Improving Deep Learning Predictions with Simulated Images, and Vice
  Versa
Nazifa Azam Khan Mikolaj Cieslak Ian McQuillan
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. Furthermore, we see how neural
network predictions can be used to help calibrate L-systems themselves,
creating a feedback loop.