Publikationen von Jana Wäldchen
Alle Typen
Zeitschriftenartikel (32)
1.
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Parametrization of biological assumptions to simulate growth of tree branching architectures. Tree Physiology (angenommen)
2.
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3, 5 (2024)
Opportunistic plant observations reveal spatial and temporal gradients in phenology. npj Biodiversity 3.
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14, 1150956 (2023)
Bridging the gap: How to adopt opportunistic plant observations for phenology monitoring. Frontiers in Plant Science 4.
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98 (6+7), S. 304 - 311 (2023)
Künstliche Intelligenz im Naturschutz. Natur und Landschaft 5.
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10, 168 (2023)
The HAInich: A multidisciplinary vision data-set for a better understanding of the forest ecosystem. Scientific Data 6.
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12 (3), 418 (2023)
Geometric morphometric versus genomic patterns in a large polyploid plant species complex. Biology 7.
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4 (6), S. 1603 - 1615 (2022)
Towards more effective identification keys: A study of people identifying plant species characters. People and Nature 8.
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37 (10), S. 872 - 885 (2022)
Emerging technologies revolutionise insect ecology and monitoring. Trends in Ecology and Evolution 9.
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13, 805738 (2022)
Deep learning in plant phenological research: A systematic literature review. Frontiers in Plant Science 10.
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12, 804140 (2022)
Image-based automated recognition of 31 Poaceae species: The most relevant perspectives. Frontiers in Plant Science 11.
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44 (8), S. 1131 - 1142 (2021)
Crowd-sourced plant occurrence data provide a reliable description of macroecological gradients. Ecography 12.
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13 (4), plab050 (2021)
Plant image identification application demonstrates high accuracy in Northern Europe. AOB PLANTS 13.
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12 (7), S. 1335 - 1342 (2021)
The Flora Incognita app – interactive plant species identfication. Methods in Ecology and Evolution 14.
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21, 576 (2020)
Flora Capture: a citizen science application for collecting structured plant observations. BMC Bioinformatics 15.
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15, 77 (2019)
Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant Methods 16.
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20, 4 (2019)
Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 17.
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18, 51 (2018)
Combining high‑throughput imaging flow cytometry and deep learning for efficient species and life‑cycle stage identification of phytoplankton. BMC Ecology 18.
Zeitschriftenartikel
9 (11), S. 2216 - 2225 (2018)
Machine learning for image based species identification. Methods in Ecology and Evolution 19.
Zeitschriftenartikel
19, 190 (2018)
Recommending plant taxa for supporting on-site species identification. BMC Bioinformatics 20.
Zeitschriftenartikel
14 (4), e1005993 (2018)
Automated plant species identification—Trends and future directions. PLoS Computational Biology