The generation of morphological data in evolutionary, taxonomic, and ecological studies of plants using herbarium material has traditionally been a labor‐intensive task. Recent progress in machine learning using deep artificial neural networks (deep learning) for image classification and object detection has facilitated the establishment of a pipeline for the automatic recognition and extraction of relevant structures in images of herbarium specimens.

中文翻译：

---

GinJinn：用于从植物标本中自动提取特征的对象检测管道。


使用植物标本馆材料生成植物进化、分类和生态研究中的形态数据传统上是一项劳动密集型任务。使用深度人工神经网络（深度学习）进行图像分类和对象检测的机器学习的最新进展促进了自动识别和提取植物标本图像中相关结构的管道的建立。