  The traditional paradigm of applying deep learning -- collect, annotate and
train on data -- is not applicable to image-based plant phenotyping as almost
400,000 different plant species exists. Data costs include growing physical
samples, imaging and labelling them. Model performance is impacted by the
species gap between the domain of each plant species, it is not generalisable
and may not transfer to unseen plant species. In this paper, we investigate the
use of synthetic data for leaf instance segmentation. We study multiple
synthetic data training regimes using Mask-RCNN when few or no annotated real
data is available. We also present UPGen: a Universal Plant Generator for
bridging the species gap. UPGen leverages domain randomisation to produce
widely distributed data samples and models stochastic biological variation. Our
methods outperform standard practices, such as transfer learning from publicly
available plant data, by 26.6% and 51.46% on two unseen plant species
respectively. We benchmark UPGen by competing in the CVPPP Leaf Segmentation
Challenge and set a new state-of-the-art, a mean of 88% across A1-4 test
datasets. This study is applicable to use of synthetic data for automating the
measurement of phenotypic traits. Our synthetic dataset and pretrained model
are available at https://csiro-robotics.github.io/UPGen_Webpage/.
