The image segmentation of target plant plays an important role in the automation of plant target detection and variable spray. The application of a single two-dimensional feature to object orientation, tracing and other occasions cannot meet the requirements of modern agriculture. However, in the segmentation of the three dimensional characteristics of plants, the traditional supervoxel clustering segmentation has the problem of high segmentation rate and poor real-time performance of plant. To solve this problem, a super voxel segmentation method was proposed, which fused saliency maps. Firstly, the color and depth maps of target plant were acquired in real time by using Kinect V2, and the RGB (RGB color model) color space images were converted into CIELab (CIELab color model) color space images. The eigenvalues of each pixel were calculated, and then the color feature map was obtained. After obtaining three feature graphs, fusion color feature graph, luminance feature graph and direction feature graph were used to construct a significant feature graph, and then the saliency map and the depth map were synchronously aligned to obtain the significant point cloud. The octree grid was used to initialize point cloud, and the grid point cloud was obtained, which satisfied the probability density threshold through Mean-Shift algorithm, and taking the maximum probability density point as the seed point，based on the Euclidean distance between points and CIELab similarity criterion as regional growth, the super voxels were generated. Finally, the locally convex connected patches (LCCP) algorithm was used to cluster the salient point cloud. The experimental results showed that the improved supervoxels based on salient point cloud-locally convex connected patches (SSV-LCCP) algorithm method can greatly improve the accuracy and rapidity of the target foreground segmentation, and effectively overcome the background noise and outliers.