**5. Conclusions**

This work presents a method for the detection of plant communities in grasslands based on CNNs and UAV data. For this, UAV imagery and botanical data were collected at regular intervals in a hay meadow during two growths. Four VUs, a *Alopecurus pratensis*community, a *Lolium perenne*-community, a *Bromus hordeaceus*-community, and *Rumex obtusifolius* plants were identified and classified with CNNs. It was investigated whether a multitemporal classification offers added value compared to a monotemporal classification. However, it was shown that not all models trained for this purpose achieved the same accuracy and the classification quality was influenced by phenology. For the preparation of phytosociological relevés, expert knowledge is essential. This complicates the generation of suitable training data for the presented models. Furthermore, only one study site with two different plant communities and two weed species was observed. To transfer the presented methodology to other grasslands to estimate the composition of the vegetation and thus the forage quality, a database of additional grassland plant communities in different variants at the same phenological phase would be necessary. The monotemporal model can give a good impression of the spatial distribution of the different plant communities from a single observation. It should further be investigated whether the accuracy of the multitemporal model can be improved with additional training data.

**Author Contributions:** M.P. conceptualized the study, captured and processed the botanical and UAV data, built and trained the CNN, and wrote the paper. K.K. added botanical and ecological aspects, reviewed the grouping of vegetation units, and guided the identification of plants and draft versions of the manuscript. T.J. and D.T. advised on remote sensing and agricultural issues, respectively, and commented on draft versions on the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** We acknowledge support by Deutsche Forschungsgemeinschaft (DFG) and Open Access Publishing Fund of Osnabrück University.

**Data Availability Statement:** The orthomosaics and botanical data generated and analyzed during the study are available from the corresponding author on reasonable request.

**Acknowledgments:** We would like to thank the Remote Sensing Group Osnabrück for providing a UAV and equipment for the field work. Special thanks goes to Nadine Molitor, who made her meadow available for the study and provided background information about its use.

**Conflicts of Interest:** The authors declare no competing interest.
