The generation of morphological traits of plants such as the leaf length, width, perimeter, area, and petiole length are fundamental features of herbarium specimens, thus providing high-quality data to investigate plant responses to ongoing climatic change and plant history evolution. However, the existing measurement methods are primarily associated with manual analysis, which is labor-intensive and inefficient. This paper proposes a deep learning-based approach, called Deep Leaf, for detecting and pixel-wise segmentation of leaves based on the improved state-of-the-art instance segmentation approach, Mask Region Convolutional Neural Network (Mask R-CNN). Deep Leaf can accurately detect each leaf in the herbarium specimen and measure the associated morphological traits. The experimental results indicate that our automated approach can segment the leaves of different families. Compared to manual measurement done by ecologist and botanist experts, the average relative error of leaf length is 4.6%, while the average relative error of leaf width is 5.7%.

叶片长度、宽度、周长、面积和叶柄长度等植物形态特征的产生是植物标本的基本特征，从而为研究植物对持续气候变化和植物历史演变的反应提供了高质量的数据。然而，现有的测量方法主要与人工分析相关，劳动密集且效率低下。本文提出了一种基于深度学习的方法，称为 Deep Leaf，基于改进的最先进实例分割方法 Mask Region Convolutional Neural Network (Mask R-CNN) 来检测和逐像素分割叶子。Deep Leaf 可以准确检测植物标本中的每一片叶子，并测量相关的形态特征。实验结果表明，我们的自动化方法可以分割不同科的叶子。与生态学家和植物学家的人工测量相比，叶长的平均相对误差为4.6%，而叶宽的平均相对误差为5.7%。