2024

  • Rzanny, M., Bebber, A., Wittich, H. C., Fritz, A., Boho, D., Mäder, P., & Wäldchen, J. (2024). More than rapid identification—Free plant identification apps can also be highly accurate. People and Nature, 00, 1–4. https://doi.org/10.1002/pan3.10676
  • Hodač, L., Karbstein, K., Kösters, L., Rzanny, M., Wittich, H.C., Boho, D., Šubrt, D., Mäder, P. and Wäldchen, J. (2024), Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics. Plant J. https://doi.org/10.1111/tpj.17053
  • Mora, K., Rzanny, M., Wäldchen, J., Feilhauer, H., Kattenborn, T., Kraemer, G., Mäder, P., Svidzinska, D., Wolf, S., & Mahecha, M. D. (2024). Macrophenological dynamics from citizen science plant occurrence data. Methods in Ecology and Evolution, 00, 1–16. https://doi.org/10.1111/2041-210X.14365
  • Karbstein, K., Kösters, L., Hodač, L., Hofmann, M., Hörandl, E., Tomasello, S., Wagner, N., Emerson, B., Albach, D., Scheu, S., Bradler, S., de Vries, J., Irisarri, I., Li, H., Soltis, P., Mäder, P., Wäldchen, J. (2024) Species delimitation 4.0: integrative taxonomy meets artificial intelligence. Trends in Ecology & Evolution. https://doi.org/10.1016/j.tree.2023.11.002
  • Rzanny, M., Mäder, P., Wittich, H.C., Boho, D. & Wäldchen, J. (2024) Opportunistic plant observations reveal spatial and temporal gradients in phenology. npj biodivers 3, 5. https://doi.org/10.1038/s44185-024-00037-7
  • Bebber, A. & Wäldchen, J. (2024). Flora Incognita – mehr als Pflanzenbestimmung. Biologie in Unserer Zeit, 54(1), 29–31. https://doi.org/10.11576/biuz-7054

2023

  • Bebber, A., Wäldchen, J., Rzanny, M., Boho, D., Wittich, HC, Fritz, A. & Mäder, P. (2023). Flora Incognita – Automatisierte Pflanzenbestimmung ermöglicht bürgerwissenschaftliches Pflanzenmonitoring. – Landschaftspflege und Naturschutz in Thüringen 59 (4): 180-183
  • 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. Front. Plant Sci. 14:1150956  https://doi.org/10.3389/fpls.2023.1150956 
  • Hodač, L., Karbstein, K., Tomasello, S., Wäldchen, J., Bradican, J. P., & Hörandl, E. (2023). Geometric morphometric versus genomic patterns in a large polyploid plant species complex. Biology, 12(3), 418.
  • Milz, S., Wäldchen, J., Abouee, A. et al. The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem. Sci Data 10, 168 (2023). https://doi.org/10.1038/s41597-023-02010-8

2022

  • 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
  • van Klink, R., August, T., Bas, Y., Bodesheim, P., Bonn, A., Fossøy, F., … Wäldchen, J. & Bowler, D. E. (2022). Emerging technologies revolutionise insect ecology and monitoring. Trends in Ecology & Evolution.
  • 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
  • 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

2021

  • 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)
  • Mäder, P., Boho, D., Rzanny, M., Seeland, M., Wittich, H. C., Deggelmann, A., & Wäldchen, J. (2021). The flora incognita app–interactive plant species identification. Methods in Ecology and Evolution. 12: 1335– 1342. https://doi.org/10.1111/2041-210X.13611
  • Mahecha, M. D., Rzanny, M., Kraemer, G., Mäder, P., Seeland, M., & Wäldchen, J. (2021). Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography. https://doi.org/10.1111/ecog.05492 (Editors’ Choice)
  • Seeland, M. & Mäder, P. (2021). “Multi-view classification with convolutional neural networks.” Plos one 16.1: e0245230.

2020

  • Boho, D., Rzanny, M., Wäldchen, J., Nitsche, F., Deggelmann, A., Wittich, H. C., … & Mäder, P. (2020). Flora Capture: a citizen science application for collecting structured plant observations. BMC bioinformatics, 21(1), 1-11. https://doi.org/10.1186/s12859-020-03920-9

2019

  • Rzanny, M., Mäder, P., Deggelmann, A., Chen, M., & Wäldchen, J. (2019). Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant methods, 15(1), 77. https://doi.org/10.1186/s13007-019-0462-4
  • Wäldchen, J. and Mäder, P. (2019). Flora incognita–wie künstliche intelligenz die Pflanzenbestimmung revolutioniert: Biol. Unserer Zeit, 49: 99-101 https://doi.org/10.1002/biuz.201970211
  • Seeland, M., Rzanny, M., Boho, D., Wäldchen, J.  & Mäder, P. (2019). Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 20:4 https://doi.org/10.1186/s12859-018-2474-x
  • Wäldchen J & Mäder P. (2018) Machine learning for image based species identification. Methods in Ecology and Evolution 2018;00:1–10. https://doi.org/10.1111/2041-210X.13075

2018

  • Wittich, H. C., Seeland, M., Wäldchen, J., Rzanny, M. & Mäder, P. (2018). Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics. 19(190). https://doi.org/10.1186/s12859-018-2201-7  
  • Hofmann, M., Seeland, M., Mäder, P., (2018). Efficiently Annotating Object Images with Absolute Size Information Using Mobile Devices. International Journal of Computer Vision: 1-18. https://doi.org/10.1007/s11263-018-1093-3
  • Wäldchen, J., Rzanny, M., Seeland, M. & Mäder, P. (2018). Automated plant species identification – Trends and future directions. PLoS Computational Biology 14 (4). https://doi.org/10.1371/journal.pcbi.1005993

2017

  • Rzanny, M., Seeland, M., Wäldchen, J., & Mäder, P. (2017). Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain. Plant Methods, 13(1), 97. https://doi.org/10.1186/s13007-017-0245-8
  • Seeland, M., Boho, D., Rzanny, M., Hofmann, M., Alaqraa, N., Wäldchen, J., Mäder, P. (2017): Flora Incognita – Bilder für das Trainieren neuronaler Netzwerke gesucht. Landschaftspflege und Naturschutz in Thüringen 54 (2): 85-86
  • Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J. & Mäder, P. (2017): Plant species classification using flower images—A comparative study of local feature representations. PLoS ONE 12(2): e0170629. doi:10.1371/journal.pone.0170629
  • Wäldchen, J. & Mäder, P. (2017): Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review. Archives of Computational Methods in Engineering. doi:10.1007/s11831-016-9206-z 

2016

  • Wäldchen, J., Thuille, A., Seelnad, M., Rzanny, M., Schulze, E.-D., Boho, D., Alaqraa, N., Hofmann, M. & Mäder, P. (2016): Flora Incognita – Halbautomatische Bestimmung der Pflanzenarten Thüringens mit dem Smartphone. Landschaftspflege und Naturschutz in Thüringen 53 (3): 121–125
  • Seeland, M., Rzanny, M., Alaqraa, N., Thuille, A., Boho, D., Wäldchen, J. & Mäder, P. (2016): Description of Flower Colors for Image based Plant Species Classification. in: Proc. 22nd German Color Workshop (FWS), Ilmenau, Germany, pp. 145-1154, 2016, ISBN: 978-3-00-053918-3, Eds. K.-H. Franke, R. Nestler