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.

翻译：人工神经网络常被用于识别作物特征，但训练其模型需要大量带标注的图像，而获取这些图像成本高昂且耗时。基于林登迈耶系统（L-systems）等程序化模型可生成视觉真实的模拟图像，其标注信息隐式可知。这些合成图像可增强或完全替代真实图像，用于训练表型分析任务的神经网络。本文系统性地改变玉米和油菜训练中真实图像与合成图像的比例，以深入理解L-systems生成合成图像辅助真实图像预测的适用场景。本研究还探索了合成图像真实性对预测性能的提升程度。我们设计了五种不同真实度的程序化油菜模型（通过调整真实性参数并校准得到），深度学习结果表明：随着油菜合成图像真实性的提升，预测效果显著改善。此外，我们发现神经网络预测结果可用于反哺L-systems的校准，形成闭环反馈机制。