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.