Minirhizotron technology is widely used for studying the development of roots. Such systems collect visible-wavelength color imagery of plant roots in-situ by scanning an imaging system within a clear tube driven into the soil. Automated analysis of root systems could facilitate new scientific discoveries that would be critical to address the world's pressing food, resource, and climate issues. A key component of automated analysis of plant roots from imagery is the automated pixel-level segmentation of roots from their surrounding soil. Supervised learning techniques appear to be an appropriate tool for the challenge due to varying local soil and root conditions, however, lack of enough annotated training data is a major limitation due to the error-prone and time-consuming manually labeling process. In this paper, we investigate the use of deep neural networks based on the U-net architecture for automated, precise pixel-wise root segmentation in minirhizotron imagery. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. We trained three neural networks with different architectures on the larger peanut root dataset to explore the effect of the neural network depth on segmentation performance. To tackle the more limited switchgrass root dataset, we showed that models initialized with features pre-trained on the peanut dataset and then fine-tuned on the switchgrass dataset can improve segmentation performance significantly. We obtained 99\% segmentation accuracy in switchgrass imagery using only 21 training images. We also observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset are more effective than features pre-trained on the large but unrelated ImageNet dataset.

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使用迁移学习克服小型迷你根管数据集

Minirhizotron 技术被广泛用于研究根的发育。这些系统通过扫描驱动到土壤中的透明管内的成像系统来原位收集植物根系的可见波长彩色图像。根系的自动分析可以促进新的科学发现，这对于解决世界紧迫的粮食、资源和气候问题至关重要。从图像自动分析植物根系的一个关键组成部分是从周围土壤中自动像素级分割根系。由于当地土壤和根系条件不同，监督学习技术似乎是应对挑战的合适工具，但是，由于容易出错且耗时的手动标记过程，缺乏足够的带注释的训练数据是一个主要限制。在本文中，我们研究了使用基于 U-net 架构的深度神经网络在 minirhizotron 图像中进行自动、精确的像素级根分割。我们编译了两个 minirhizotron 图像数据集来完成这项研究：一个包含 17,550 个花生根图像，另一个包含 28 个柳枝稷根图像。两个数据集都与手动标记的地面实况掩码配对。我们在更大的花生根数据集上训练了三个具有不同架构的神经网络，以探索神经网络深度对分割性能的影响。为了处理更有限的柳枝稷根数据集，我们展示了使用在花生数据集上预先训练的特征初始化然后在柳枝稷数据集上进行微调的模型可以显着提高分割性能。我们仅使用 21 个训练图像在柳枝稷图像中获得了 99% 的分割精度。我们还观察到，在密切相关但相对中等大小的数据集（如我们的花生数据集）上预训练的特征比在大型但不相关的 ImageNet 数据集上预训练的特征更有效。