Rice is a major staple food crop worldwide, and accurate measurement of phenotypic traits during the tillering stage is essential for breeding programs and yield assessment. Conventional measurement methods are often time-consuming, labor-intensive, and susceptible to subjective errors. To overcome these limitations, this study introduces a 3D reconstruction approach based on Neural Radiance Fields(NeRF) for high-precision, non-destructive extraction of phenotypic parameters of rice at the tillering stage. The method begins by capturing multi-view videos of rice plants using a consumer-grade smartphone, followed by an adaptive frame extraction algorithm to obtain high-quality image sequences. Camera poses are then estimated using Structure-from-Motion (SfM), and an improved Instant-NGP algorithm is applied for efficient 3D reconstruction. Compared to the original NeRF, the proposed method achieves a 17.3% improvement in peak signal-to-noise ratio, a 54.3% reduction in GPU memory usage, and a 99.4% decrease in reconstruction time. The resulting point clouds undergo preprocessing—including downsampling, denoising, coordinate correction, and segmentation—to extract key phenotypic traits such as plant height, stem diameter, tiller number, tiller angle, projected area, bounding box volume, and leaf count. Experimental results show strong agreement between automated and manual measurements, with coefficients of determination (R²) of 0.98, 0.94, 1.00, 0.95, and 0.97 for plant height, stem diameter, tiller number, tiller angle, and leaf number, respectively. The corresponding mean absolute percentage errors were 2.38%, 5.16%, 0%, 7.15%, and 2.20%. This research offers reliable technical support for rice breeding and precision cultivation.