Flora-Incognita observations enable phenological monitoring throughout Europe

A new study by our research group shows that plant observations collected with identification apps can provide information about the developmental stages of plants – both on a small scale and across Europe. [Read the paper]

Why is it important to document phenology?

Many plants in temperate climates go through a cycle of flowering, leaf emergence, fruiting, leaf colouration and leaf fall every year. This process is called phenology and is strongly influenced by local climatic conditions (for example, the number of days per year on which a certain minimum temperature required for growth is reached; see “Growing Degree Day” or GDD). It is, therefore, not surprising that climate change has a substantial impact on phenology. For example, spring begins earlier than it did in the 1950s, meaning the growing season starts much earlier than it did back then. Such changes impact agricultural processes and can also lead to ecological imbalances. For example, plants begin to flower even before their pollinators are active. However, not all plants react equally to climatic changes. Species with a broader tolerance range for warm days, or where other factors determine phenology, are virtually unaffected by shifts. To gain a truly accurate understanding of the influence of climate on plant phenology, it is essential to document the phenology of as many different species as possible, in different countries and geographical regions.

How does phenological monitoring work?

Phenology is already being documented using various methods. Satellite images recognize the greening of entire areas, and cameras in treetops produce automated image series on the condition of the vegetation layer below. Such data sets allow statements to be made about large scales, but hardly allow any conclusions to be drawn about the phenology of individual species or even individuals. For this purpose, there are initiatives that are carried out with the help of trained volunteers. However, the number of these citizen scientists is constantly decreasing, and this type of data collection is usually limited to certain plant species (often trees), countries, or even smaller regions.

Is it possible to document phenology with Flora Incognita?

Data collected via plant identification apps such as Flora Incognita can be a solution. The scientists in our project already proved this in 2023: Plants are primarily noticed and photographed when they are conspicuous and also flower, bear colorful fruit, or autumn leaves. This results in observation patterns that indicate phenological events. These patterns often coincide with those published by the German Weather Service (DWD) concerning the onset of flowering species in Germany. Assuming that the DWD registers an earlier start of flowering of the elderberry in one year than in the previous year, this shift is also reflected in the identification requests from Flora Incognita.
Details of this study can be found in this article: Phenology monitoring with Flora Incognita plant observations.

A new study shows phenologies and bioclimatic correlations across Europe

Our new publication now shows that smartphone observations even reflect known supra-regional phenological patterns, such as

  • the later flowering of many species in Northern and Eastern Europe or
  • the later flowering of many species at higher altitudes, but also
  • a Europe-wide shift in the start of flowering between years, as has already been proven for Germany.

This proves that the data generated by plant identification apps is a reliable source for the occurrence of plants at a specific time and place and is well suited for answering further research questions – even on a larger scale.

The results of the study at a glance

Plants are more likely to flower when there are more warm days
We compared the Europe-wide observation data (sources: Flora Incognita and reporting platforms such as iNaturalist) from 2020 and 2021 for 20 different plant species. We found that spring-flowering plants in particular, such as the gamander speedwell Veronica chamaedrys, flowered earlier in 2020 – up to two weeks earlier than in 2021.

An analysis of the temperature at each location showed that there were significantly more days in spring 2020 on which an average of 5°C or more was reached, meaning that the plants could absorb more heat in a shorter period of time. The effect was less pronounced for species that flower later in the year, such as the tansy Tanacetum vulgare or the common viper’s bugloss Echium vulgare.

Plants flower later if they grow at higher altitudes, further east or north.
However, patterns could be recognized not only between the years but also between different regions. It is known that the same species flowers at different times depending on the location. (-> Hopkins’ bioclimatic law) For example, if the same plant species occurs in Sweden and Spain, the Spanish plant will flower a few days or even weeks earlier than the one in the north. The exact days until flowering naturally vary depending on the plant species. This regularity can also be illustrated with Flora Incognita data:

This figure shows the median of the observation data for the three plant species already presented. Pink and orange indicate that the species flowered early in the year at the respective location, while the same species flowered later at another location (coded dark green and blue). The longitudes and latitudes are clearly differentiated, as well as the low and high mountain ranges. Details of the data and methods used can be found in the publication linked at the end of the article.

All 20 plant species analyzed could be classified into one of three main patterns, illustrated here as examples. Veronica chamaedrys shows more reddish colours in 2020 than in 2021; as already mentioned, this is due to the warmer temperatures in spring 2020. Echium vulgare shows only minor responses to different climatic conditions over the years. For Tanacetum vulgare, we found that phenology shows an inverse pattern compared to the other species: Tansy is more likely to flower in eastern, northern, and high altitudes than its siblings in western, southern, and lower-lying parts of Europe. This phenomenon has also been described in the scientific literature. Species that need many warm days to flower have adapted to cold locations by shortening their vegetation period and flowering earlier.

For the first time, the new publication shows temporal and spatial shifts in plant phenology on a Europe-wide scale using data not collected explicitly for this purpose. For the users of Flora Incognita, this means that each individual plant identification satisfies more than just their curiosity. By documenting plant occurrences at a specific time in a particular place, they create a growing and robust data source on phenology that knows no national borders, includes new species, and can answer numerous further research questions.

Thank you for your curiosity.

The new publication is now freely available:
Rzanny, M., Mäder, P., Wittich, H.C. et al. Opportunistic plant observations reveal spatial and temporal gradients in phenology. npj biodivers 3, 5 (2024). https://doi.org/10.1038/s44185-024-00037-7

Veronica chamaedrys in the title image was captured by Ilse Schönfelder.

Adopting Flora Incognita Plant Observations for Phenology Monitoring

Importance of Phenology
A new paper, published by the Flora Incognita Research Group, shows that the plant observations collected via Flora Incognita can support phenological monitoring initiatives. Why is that important?
Observing plant phenology helps scientists to understand how climate change affects plants, for example. If flowering periods shift due to changing climatic conditions, this can have significant consequences for ecological relationships or the spread of species. Traditionally, the phases of phenology (e.g. bud break, leaf-out, onset of flowering, and leaf senescence) are recorded manually by trained volunteers – but their numbers are steadily declining.

Traditional Phenology Monitoring
In Germany, mainly the German Weather Service conducts the “official” plant phenology monitoring (Deutscher Wetterdienst, DWD). Here, every observer is given a specific “station,” corresponding to a dedicated spot for observing one tree, shrub or herbaceous plant. The number of these stations varies depending on the observed species. This means that some species have more stations than others. During the growing season, observers need to check on the plants they are studying at least two times each week and record the day when specific pheno phases begin.

Process diagram, showing how plant observation data are converted into onset of flowering dates and related to DWD (German meteorological service) observation stations for one examplary species. (from: Katal & Rzanny et al. 2023)

Process diagram, showing how plant observation data are converted into onset of flowering dates and related to DWD (German meteorological service) observation stations for one examplary species. (from: Katal & Rzanny et al. 2023)

Processing Flora Incognita data
The new paper’s lead authors Negin Katal and Michael Rzanny found a way to process Flora Incognita data in a way that they resemble DWD stations: They identified the locations of the DWD stations, and for each species, created a 5 km circle around them. Within this circle, they collected all the Flora Incognita observations for that species within a certain altitude range. At least 35 of those opportunistic records were needed to create a “Flora Incognita station”. If those could not be reached, even within an additional buffer (1 km at a time, up to 55 km), no Flora Incognita station was established at that location.

Interpolation of data
In the next step, the onset of flowering in 2020 and 2021 was calculated for each of these Flora Incognita stations, and the station data was interpolated for the whole of Germany. The resulting interpolation maps show similar results for most species as the manual documentation by the DWD.

Spatially interpolated maps based on the DWD and Flora Incognita stations for the onset of flowering of Sambucus nigra and Taraxacum officinale in 2020 and 2021. The color scale indicates the day of the year of the onset of flowering in each grid cell (from: Katal & Rzanny et al. 2023).

Spatially interpolated maps based on the DWD and Flora Incognita stations for the onset of flowering of Sambucus nigra and Taraxacum officinale in 2020 and 2021. The color scale indicates the day of the year of the onset of flowering in each grid cell (from: Katal & Rzanny et al. 2023).

Take-away message
The main finding of the publication is that the onset of flowering can be inferred from our plant observations – at least for annual herbaceous species or shrubs with a conspicuous flowering phase. This means that Flora Incognita data can supplement the traditionally collected phenological surveys on a broad scale, but also for many new species. This allows us to make a valuable contribution to the documentation of phenological shifts, which is of vital importance for the documentation and understanding of climate change, among other things.

We want to thank the many users of Flora Incognita who contribute to this new source of data. It is your curiosity that enables research like this.

The publication is now freely available:
Katal, N., Rzanny, M., Mäder, P., Römermann, C., Wittich, H. C., Boho, D., Musavi, T. & Wäldchen, J. (2023). Bridging the gap: How to adopt opportunistic plant observations for phenology monitoring

Frontiers in Plant Science14. doi: 10.3389/fpls.2023.1150956

The future of key-based plant identification – a user study

Our publication “Towards more effective identification keys – a study of people identifying plant species characters” shares insights into this by systematically examining how well people with different backgrounds in botany perceive, understand and relate morphological plant characteristics. In the end, our assessment suggests a set of design principles for intuitive and user-friendly identification keys.

Read it here: Towards more effective identification keys – a study of people identifying plant species characters


The correct and quick identification of plant species plays an important role in the protection of biodiversity, because people can only protect what they know. Automated species identification via apps like Flora Incognita can help closing the knowledge gap, but still the most common way of identifying plant species is working with character keys in printed books. For laypersons, these keys are often difficult to understand because of the widespread use of technical terms and the lack of easily understandable illustrations.

But biodiversity monitoring often involves or even relies on citizen scientists. In many cases, detailed information on species, phenological characteristics or other markers are required to fulfill the given tasks. To be able to keep and grow the number of citizen scientists contributing to monitoring projects, new approaches to support beginners would be helpful – especially in building species identification skills based on plant characteristics quickly and reliably.

Why is the correct perception of plant characters important?

In this example, you can see pictures of the European buckthorn (Rhamnus cathartica) and the common dogwood (Cornus sanguinea). On first sight, both species look very similar, so the exact perception and description of the leaves’ edges is a determining factor of species identification:

A leaf of Cornus sanguinea, the common dogwood

Cornus sanguinea, the common dogwood

A leaf of Rhamnus cathartica, the European buckthorn

Rhamnus cathartica, the European buckthorn

With its grey-brown bark and often thorny branches, Rhamnus cathartica has elliptic to oval leaves with serrated margins.Cornus sanguinea on the other hand has dark greenish-brown branches with elliptic to oval leaves that have an entire margin.
The determining factor in this case is one adjective that needs to be known, understood and perceived to identify the plant in question.

The study

We conducted an online survey with 484 participants. After assessing the participant’s prior plant and species knowledge, they were given the task to identify morphological plant characters from a number of plant images, supported by pictograms, as shown below.  In total, 25 different plant characters were shown on 6 images from different perspectives.


On average, the participants identified 79% of the characters correctly, even those without extensive species knowledge. Those who declared themselves as intermediate or expert in terms of plant expertise needed less images of the respective plant to give a clear answer, and felt overall more sure about their answer. On average, flower-related characters were identified significantly more often and faster than leaf-related characters. Also, flower-related characters needed less images to find a definite answer.


It seems that with carefully derived plant characters, illustrated by a combination of icons and explanatory text, even complex structures can be understood and correctly addressed even from laypersons. With this, we show that future design of traditional identification keys should not only focus on botanical terms, but also on the users and their current skill set. Improving the species knowledge and learning plant identification methods via apps or printed guidelines can be supported by intuitive icons, descriptions and questions that guide the user through the identification process.


Wäldchen, J., Wittich, H. C., Rzanny, M., Fritz, A., & Mäder, P. (2022). Towards more effective identification keys: A study of people identifying plant species characters. People and Nature. https://doi.org/10.1002/pan3.10405

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

Can grasses be identified automatically via smartphone images?

Couch grass or ryegrass? Grasses are considered species, that are difficult to identify. The couch grass (Agropyron repens) is feared and often controlled and in gardens. Ryegrass (Lolium perenne), on the other hand, is the basis of many lawn seed mixtures and is a valuable forage grass. The question is: Can you even differentiate them? In our recently published paper, we investigated whether grasses can be automatically recognized and distinguished despite their great similarity. We wanted to know which perspectives are suitable and whether this would even possible without flowers present.

We studied 31 species using images of the inflorescence, the leaves and the ligule. We found that a combination of different perspectives improves the results. The inflorescence provided the most information. If no flowers are present, pictures of the ligule from the direction of the leaf are best suited to distinguish the grass species. All images were taken with different smartphones.

What do we learn from this experiment for our plant identification app Flora Incognita? Even more difficult groups can be well identified automatically, provided suitable images of the correct plant parts are provieded. These newly gained insights will be incorporated into the further development of our app.

In our experiment, we achieved > 96% accuracy for the 31 species when combining all perspectives. We also gained many, many training images that will significantly improve the reliability of the Flora Incognita app for grasses in the future.



Rzanny M, Wittich HC, Mäder P, Deggelmann A, Boho D & Wäldchen J (2022) Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant Perspectives. Front. Plant Sci. 12:804140. https://doi.org/10.3389/fpls.2021.804140


Flora Incognita demonstrates high accuracy in Northern Europe

The Flora Incognita project aims to inspire people – to get to know their (botanical) surroundings better, but also to think outside the box and reflect on the possibilities of artificial intelligence, deep learning or biodiversity. A great example of this is what Jaak Pärtel did: As a student project, he investigated the accuracy of the Flora Incognita application in Estonia, Northern Europe and even published a paper about it! With this exceptional project, Jaak won the Estonian National Youth Science Competition! Congratulations, Jaak! Here is a short interview, to share more details.


Hello, Jaak, congratulations on your first place in the Estonian National Youth Science Competition. Would you have imagined that happening when you started the project?

I honestly had no idea about what my project would become in a year. However, I was certain from the beginning that I want to do something that would not be “just another student project”. I got very positive reviews for the project in school, so I thought I would give it a try in the national competition. I was really surprised when I heard the results for the competition. Even that was not all, as I have published a scientific article (co-authors Jana Wäldchen and Meelis Pärtel) based on the project’s dataset and will represent my country in the European Union Contest for Young Scientists 2020/2021 this September.

How did you get the idea to do this project?
My two interests were life sciences and technology, so I found a suitable combination of the two. I had heard of plant identification apps but was not sure how  I wanted to have a field works experience and collect an extensive dataset to analyse it statistically.

Can you explain briefly what you investigated and how you went about it?
I investigated the accuracy of the plant identification applications Flora Incognita. I conducted the study in two parts: one with 1500 plant images from a database and second with 1000 observations in Estonian wilderness. I also investigated whether plants with flowers were identified more accurately and how much time automated identification took compared to traditional methods.

What are your main results in the project?
The main result of my project was that both applications reached close to 80% in accuracy in Estonian field conditions, with the correct species among the top five suggestions in circa 90% of the observations. In field conditions, plants with flowers were identified considerably more accurately than ones without them. Automatic identification took one minute compared to over four minutes for manual identification. During my project, I also translated Flora Incognita into my national language – Estonian.

What were you telling bypassers when they saw you documenting flowers?  :-)
I had no such situations, as most of my field works took place in locations with little populace. However, I would have said that I am a researcher collecting a dataset about plant apps. A surprising number of people have at least heard about the apps and would probably understand my mission in the field.

What are your next plans?
As of now I am serving my country in mandatory conscription service but after that I will start my Bachelor’s studies of Biology and nature conservation in University of Tartu. I would like to pursue science as a carreer and use innovative and computational methods in biology.


Pärtel, J., Pärtel, M., & Wäldchen, J. (2021). Plant image identification application demonstrates high accuracy in Northern Europe. AoB PLANTS. Volume 13, Issue 4, https://doi.org/10.1093/aobpla/plab050 (Editors’ Choice)
Images: Jaak Pärtel

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