  Accurately phenotyping plant wilting is important for understanding responses
to environmental stress. Analysis of the shape of plants can potentially be
used to accurately quantify the degree of wilting. Plant shape analysis can be
enhanced by locating the stem, which serves as a consistent reference point
during wilting. In this paper, we show that deep learning methods can
accurately segment tomato plant stems. We also propose a control-point-based
ground truth method that drastically reduces the resources needed to create a
training dataset for a deep learning approach. Experimental results show the
viability of both our proposed ground truth approach and deep learning based
stem segmentation.
