Abstract Being able to identify plant species is an important factor for understanding biodiversity and its change due natural anthropogenic drivers. We discuss the freely available Flora Incognita app Android, iOS Harmony OS devices that allows users interactively capture their observations. Specifically developed deep learning algorithms, trained on extensive repository of observations, classify images with yet unprecedented accuracy. By using this technology in a context‐adaptive...

Summary Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary ecological questions (pollination, paleobotany), also for other fields research (e.g. allergology, honey analysis or forensics). Researchers exploring alternative methods to automate these but, several reasons, manual microscopy is still the gold standard. In this study, we present new method pollen using multispectral imaging flow cytometry combination with deep...

Phytoplankton species identification and counting is a crucial step of water quality assessment. Especially drinking reservoirs, bathing ballast need to be regularly monitored for harmful species. In times multiple environmental threats like eutrophication, climate warming introduction invasive more intensive monitoring would helpful develop adequate measures. However, traditional methods such as microscopic by experts or high throughput flow cytometry based on scattering fluorescence...

Opportunistic plant records provide a rapidly growing source of spatiotemporal observation data. Here, we used such data to explore the question whether they can be detect changes in species phenologies. Examining 19 herbaceous and one woody two consecutive years across Europe, observed significant shifts their flowering phenology, being more pronounced for spring-flowering (6-17 days) compared summer-flowering (1-6 days). Moreover, show that these are suitable model large-scale...

Modern plant taxonomy reflects phylogenetic relationships among taxa based on proposed morphological and genetic similarities. However, taxonomical relation is not necessarily reflected by close overall resemblance, but rather commonality of very specific characters or similarity the molecular level. It an open research question to which extent relations within higher taxonomic levels such as genera families are shared visual constituting species. As a consequence, it even more questionable...

Abstract Background Digital plant images are becoming increasingly important. First, given a large number of deep learning algorithms can be trained to automatically identify plants. Second, structured image-based observations provide information about morphological characteristics. Finally in the course digitalization, digital collections receive more and interest schools universities. Results We developed freely available mobile application called Flora Capture allowing users collect...

Many applications in chemistry, biology and medicine use microfluidic devices to separate, detect analyze samples on a miniaturized size-level. Fluid flows evolving channels of only several tens hundreds micrometers size are often 3D nature, affecting the tailored transport cells particles. To flow phenomena local distributions particles within those channels, astigmatic particle tracking velocimetry (APTV) has become valuable tool, condition that basic requirements like low optical...

Poaceae represent one of the largest plant families in world. Many species are great economic importance as food and forage plants while others important weeds agriculture. Although a large number studies currently address question how can be best recognized on images, there is lack evaluating specific approaches for uniform groups considered difficult to identify because they obvious visual characteristics. an example such group, especially when non-flowering. Here we present results from...

Plant phenology, the study of seasonal events in plants' life cycles such as budburst, flowering onset, leaf-out, fruit ripening, and senescence, is intrinsically linked to climatic conditions plays a crucial role ecosystem processes like carbon nutrient cycling. Due its ecological importance, many countries have established phenological monitoring networks based on systematic protocols. However, declining volunteer participation recent decades has raised concerns about continuity these...

SUMMARY Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent “black box” problem DL models. To evaluate effectiveness capturing shape, we used geometric morphometrics (GM), an emerging component eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus directly situ and after herbarization. From these corresponding images,...

Abstract Hart et al. (2023) conducted a study to evaluate the accuracy of five plant identification apps based on snapshot images as used in practice by field ecologists. Their results revealed varying accuracies per app, ranging from 86.9% 46.4%. We explore reasons why failed deliver expected result. re‐evaluated image dataset using another app (Flora Incognita) order understand discrepancies between ground truth and predictions. found that mismatches given returned labels can arise due...

Plant phenology plays a vital role in assessing climate change. To monitor this, individual plants are traditionally visited and observed by trained volunteers organized national or international networks - Germany, for example, the German Weather Service, DWD. However, their number of observers is continuously decreasing. In this study, we explore feasibility using opportunistically captured plant observations, collected via identification app Flora Incognita to determine onset flowering...

Plant phenology investigates the timing of critical events in a plant's life cycle, encompassing budburst, flowering, fruiting, and senescence, with their significance rooted responsiveness to environmental conditions. Despite growing interest phenology, challenges persist documenting these processes due extensive spatial temporal scales. While global phenological networks traditionally collect data at individual scale, concern is arising regarding declining number observers, prompting...

Accurate plant species identification is essential for many scenarios in botanical research and conservation of biodiversity. Since a main obstacle the large number possible candidate to consider, assistance through automatic techniques highly desirable. On one side, photos organs taken by users field can effectively be used machine learning-based image classification, predicting most likely matching taxa. At same time, metadata on user's spatio-temporal context usually goes unused despite...