GAPose-GS: Globally adaptive pose-optimized gaussian splatting for plant 3D reconstruction towards more precise phenotyping

High-precision plant phenotyping requires efficient 3D reconstruction with high fidelity, yet existing methods such as MVS and NeRF all have problems of feature dependence and error accumulation during 3D reconstruction, which leads to geometric distortion in reconstruction and restricts the reconstruction efficiency. To address this bottleneck, this study first determined the multi-view image acquisition strategy. Further, based on the self-built multi-view dataset of chili peppers, it proposed an algorithm for efficient and high-fidelity 3D reconstruction of complex plant structures through global adaptive pose optimization and gaussian splash rendering technology, referred to as the GAPose-GS algorithm. Experimental results indicate that the Peak Signal-to-Noise Ratio ( PSNR ) improves by 52.0 %, 26.4 %, and 4.2 % compared to NeRF, Instant-NGP, and 3D Gaussian Splatting respectively. Additionally, the Structural Similarity Index Measure ( SSIM ) increases by 22.9 %, 12.8 %, and 4.3 % respectively over above methods. The point cloud data reconstructed based on this algorithm also has advantages in the measurement of phenotypic parameters. Compared with the actual measured values, the R² of the phenotypic parameters such as pepper plant height, canopy width, and leafstalk angle obtained in this study are 0.997, 0.954 and 0.978 respectively, and the RMSE are 0.236 cm, 1.082 cm and 2.344° respectively, and the MAE are 0.209 cm, 0.880 cm and 1.965° respectively. The accuracy was significantly better than that of the existing phenotypic calculation methods. Verification across different growth stages of wheat and maize was performed universally, with all errors remaining below 1.1 %, providing new ideas and technologies for high-precision, low-cost, and high-throughput crop phenotypic research.