  Automated segmentation of individual leaves of a plant in an image is a
prerequisite to measure more complex phenotypic traits in high-throughput
phenotyping. Applying state-of-the-art machine learning approaches to tackle
leaf instance segmentation requires a large amount of manually annotated
training data. Currently, the benchmark datasets for leaf segmentation contain
only a few hundred labeled training images. In this paper, we propose a
framework for leaf instance segmentation by augmenting real plant datasets with
generated synthetic images of plants inspired by domain randomisation. We train
a state-of-the-art deep learning segmentation architecture (Mask-RCNN) with a
combination of real and synthetic images of Arabidopsis plants. Our proposed
approach achieves 90% leaf segmentation score on the A1 test set outperforming
the-state-of-the-art approaches for the CVPPP Leaf Segmentation Challenge
(LSC). Our approach also achieves 81% mean performance over all five test
datasets.
