Abstract: To address inefficiencies and limited automation in traditional methods for acquiring mature wheat three-dimensional phenotyping, which struggle to balance efficiency and precision, a comprehensive phenotypic workflow based on 3D Gaussian Splatting（3DGS）was established．This approach integrated three technical modules: high-fidelity 3D reconstruction from multi-view images using 3DGS, extraction and accuracy validation of phenotypic traits represented by plant height, and organ segmentation（leaf, stem, spike）via point cloud analysis using PointNet++ model．Experimental results showed that 3DGS could efficiently reconstruct detailed three-dimensional wheat plant models, achieving peak signal-to-noise ratios, structural similarity indices, and learned perceptual image block similarity of 36.9594 dB, 0.9746, and 0.1146, respectively．Plant height measurements showed high consistency with manual data（determination coefficient R² = 0.9713, root mean square error was 1.565 cm）．PointNet++ model achieved best organ segmentation accuracy and average intersection-over-union ratios of 0.78069 and 0.63954 under optimized parameters（10000 sampling center points）．On test set, ear segmentation accuracy was the highest, with precision rate of 0.8604 and intersection over union of 0.7547．Three-dimensional models of wheat generated using this method exhibited high-quality reconstruction and precision, confirming its advantages in efficiency and precision for three-dimensional phenotyping analysis and demonstrating strong application potential．