Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

中文翻译：

---

植物模型在深度学习中的应用：莲座植物叶子计数的应用。

深度学习为基于图像的植物表型分析提供了许多机会。在这里，我们考虑深度卷积神经网络执行叶子计数任务的能力。深度学习技术通常需要大量且多样化的数据集来学习可概括的模型，而无需提供用于执行任务的先验工程算法。然而，对于植物表型分析领域的应用来说，这一要求具有挑战性，因为可用的数据集通常很小，并且生成新数据的成本很高。在这项工作中，我们提出了一种使用合成植物的渲染图像来增强植物表型数据集的新方法。我们证明，使用高质量 3D 合成植物来扩充数据集可以提高叶子计数任务的性能。我们还表明，该模型生成任意表型分布的能力可以缓解在不同数据集上进行训练和测试时数据集偏移的问题。最后，我们表明，在训练叶子计数任务的神经网络时，真实植物和合成植物具有显着的互换性。