Field phenotyping is fundamental to crop variety evaluation. However， traditional data acquisition methods are often destructive， costly， and subjective， and conventional 3D reconstruction techniques struggle to capture fine plant structures. To address these challenges， this study proposed a high-throughput phenotyping method for individual oat plants based on smartphone video acquisition and 3D Gaussian Splatting （3DGS） technology. The experiment utilized 187 oat plants at the milky stage as tested materials. Multi-view video data captured by smartphones were used to generate 3DGS point cloud models， from which canopy structural parameters—including projected area， maximum canopy width， and average canopy width-were extracted. Canopy volume was calculated using both Convex Hull and Voxelization （voxel size 0.01 m and 0.005 m） methods. Furthermore， biomass （fresh and dry weight） prediction models based on canopy volume were constructed using Random Forest （RF） and Support Vector Machine （SVM） algorithms. The results showed that： （1） 3DGS technology achieved high-fidelity reconstruction of the complex morphology of oats， effectively resolving issues related to slender leaves， thin stems， and severe self-occlusion， thereby demonstrating significant advantages over traditional reconstruction methods in restoring intricate structural forms； （2） Biomass estimation models based on 3DGS point cloud volume were reliable， with dry weight prediction accuracy （Accuracy≈0.88） generally superior to that of fresh weight； notably， high-resolution voxelization （voxel size 0.005 m） demonstrated a slight advantage in accurately describing the physical canopy structure. The workflow established in this study-“Smartphone Video Acquisition-3DGS Modeling-Phenotypic Parameter Extraction”-offered a low-cost， high-precision， and non-destructive solution， providing effective technical support for high-throughput phenotypic analysis of fields.

Gaussian splatting  /  Oat  /  Biomass prediction  /  Machine learning  /  Canopy volume  /  High-throughput phenotyping