Abstract
Maize plant height and stem diameter can effectively reflect plant growth status and lodging resistance. Therefore, accurate measurement of these parameters enables precise monitoring of plant growth and provides data to support agricultural decision-making. LiDAR enables the acquisition of three-dimensional (3D) crop point clouds, providing a direct source of data for phenotypic parameter extraction. However, existing 3D point cloud acquisition systems for crop phenotyping are often costly and offer limited operational flexibility under field conditions. This study proposes a workflow for extracting key phenotypic parameters from individual field-grown maize plants by integrating 3D reconstruction with point cloud instance segmentation. First, field-grown maize plants are reconstructed in 3D using a LiDAR–SLAM system mounted on an unmanned ground vehicle (UGV). Because accurate segmentation of individual maize plants from field LiDAR point clouds is challenging, a density-adaptive strategy is incorporated into an instance segmentation network based on central-axis offset aggregation. Specifically, each point is projected toward the central axis of its corresponding plant, and local point density is used to guide individual-plant instance segmentation. Plant height and stem diameter are subsequently extracted automatically from the segmented point cloud of each plant and evaluated against manual measurements. Experimental results show that the density-adaptive instance segmentation method achieves an mAP50-95 of 77.84%, representing an improvement of 8.54%. The mean absolute errors (MAEs) are 7.52 cm for plant height and 1.02 mm for stem diameter. These results demonstrate that, under the tested grain-filling-stage field plots, the proposed method enables effective individual-plant segmentation and extraction of key phenotypic parameters. Given that validation was limited to two plots acquired on a single date, claims of broad field robustness should be interpreted within this experimental scope.