Abstract. The detection of plant organs is an important research field of plant recognition area. However, due to the lack of database of plant organs, the application of convolutional neural network-based object detection on plant species is very limited. A database of plant organs for deep learning-based object detection is constructed. A huge number of plant images are clawed using specific keywords through keyword search engines such as Baidu and Google. After that, an automatic junk image cleaning method is performed to remove junk images. Finally, artificial labeling is used to delineate plant organ regions. To evaluate the quality of the database, experiments in different object detection models are implemented. Results show that the established plant organ database has good performance in plant organs positioning and classification.

中文翻译：

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走向自然界中的植物器官：植物器官系统的新数据库

摘要。植物器官的检测是植物识别领域的一个重要研究领域。然而，由于缺乏植物器官数据库，基于卷积神经网络的目标检测在植物物种上的应用非常有限。构建了一个基于深度学习的目标检测植物器官数据库。通过百度、谷歌等关键词搜索引擎，使用特定关键词抓取大量植物图片。之后，执行自动垃圾图像清理方法以去除垃圾图像。最后，人工标记用于描绘植物器官区域。为了评估数据库的质量，在不同的对象检测模型中进行了实验。结果表明，建立的植物器官数据库在植物器官定位和分类方面具有良好的性能。