Research on Grapevine Structure Segmentation Method Based on Machine Vision
The precisive segmentation of grapevine structure is an important prerequisite for reasoning and locating the pruning points.To precisely segmentate grapevine structure,this article established a vine structure data set under natural planting conditions,and proposed a grape vine structure division method based on the U-net model.Through the comparative experiment of backbone feature extraction network and model segmentation performance,the optimal U-net model structure was obtained and its segmentation performance under different density degree targets was verified.The results showed that the precision of the U-net model with VGG 16 as the backbone feature extraction network was 93.55％,the recall was 94.15％,the mean pixel accuracy was 94.15％,and the mean intersection over union was 88.65％.Compared with traditional image segmation methods and control group model segmentation effects,it could ensure that the structure of the grape vines was complete in the context of natural planting,and the connection relationship between the structure was correct,so it was suitable for the segmentation task of grapevine structures with shade between plants,laid the foundation for achieving intelligent grape vines in winter pruning operations.
万方数据
维普
