentally, its value is immeasurably high, both ecologically and economically,” says Dr. Susanne Dunker, head of the working group on imaging flow cytometry at the Department for Physiological Diversity at UFZ and iDiv. “Against the background of climate change and the accelerating loss of species, it is particularly important for us to gain a better understanding of these interactions between plants and pollinators.” Pollen analysis is a critical tool in this regard. 

Each species of plant has pollen grains of a characteristic shape, surface structure and size. When it comes to identifying and counting pollen grains – measuring between 10 and 180 micrometres – in a sample, microscopy has long been considered the gold standard. However, working with a microscope requires a great deal of expertise and is very time-consuming. “Although various approaches have already been proposed for the automation of pollen analysis, these methods are either unable to differentiate between closely related species or do not deliver quantitative findings about the number of pollen grains contained in a sample,” continues UFZ biologist Dr. Dunker. Yet it is precisely this information that is critical to many research subjects, such as the interaction between plants and pollinators. 

In their latest study, Susanne Dunker and her team of researchers have developed a novel method for the automation of pollen analysis. To this end they combined the high throughput of imaging flow cytometry – a technique used for particle analysis – with a form of artificial intelligence (AI) known as deep learning to design a highly efficient analysis tool, which makes it possible to both accurately identify the species and quantify the pollen grains contained in a sample. Imaging flow cytometry is a process that is primarily used in the medical field to analyse blood cells but is now also being repurposed for pollen analysis. “A pollen sample for examination is first added to a carrier liquid, which then flows through a channel that becomes increasingly narrow,” says Susanne Dunker, explaining the procedure. “The narrowing of the channel causes the pollen grains to separate and line up as if they are on a string of pearls, so that each one passes through the built-in microscope element on its own and images of up to 2,000 individual pollen grains can be captured per second.” Two normal microscopic images are taken plus ten fluorescence microscopic images per grain of pollen. When excited with light radiated at certain wavelengths by a laser, the pollen grains themselves emit light. “The area of the colour spectrum in which the pollen fluoresces – and at which precise location – is sometimes very specific. This information provides us with additional traits that can help identify the individual plant species,” reports Susanne Dunker. In the deep learning process, an algorithm works in successive steps to abstract the original pixels of an image to a greater and greater degree in order to finally extract the species-specific characteristics. “Microscopic images, fluorescence characteristics and high throughput have never been used in combination for pollen analysis before – this really is an absolute first.” Where the analysis of a relatively straightforward sample takes, for example, four hours under the microscope, the new process takes just 20 minutes. UFZ has therefore applied for a patent for the novel high-throughput analysis method, with its inventor, Susanne Dunker, receiving the UFZ Technology Transfer Award in 2019.

The pollen samples examined in the study came from 35 species of meadow plants, including yarrow, sage, thyme and various species of clover such as white, mountain and red clover. In total, the researchers prepared around 430,000 images, which formed the basis for a data set. In cooperation with TU Ilmenau, this data set was then transferred using deep learning into a highly efficient tool for pollen identification. In subsequent analyses, the researchers tested the accuracy of their new method, comparing unknown pollen samples from the 35 plant species against the data set. “The result was more than satisfactory – the level of accuracy was 96 per cent,” says Susanne Dunker. Even species that are difficult to distinguish from one another, and indeed present experts with a challenge under the microscope, could be reliably identified. The new method is therefore not only extremely fast but also highly precise.

In the future, the new process for automated pollen analysis will play a key role in answering critical research questions about interactions between plants and pollinators. How important are certain pollinators like bees, flies and bumblebees for particular plant species? What would be the consequences of losing a species of pollinating insect or a plant? “We are now able to evaluate pollen samples on a large scale, both qualitatively and- at the same time – quantitatively. We are constantly expanding our pollen data set of insect-pollinated plants for that purpose,” comments Susanne Dunker. She aims to expand the data set to include at least those 500 plant species whose pollen is significant as a food source for honeybees.

Susanne Dunker, Elena Motivans, Demetra Rakosy, David Boho, Patrick Mäder, Thomas Hornick, Tiffany M. Knight: Pollen analysis using multispectral imaging flow cytometry and deep learning. New Phytologist https://doi.org/10.1111/nph.16882

Kosmos „Was blüht den da“ in our Flora Incognita App

„Was blüht denn da?“ –  always mobile!

All buyers of the current edition of the two books “Was blüht denn da? Das Original” and “Was blüht denn da? Der Fotoband” have the possibility to have the contents of their “Was blüht denn da?” with them on their smartphone.

After entering a code in our identification app “Flora Incognita”, the corresponding texts and images will be displayed for all plants included in “Was blüht denn da?”.

For further questions please contact directly KOSMOS.



„The only things you want to conserve are the things you know”

Jana Wäldchen and her team from the Max Planck Institute for Biogeochemistry have played a key role in developing the plant identification app, Flora Incognita. We discussed with her how being able to identify different plants contributes to species diversity, which plant species are particularly under threat and how non-native species are suppressing local plants.

What role do Citizen Science projects such as Flora Incognita play in protecting the variety of species?

Projects such as Flora Incognita that involve the general public play two important roles. On the one hand, they simplify the identification process. Anyone who is interested in plants can now easily, quickly and fairly precisely put a name to an unknown species. This means that more attention is paid to plant variety and that people become more aware of nature and the need to protect it.

Naturally, documenting the variety of plant species also makes an important contribution. As a result, scientists and nature conservation authorities also benefit from the app. Thanks to the identified species and their location, extremely valuable data records can be created that provide information that is of relevance for research into species protection and biodiversity. In the long term, the data from the Flora Incognita app will make it possible to find new answers to questions such as: When do certain species flower, and where? How widely to the properties of a single plant species vary? How are the composition and locations of the plants shifting in response to climate change and the type of land use?

The Flora Incognita app has been in use for two years. What has changed during that time?

The goal of the project is to make it easier for people to identify plants, and in this way, to increase their awareness of the wide variety of species around them. We have received many emails and comments from users that confirm that we’re taking the right approach towards achieving this goal. We’re not just getting feedback about how easy it is to identify plants. Many users also write that this easy identification has broadened their view of the variety of species.

Comments such as “At last, we’re not just ‘blindly’ walking through the forest!” or “This is a fun way of finding out more about the environment” show that the app is making an important contribution towards raising awareness of plant diversity. It is very satisfying to have achieved this goal. Plant identification plays an important part in species protection, since we only want to conserve the things that we know. 

How does the app make plant identification easier?

It’s a situation we’re all familiar with. While out walking, you spot a plant that you wouldlike to know more about. What is the plant called, is it poisonous, or might it be a protected species? For laypersons, the standard identification books are difficult to understand; identifying the plants takes a lot of time and is usually difficult to do because so many specialist terms are involved. This presents a major hurdle for people who are interested in identifying plants and who want to find out more about them.

Picture guides have made it easier to identify the most common species, but either we don’t always take them with us, or they don’t cover the entire range of plant varieties. Our Flora Incognita app allows users to identify plants quickly, easily and fairly precisely. Using the camera in your smartphone, you take a snapshot of the flower, and possibly also the leaves, and in just a few seconds, the suggested name of the plant is shown. Users are also given additional information such as its local protection status, important plant features, areas where it is found and information about similar species with which it is easily confused.

How often has the app been downloaded? How many plants have already been identified using the app?

The app has been downloaded over a million times. It has already been used to identify more than eight million plants. To date, Flora Incognita has identified around 4,600 different species.

Can the app be used to draw conclusions about plant variety in specific ecosystems?

The plant observations collected using the app are not systematic and only reflect what the users see in nature, and what attracts their interest. This means that we only get information about which species are growing in specific areas. This does not enable us to draw conclusions as to whether certain species are not present. Those species that are common and striking in appearance are identified more often than the rarer, less spectacular ones.

Even so, after just two vegetation periods, we can determine similar patterns of spread for certain species to those identified in floristic, systematic mapping for the whole of Germany. This illustrates the potential for the app to supplement floristic mapping in the long term. We are very confident that this autumn, we will have collected sufficient observations to allow us to make more detailed statements about plant variety in different regions.

However, we can now already say that most identifications are made in urban areas, in the places very close to where people live. The species that are photographed most often are nitrogen indicators such as yarrow (Achillea millefolium agg.), dandelions (Taraxacum), ground ivy (Glechoma hederacea agg.) or garlic mustard (Alliaria petiolata), which mainly grow in anthropogenic habitats. At the same time, we have also noticed that people often use the app when they are away on holiday. For example, in 2019, observation figures were higher in relation to the size of the population on the North and Baltic Sea coasts and in the Alpine region than in other areas.

Which plant species are particularly under threat?

In Germany, nearly a third of native wild species are endangered. This is the figure given in the endangered species list of ferns and flowering plants, mosses and algae, which was published by the German Federal Agency for Nature Conservation in December 2018. Due to intensive farming, many species have declined significantly, such as the flame adonis (Adonis flammea), which grows in central Europe on chalky, shallow arable land. The round-leafed modesty (Bupleurum rotundifolium), which has similar soil requirements, has disappeared in many places.

Another group of species that is particularly under threat are plants that grow in non-cultivated, in particular low-nutrient, poor soil.  They include species such as the small pasque flower (Pulsatilla pratensis) and catsfoot (Antennaria dioica). However, typical moorland plants such as types of sundew (Drosea spec.) or bogbean (Menyanthes trifoliata) are also classified as endangered species. The main cause of the decline in numbers is the increasing addition of nutrients to the soil. As well as those plants that are included on the list of endangered species, there are also others for which Germany is responsible internationally for protecting, either because they only grow in Germany, or because their populations here are large compared to other countries. They include species such as the sycamore (Acer pseudoplatanus) and copper beech (Fagus sylvatica).

Which ecosystems need particular protection?

A particularly large number of species groups that are under threat or in danger of becoming extinct grow on low-nutrient soil, such as heathland, grassland, and moorland.  However, hay meadows rich in plant species, orchards, floodplains, alpine meadows and coastal dunes also need special protection.

Which neophytes grow particularly well in Germany?

Neophytes are plants that began to grow after 1492 in areas from which they do not naturally originate. In Germany, more than 400 neophytes have become firmly established. Of these, more than 50 species have been classified by the German Federal Agency for Nature Conservation as being invasive or potentially invasive. Many of us are familiar with common plants such as the Canadian goldenrod (Solidago canadensis), hill mustard (Bunias orientalis), Japanese knotweed (Fallopia japonica), giant hogweed (Heracleum mantegazzianum) and Himalayan balsam (Impatiens glandulifera).

These “invasive” non-native species generate problems for nature conservation and lead to economic damage by reducing crop yields, causing increased use of pesticides in agriculture and forestry, or making it more expensive to maintain roads, waterways and railway tracks, for example. Giant hogweed and common ragweed (Ambrosia artemisiifolia) also contain substances that cause burns or allergies among humans.

After habitat destruction, invasive species are regarded as posing the second-greatest threat to biodiversity throughout the world. Above all, invasive species compete with native species for habitat and resources. In doing so, they can suppress individual native species or even entire natural communities. One clear example for the area near where we work in Jena is hill mustard. It has spread like wildfire and is now pushing out native and rare plant species from the species-rich meadow and semi-dry grassland biotopes. Early detection and rapid response are critical processes to prevent the spread and establishment of invasive species.

How will the app be developed in the future?

While in recent years, we have mainly worked on the app and automatic identification, in the follow-up project, Flora Incognita++, our main aim is to evaluate the observation data. Here, studies are needed for automatic quality control. We also want to be able to draw conclusions about flowering periods, dissemination, and coexistence.

We have also not yet met all our quality standards when it comes to automatic identification. In the follow-up project, we will improve the detection algorithms specifically for species groups that are critical to identification, such as sweet and sedge grasses.

We are also working on our third app (Flora Key), which contains an interactive identification key and which enables manual identification on the basis of morphological properties of the plant. This identification key will also be integrated into the Flora Incognita app.

The Flora Incognita project is jointly funded by the German Federal Ministry of Education and Research (BMBF), the German Federal Agency for Nature Conservation and the Thuringian Ministry for the Environment, Energy and Nature Conservation, and will continue to take an application-oriented approach. An important goal of our research group will continue to be to forge links between science, the general public and the government authorities.

Many thanks for this interview!

Interview: Barbara Abrell (Max Planck Society)