Recently, color index and light detection and ranging (LiDAR) point cloud data have been extensively used in agriculture and forestry remote sensing. However, they bring the characteristics of different objects in the same spectrum and data redundancy. Considering pitaya plants in the Karst Plateau Valley area as an example, the method test area and accuracy verification area were set using UAV visible light images and image matching point cloud data. By fusing the calculation results of four color indexes of visible band difference color index (VDVI), red green blue color index (RGBVI), normalized green blue difference index (NGBDI), normalized green red difference color index (NGRDI) and canopy height model (CHM) data, the identification rules of pitaya single plant that fuse color index and spatial structure of point cloud data were developed for segmentation and extraction. The accuracy evaluation data of real pitaya plant contour was established as a reference. The precision of fusion extraction of four color indexes and point cloud data was compared with a single factor of color index or CHM segmentation. Then, the optimal extraction scheme is selected to confirm the feasibility of the proposed method. The results are as follows. The fusion method of the color index and spatial structure has higher extraction accuracy. The F measures are >91%, and the difference between the matching area and mean values of the real value is ~0.1 m². The VDVI index fusion results achieved the highest accuracy. The area value per plant was the closest to the true value; the root mean square error (RMSE) was 0.28 m², and the area value data were concentrated. The F measure in the accuracy verification area was 88.12%, and the RMSE was 0.27 m². The overall extraction effect of pitaya plants was good; however, low shrubs could affect the accuracy of pitaya plants identification to certain extent. The proposed method of fusion image spectral features and spatial structure of point cloud data can effectively enhance plant recognition features. It has good adaptability for identifying pitaya plants in Karst mountains, which can provide a reference for the extraction potential of a single pitaya plant in Karst mountains..