Abstract: 【Objective】 To achieve the accurate acquisition of the canopy data of single plant in the greenhouse tomato crop row, to analyze the crop growth status and to provide canopy data support for target spraying. 【Methods】 A 3 d LiDAR was used to build A detection platform for tomato plant canopy. A 3D LiDAR was used to move the 3D LiDAR at A speed of 0.05 m/s. A total of 40 tomato plant point clouds were saved by Ctrlview, the upper computer software of the radar. To bilateral point cloud using ICP (iterative closest point) algorithm for registration, the plane fitting method based on characteristic value is to remove the ground, using the mean shift algorithm (Meanshift) line segmentation of tomato yield point in the cloud, obtain canopy parameters, and comparing with manual measurement verification accuracy, finally will yield point cloud using alpha in MATLAB algorithm based on the shape reconstruction and volume, and compared using convex hull algorithm crops reference. 【Results】 The test results show that the measurement errors of the platform in the forward direction and the vertical direction of the lidar are -2.65% and -3.95% respectively. The average absolute error M 12 was 0.025 m and 0.031 m, respectively, compared with the measured height. The average error of the volume obtained by reconstruction using Alpha Shape algorithm is about 15.3% lower than that of the convex hull algorithm, which is not much different from that obtained manually, and the indexes are good. 【Conclusion】 The mean shift algorithm is adopted to tomato line point cloud segmentation result compared with the artificial measure A, B two groups of root mean square error of the RMSE are 0.039 and 0.043 respectively, using the alpha shape algorithm canopy volume access and reference comparison V_RMSE 0.011,3, shows that the laser radar in crop silhouette information has A certain accuracy and reliability of this method can be used to plant crop canopy of greenhouse environment data acquisition.