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Katal, N.; Rzanny, M.; Mäder, P.; Wäldchen, J.: Deep learning in plant phenological research: A systematic literature review. Frontiers in Plant Science 13, 805738 (2022)
Schmid, B.; Schmitz, M.; Rzanny, M.; Scherer-Lorenzen, M.; Mwangi, P. N.; Weisser, W. W.; Hector, A.; Schmid, R.; Flynn, D. F. B.: Removing subordinate species in a biodiversity experiment to mimic observational field studies. Grassland Research 1 (1), pp. 53 - 62 (2022)
Mahecha, M. D.; Rzanny, M.; Kraemer, G.; Mäder, P.; Seeland, M.; Wäldchen, J.: Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography 44 (8), pp. 1131 - 1142 (2021)
Mäder, P.; Boho, D.; Rzanny, M.; Seeland, M.; Wittich, H. C.; Deggelmann, A.; Wäldchen, J.: The Flora Incognita app – interactive plant species identfication. Methods in Ecology and Evolution 12 (7), pp. 1335 - 1342 (2021)
Rzanny, M.; Mäder, P.; Deggelmann, A.; Chen, M.; Wäldchen, J.: Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant Methods 15, 77 (2019)
Hines, J.; Giling, D. P.; Rzanny, M.; Voigt, W.; Meyer, S. T.; Weisser, W. W.; Eisenhauer, N.; Ebeling, A.: A meta‐food web for invertebrate species collected in a European grassland. Ecology 100 (6), e02679 (2019)
Seeland, M.; Rzanny, M.; Boho, D.; Wäldchen, J.; Mäder, P.: Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 20, 4 (2019)
Wittich, H. C.; Seeland, M.; Wäldchen, J.; Rzanny, M.; Mäder, P.: Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics 19, 190 (2018)
Ebeling, A.; Rzanny, M.; Lange, M.; Eisenhauer, N.; Hertzog, L. R.; Meyer, S. T.; Weisser, W. W.: Plant diversity induces shifts in the functional structure and diversity across trophic levels. Oikos 127 (2), pp. 208 - 219 (2018)
Rzanny, M.; Seeland, M.; Wäldchen, J.; Mäder, P.: Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods 13, 97 (2017)
Seeland, M.; Rzanny, M.; Alaqraa, N.; Wäldchen, J.; Mäder, P.: Plant species classification using flower images—A comparative study of local feature representations. PLoS One 12 (2), e0170629 (2017)
Begehold, H.; Rzanny, M.; Winter, S.: Patch patterns of lowland beech forests in a gradient of management intensity. Forest Ecology and Management 360, pp. 69 - 79 (2016)
The new research project "PollenNet" aims to use artificial intelligence to accurately predict the spread of pollen. In order to improve allergy prevention, experts are bringing together the latest interdisciplinary findings from a wide range of fields.
If rivers overflow their banks, the consequences can be devastating. Using methods of explainable machine learning, researchers at the Helmholtz Centre for Environmental Research (UFZ) have shown that floods are more extreme when several factors are involved in their development.
Europe is the fastest warming continent in the world. According to the European Environment Agency’s assessment, many of these risks have already reached critical levels and could become catastrophic without urgent and decisive action.
Plant observations collected with plant identification apps such as Flora Incognita allow statements about the developmental stages of plants - both on a small scale and across Europe.
Global experts have unveiled the annual 10 New Insights in Climate Science report. The report equips policymakers with the latest and most pivotal climate science research from the previous 18 months, synthesised to help inform negotiations at COP28 and policy implementation through 2024 and beyond.
The plant identification app Flora Incognita receives this year's Sonja Bernadotte Award for its importance in nature education for all age groups and its high scientific standards and usefulness.
Vegetation can respond to drought through different mechanisms, including changes in the plants’ structure and physiology. By analyzing state-of-the-art satellite-derived datasets with explainable machine learning methods, an international team around Wantong Li and René Orth showed that the vegetation’s physiology in many ecosystems has deviated from its structure under drought on a global scale.
Carbon sinks on the land surface mitigate the greenhouse effect. An international team of scientists has now determined that the vast majority of Europe’s total above-ground carbon storage is provided by the forests of Eastern Europe. However, this carbon sink has declined, mainly due to changes in land use.
The world’s forests, grasslands, and other terrestrial ecosystems have played a substantial role in offsetting human carbon emissions—a capability that researchers say would be threatened by continued global change.
A new study shows that the efficiency of microbial carbon use is at least four times more influential than other biological factors or environmental conditions on the global storage and distribution of carbon in soil.
Germany's most popular plant identification app "Flora Incognita" has been further upgraded by a new artificial intelligence. This triples the number of plant species that can be identified up to 16,000. In addition, the app is now available in 20 different languages and also in offline mode.