Improving Deep Learning Predictions with Simulated Images, and Vice Versa

arXiv:2404.05128v1 Announce Type: cross
Abstract: 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 …

abstract artificial artificial neural networks arxiv cs.cv cs.lg deep learning features however identify images improving networks neural networks plants predictions systems training type