  Monitoring the responses of plants to environmental changes is essential for
plant biodiversity research. This, however, is currently still being done
manually by botanists in the field. This work is very laborious, and the data
obtained is, though following a standardized method to estimate plant coverage,
usually subjective and has a coarse temporal resolution. To remedy these
caveats, we investigate approaches using convolutional neural networks (CNNs)
to automatically extract the relevant data from images, focusing on plant
community composition and species coverages of 9 herbaceous plant species. To
this end, we investigate several standard CNN architectures and different
pretraining methods. We find that we outperform our previous approach at higher
image resolutions using a custom CNN with a mean absolute error of 5.16%. In
addition to these investigations, we also conduct an error analysis based on
the temporal aspect of the plant cover images. This analysis gives insight into
where problems for automatic approaches lie, like occlusion and likely
misclassifications caused by temporal changes.
