Abstract: Accurate acquisition and analysis of crop geometric information is an important basis for the implementation of precision agriculture. Canopy height and volume are important decision parameters for variable sprayer application rate. In the field environment, the large change of ambient light has an important influence on the measurement of canopy geometry information by sensors. At the same time, there are few researches on the problems of remove the effect of ground roughness and difficulty in distinguishing individual plants due to branches and leaves crossing under the ridge planting mode of field soybean. Therefore, it is necessary to design an information acquisition system that is less affected by light conditions and an algorithm to improve the ability to extract geometric information from individual crops. In this study, a crop phenotype detection system based on airborne lidar was constructed and its accuracy was verified. A method of extracting individual plant based on local geometric feature segmentation and mean shift algorithm was proposed. In the process of soybean plant and ground classification, firstly, the local geometric features constructed in the optimal neighborhood are classified into 2D and 3D local shape features according to their dimensions. Secondly, in order to select 5 feature combinations that are strongly related to classification, all features were evaluated using Gini index algorithm, Chi-square algorithm, ReliefF algorithm, and random forest method. Finally, according to different feature combinations, a random forest classifier is selected to predict the test set data. In the process of extracting a single soybean plant, the point cloud data of different plants were used to obtain the point cloud data of a single plant using the mean shift algorithm to complete the extraction of a single soybean plant. In the process of obtaining geometric information of single plants, the height of plants was defined as the height difference from the intersection point of soybean stem and ground to the highest point of crops. In actual measurement, because the laser beam was blocked by branches and leaves, it is difficult to obtain the intersection point of soybean stem and ground, so the paper used the method of projecting the center of gravity of single point cloud to the ground fitting surface to estimate the intersection point. Furthermore, the plant height of single plant was obtained by subtracting the estimated intersection point from the maximum point. The experimental results showed that the maximum relative errors of the lidar scanning measurement system along the carrier moving direction, vertical moving direction and vertical ground direction were 0.58% (5.8 cm), ?1.75% (?7.0 cm) and ?1.74% (?3.4 cm), respectively. In the process of soybean crop and ground classification, the AUC (area under curve) value of the classification index ROC (receiver operating characteristic) curve was 0.994, achieving a good classification effect based on feature combination which was selected from 26 features using random forest algorithms. The relative error was 11.83% between the number of artificially counted plants and the number of manual measurements, and the distribution correlation was the highest with R=0.675 when the mean shift algorithm parameter is 20 cm. The average relative error of the height estimated method in this paper was 5.14%, which was better than RANSAC algorithm. This paper can provide reference for crop segmentation and yield statistics. Future research should focus on converting the obtained target crop information into a prescription map and storing it in a server for application in online spraying.