  Plants are dynamic organisms and understanding temporal variations in
vegetation is an essential problem for robots in the wild. However, associating
repeated 3D scans of plants across time is challenging. A key step in this
process is re-identifying and tracking the same individual plant components
over time. Previously, this has been achieved by comparing their global spatial
or topological location. In this work, we demonstrate how using shape features
improves temporal organ matching. We present a landmark-free shape compression
algorithm, which allows for the extraction of 3D shape features of leaves,
characterises leaf shape and curvature efficiently in few parameters, and makes
the association of individual leaves in feature space possible. The approach
combines 3D contour extraction and further compression using Principal
Component Analysis (PCA) to produce a shape space encoding, which is entirely
learned from data and retains information about edge contours and 3D curvature.
Our evaluation on temporal scan sequences of tomato plants shows, that
incorporating shape features improves temporal leaf-matching. A combination of
shape, location, and rotation information proves most informative for
recognition of leaves over time and yields a true positive rate of 75%, a 15%
improvement on sate-of-the-art methods. This is essential for robotic crop
monitoring, which enables whole-of-lifecycle phenotyping.
