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Accurate and efficient reconstruction of seedling crop structures is crucial for obtaining phenotype parameters. The traditional method for 3D reconstruction based on the structure from motion and multi-view stereo (SFM-MVS) algorithm, which had high reconstruction accuracy and high computional cost. It was difficult to meet the demand for rapid acquisition of phenotype parameters. A system for acquiring phenotype parameters and creating 3D models of seedling crops was proposed by using neural radiance fields (NeRF). The system utilized smart phone to capture RGB images of the objects from various viewpoints and constructed the 3D model through the NeRF algorithm. The algorithms of line fitting and region growing in point cloud library (PCL) were used to automatically segment the plants. Additionally, the algorithms of distance-minimum traversal, circle fitting, and triangulation were used to measure phenotype parameters such as plant height, stem diameter, and leaf area. To assess the reconstruction efficiency and accuracy of phenotype parameter measurement, seedling plants of pepper, tomato, strawberry and epipremnum aureum were selected as subjects. The reconstruction results were compared by using the NeRF and the SFM-MVS algorithm. The results indicated that both methods were capable of achieving superior reconstruction outcomes. The root mean square errors of the point-to-point distances of each seedlings were only 0.128cm to 0.359cm. But in terms of speed, this method improved the reconstruction speed by an average of 700% compared with the SFM-MVS method. The method used to extract plant height and stem diameter of chili pepper seedlings had a coefficient of determination (R2) of 0.971 and 0.907, respectively. The root mean square error (RMSE) was 0.86cm and 0.017cm, respectively. The R2 of the leaf area extracted from the plants at seedling stage ranged from 0.909 to 0.935, and the RMSE ranged from 0.75cm2 to 3.22cm2, indicating a high level of accuracy in measurement. The proposed method can significantly speed up 3D reconstruction and acquisition of phenotype parameters. This would provide a more efficient technical means for vegetable breeding and seedling selection.