  Artificial neural networks are often used to identify features of crop
plants. However, training their models requires many annotated images, which
can be expensive and time-consuming to acquire. Procedural models of plants,
such as those developed with Lindenmayer-systems (L-systems) can be created to
produce visually realistic simulations, and hence images of plant simulations,
where annotations are implicitly known. These synthetic images can either
augment or completely replace real images in training neural networks for
phenotyping tasks. In this paper, we systematically vary amounts of real and
synthetic images used for training in both maize and canola to better
understand situations where synthetic images generated from L-systems can help
prediction on real images. This work also explores the degree to which realism
in the synthetic images improves prediction. We have five different variants of
a procedural canola model (these variants were created by tuning the realism
while using calibration), and the deep learning results showed how drastically
these results improve as the canola synthetic images are made to be more
realistic. Furthermore, we see how neural network predictions can be used to
help calibrate L-systems themselves, creating a feedback loop.
