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Focusing on potted succulent plants, handheld RGB cameras were utilized to collect video data of 11 potted succulent plants. By converting videos into image frames, high-quality clear frames were selected, and camera poses were calculated, and containing rich information RGB image data was obtained. An improved method for three-dimensional reconstruction of succulent plants based on NeRF was proposed. A new ray sampling strategy tailored to actual scenes was introduced, along with an enhanced image restoration module and an implicit model for point cloud reconstruction. Seven phenotypic parameters of succulent plants were extracted from the point cloud reconstruction results, including leaf count, plant height, crown circumference, convex hull volume, leaf length, leaf width, and leaf color. Finally, a precision assessment and error analysis were conducted on five representative and easily measurable phenotypic parameters: leaf count, plant height, crown circumference, leaf length, and leaf width. The mean absolute percentage error (MAPE) for these parameters was respectively 2.32%, 3.95%, 4.95%, 5.59%, and 9.55%, and the root mean square error (RMSE) was respectively 0.86 leaves and 1.95 mm, 17.54 mm, 1.87 mm, 1.27 mm, with respective R2 values of 0.99, 0.99, 0.86, 0.91, and 0.89. The results of precision assessment indicated that the extracted phenotypic parameters can accurately and efficiently reflect the growth status of succulent plants. By leveraging advantages in RGB image synthesis technology, image processing, and 3D point cloud reconstruction, non-destructive extraction of phenotypic parameters for potted succulent plants was achieved with high precision. The research result can provide important technical support for succulent plant cultivation and nurturing, as well as for studies involving non-fixed, multi-perspective RGB data acquisition.