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BMC Ecology|December 5, 2018
Combining high-throughput imaging flow cytometry and deep learning for efficient species and life-cycle stage identification of phytoplanktonSusanne Dunker, David Boho, Jana Wäldchen, et al.
BMC Bioinformatics|January 5, 2019
Image-based classification of plant genus and family for trained and untrained plant speciesMarco Seeland, Michael Rzanny, David Boho, et al.
Npj Biodiversity|September 6, 2024
Opportunistic plant observations reveal spatial and temporal gradients in phenologyMichael Rzanny, Patrick Mäder, Hans Christian Wittich, et al.
Frontiers in Plant Science|February 14, 2022
Image-Based Automated Recognition of 31 Poaceae Species: The Most Relevant PerspectivesMichael Rzanny, Hans Christian Wittich, Patrick Mäder, et al.
The New Phytologist|August 18, 2020
Pollen analysis using multispectral imaging flow cytometry and deep learningSusanne Dunker, Elena Motivans, Demetra Rakosy, et al.
Frontiers in Plant Science|October 20, 2023
Bridging the gap: how to adopt opportunistic plant observations for phenology monitoringNegin Katal, Michael Rzanny, Patrick Mäder, et al.
BMC Bioinformatics|December 15, 2020
Flora Capture: a citizen science application for collecting structured plant observationsDavid Boho, Michael Rzanny, Jana Wäldchen, et al.
International Journal of Biometeorology|July 4, 2025
Expanding phenological insights: automated phenostage annotation with community science plant imagesNegin Katal, Michael Rzanny, Patrick Mäder, et al.
The Plant Journal : for Cell and Molecular Biology|October 9, 2024
Deep learning to capture leaf shape in plant images: Validation by geometric morphometricsLadislav Hodač, Kevin Karbstein, Lara Kösters, et al.
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