  This work applies an encoder-decoder-based machine learning network to detect
and track the motion and growth of the flowering stem apex of Arabidopsis
Thaliana. Based on the CenterTrack, a machine learning back-end network, we
trained a model based on ten time-lapsed labeled videos and tested against
three videos.
