Abstract: Accurate identification of cotton top bud is important for cotton topping operation to detect cotton terminal bud accurately in field, a recognition method using Faster R-CNN (Faster Region Convolutional Neural Networks, Faster R-CNN) by integrating dynamic mechanism was proposed to solve the recognition difficulties caused by the small size of cotton terminal bud when it is topped in densely planted fields. The RegNetX-6.4GF model was used as the backbone network to improve its image feature extractor capabilities. Due to number of proposals under a higher IOU(Intersection Over Union, IOU) as well as the matching degree between anchor and the target shape affect the performance of the detector, the method proposed in this paper changed the original anchor generation mechanism by combining FPN (Feature Pyramid Network, FPN) and Guided Anchoring in RPN (Region Proposal Network, RPN), which will cause the distribution of the proposals generated by the RPN of the algorithm under different IOUs dynamically change during the training process. To adapt the dynamic change of proposals distribution, we integrated Dynamic Region Convolutional Neural Networks ( Dynamic R-CNN) in Faster R-CNN, which can dynamically adjust the value of IOU to obtain high quality proposals. And the GROIE (Generic ROI Extractor, GROIE) mechanism was inducted to extract ROI (Region of Interest, ROI) to improve the feature fusion capability. In this paper, 4 819 images of gossypium hirsutum population which contain seven leaf types were taken from the top of cotton plant at distance of 30-50 cm (medium distance) and 50-100 cm (long distance) under uniform light, oblique strong light, direct strong light, and shadows. Those images were processed as MS COCO 2017 format dataset and assigned to the training set, validation set, and test set, which contained 2 815, 704, and 1 300 pictures respectively. The experimental results demonstrated that FPS (Frames Per Second, FPS) of proposed model was up to 10.3 frames/s and the Mean Average Precision (MAP) of bud identification reached to 98.1% which was 3.2 percentage points higher than original Faster R-CNN model. The validation set were used to compare performance of mainstream recognition algorithm and proposed method. Results showed that the improved Faster R-CNN's MAP was 7.3% higher than original Faster R-CNN, which was also higher than RetinaNet, Cascade R-CNN (Cascade Region Convolutional Neural Networks, Cascade R-CNN) and RepPoints by 78.9%, 10.1% and 8.3% when IOU was set to 0.5. The improved Faster R-CNN proposed in this paper meets the accuracy and real-time requirements of cotton topping operation.