Improving Data Efficiency for Plant Cover Prediction with Label
  Interpolation and Monte-Carlo Cropping
Matthias Körschens Solveig Franziska Bucher Christine Römermann Joachim Denzler
The plant community composition is an essential indicator of environmental
changes and is, for this reason, usually analyzed in ecological field studies
in terms of the so-called plant cover. The manual acquisition of this kind of
data is time-consuming, laborious, and prone to human error. Automated camera
systems can collect high-resolution images of the surveyed vegetation plots at
a high frequency. In combination with subsequent algorithmic analysis, it is
possible to objectively extract information on plant community composition
quickly and with little human effort. An automated camera system can easily
collect the large amounts of image data necessary to train a Deep Learning
system for automatic analysis. However, due to the amount of work required to
annotate vegetation images with plant cover data, only few labeled samples are
available. As automated camera systems can collect many pictures without
labels, we introduce an approach to interpolate the sparse labels in the
collected vegetation plot time series down to the intermediate dense and
unlabeled images to artificially increase our training dataset to seven times
its original size. Moreover, we introduce a new method we call Monte-Carlo
Cropping. This approach trains on a collection of cropped parts of the training
images to deal with high-resolution images efficiently, implicitly augment the
training images, and speed up training. We evaluate both approaches on a plant
cover dataset containing images of herbaceous plant communities and find that
our methods lead to improvements in the species, community, and segmentation
metrics investigated.