关键词: 图像分割, 特征提取, 葡萄藤, 深度学习

Abstract: The precise identification and positioning of pruning points is the basis for the intelligent pruning of grapevines in winter, the segmentation of the critical structure of the grapevine is an important prerequisite for reasoning about the precise pruning point. Aiming at the problem that the existing cutting method is greatly affected by the background, resulting in the loss of critical structures of the grapevine, and inaccurate identification and positioning of pruning points, a segmentation method of grapevine critical structure based on Mask R-CNN was proposed, the grapevine pruning model and the critical structure data sets were established. Through the comparative experiment of backbone feature extraction network and segmentation performance, the optimal Mask R-CNN model structure was obtained and its fitting and generalization ability and segmentation performance in different natural backgrounds were verified, The results showed that the Mask R-CNN model with ResNet 101+FPN as the backbone feature extraction network proposed had better fitting and generalization ability, compared with the control group model, the accuracy rate was increased by 7.33% and 8.89%, the recall rate was increased by 9.32% and 9.26%, and the average precision was increased by 12.69% and 12.63% respectively, it could overcome various natural planting background factors, the edge of the segmentation target was complete, and the connection relationship between the critical structures of the grapevine was correct.

Key words: image processing, feature extraction, grapevine, deep learning