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)
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)
Dunker, S.; Boho, D.; Wäldchen, J.; Mäder, P.: Combining high‑throughput imaging flow cytometry and deep learning for efficient species and life‑cycle stage identification of phytoplankton. BMC Ecology 18, 51 (2018)
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)
Wäldchen, J.; Mäder, P.: Plant species identification using computer vision: A systematic literature review. Archives of Computational Methods in Engineering 25 (2), pp. 507 - 543 (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)
Wäldchen, J.; Thuille, A.; Seeland, M.; Rzanny, M.; Schulze, E. D.; Boho, D.; Alaqraa, N.; Hofmann, M.; Mäder, P.: Flora Incognita – Halbautomatische Bestimmung der Pflanzenarten Thüringens mit dem Smartphone. Landschaftspflege und Naturschutz in Thüringen 53 (3), pp. 121 - 125 (2016)
Schulze, E. D.; Bouriaud, O.; Wäldchen, J.; Eisenhauer, N.; Walentowski, H.; Seele, C.; Heinze, E.; Pruschitzki, U.; Dănilă, G.; Marin, G.et al.; Hessenmöller, D.; Bouriaud, L.; Teodosiu, M.: Ungulate browsing causes species loss in deciduous forests independent of community dynamics and silvicultural management in Central and Southeastern Europe. Annals of Forest Research 57 (2), pp. 267 - 288 (2014)
Walentowski, H.; Schulze, E. D.; Teodosiu, M.; Bouriaud, O.; von Heßberg, A.; Bußler, H.; Baldauf, L.; Schulze, I.; Wäldchen, J.; Böcker, R.et al.; Herzog, S.; Schulze, W.: Sustainable forest management of Natura 2000 sites: a case study from a private forest in the Romanian Southern Carpathians. Annals of Forest Research 56 (1), pp. 217 - 245 (2013)
Wäldchen, J.; Schöning, I.; Mund, M.; Schrumpf, M.; Bock, S.; Herold, N.; Uwe Totsche, K.; Schulze, E. D.: Estimation of clay content from easily measurable water content of air-dried soil. Journal of Plant Nutrition and Soil Science 175 (3), pp. 367 - 376 (2012)
Wäldchen, J.; Schulze, E. D.; Mund, M.; Winkler, B.: Der Einfluss politischer, rechtlicher und wirtschaftlicher Rahmenbedingungen des 19. Jahrhunderts auf die Bewirtschaftung der Wälder im Hainich-Dün-Gebiet (Nordthüringen). Forstarchiv 82, pp. 35 - 47 (2011)
Wäldchen, J.; Pusch, J.; Luthardt, V.: Zur Diasporen-Keimfähigkeit von Segetalpflanzen: Untersuchungen in Nord-Thüringen. Beiträge für Forstwirtschaft und Landschaftsökologie 38 (2), pp. 145 - 156 (2005)
A study by Leipzig University, the German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig (iDiv) and the MPI for Biogeochemistry shows that gaps in the canopy of a mixed floodplain forest have a direct influence on the temperature and moisture in the forest soil, but only a minor effect on soil activity.
From the Greek philosopher Aristotle to Charles Darwin to the present day, scientists have dealt with this fundamental question of biology. Contrary to public perception, however, it is still largely unresolved. Scientists have now presented a new approach for the identification and delimitation of species using artificial intelligence (AI).
A research team led by the German Centre for Integrative Biodiversity Research (iDiv) and Leipzig University has developed an algorithm that analyses observational data from the Flora Incognita app. The novel can be used to derive ecological patterns that could provide valuable information about the effects of climate change on plants.
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.
We have gained a new external member: Prof. Dr. Christian Wirth has been appointed by the Senate of the Max Planck Society as External Scientific Member. As a former group leader and later fellow at the institute, Prof. Wirth initiated and supported the development of the TRY database, the world's largest collection on plant traits.
A new study shows a natural solution to mitigate the effects of climate change such as extreme weather events. Researchers found that a diverse plant community acts as a buffer against fluctuations in soil temperature. This buffer, in turn, can have a decisive influence on important ecosystem processes.
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.
The Deutsche Forschungsgemeinschaft (DFG) is to fund a Research Unit in the Jena Experiment for a further four years with around five million euros. The new focus is on the stabilising effect of biodiversity against extreme climate events such as heat, frost or heavy rainfall.
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.
With a kick-off event on January 12, 2023, Friedrich Schiller University Jena, the Max Planck Institute for Biogeochemistry and the German Aerospace Center jointly opened the ELLIS Unit Jena. Machine learning and artificial intelligence are being used to help address global environmental crises.
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.