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.

通过使用 L 系统生成的合成图像，可以在培训神经网络进行表型鉴定任务时，通过增加或完全替代真实图像，从视觉上更真实地模拟植物，从而在更好地理解合成图像生成的情况下使用不同数量的真实图像和合成图像进行系统化分析。此外，利用神经网络的预测结果可以用于校准 L 系统本身，从而形成一个反馈循环。