In order to rapidly and accurately obtain crop growth information such as plant height and volume in the field environment, this paper takes peanuts as the research object and adopts 3D LiDAR perception technology to obtain peanut point cloud data in the field. After registration, denoising, and other processing, a three-dimensional point cloud model is constructed. The point cloud plant segmentation method based on KD-TREE is used to segment individual peanut plants from the point cloud data. The convex hull algorithm is used to estimate plant volume, and the rotating caliper method is utilized to extract plant height and maximum canopy width, thereby obtaining peanut growth information. Point cloud data of peanut plants at three different growth stages were collected in a peanut planting experimental area. The proposed method was used to carry out verification tests for individual peanut plant segmentation and extraction of plant height and maximum canopy width. The accuracy of growth information acquisition was investigated, and recall and precision rates were used to evaluate the results. Experimental results showed that the recall and precision rates of individual peanut plant segmentation in the field could reach over 85%, indicating that the proposed method has good accuracy and completeness for segmenting peanut point cloud data in the field. The extracted parameters such as peanut plant height and maximum canopy width were compared with manual measurements. The average absolute percentage errors of plant height in the three different growth stages were 6.2705%, 4.3675%, and 4.9859%, respectively, and the maximum canopy widths were 7.1140%, 5.6063%, and 4.5410%, respectively. The root mean square errors of plant height were 0.0096m, 0.0152m, and 0.0271m, respectively, and the root mean square errors of the maximum canopy width were 0.0110m, 0.0201m, and 0.0203m, respectively. The linear regression determination coefficients of plant height data were 0.88797, 0.95101, and 0.84183, respectively, and the linear regression determination coefficients of maximum canopy width data were 0.93431, 0.93179, and 0.92717, respectively. These results verify the accuracy and feasibility of using point clouds to measure peanut growth data, enabling high-precision, non-destructive extraction of peanut phenotypic parameters. The research results provide important technical support for peanut cultivation and breeding.