  The rice panicle traits significantly influence grain yield, making them a
primary target for rice phenotyping studies. However, most existing techniques
are limited to controlled indoor environments and difficult to capture the rice
panicle traits under natural growth conditions. Here, we developed PanicleNeRF,
a novel method that enables high-precision and low-cost reconstruction of rice
panicle three-dimensional (3D) models in the field using smartphone. The
proposed method combined the large model Segment Anything Model (SAM) and the
small model You Only Look Once version 8 (YOLOv8) to achieve high-precision
segmentation of rice panicle images. The NeRF technique was then employed for
3D reconstruction using the images with 2D segmentation. Finally, the resulting
point clouds are processed to successfully extract panicle traits. The results
show that PanicleNeRF effectively addressed the 2D image segmentation task,
achieving a mean F1 Score of 86.9% and a mean Intersection over Union (IoU) of
79.8%, with nearly double the boundary overlap (BO) performance compared to
YOLOv8. As for point cloud quality, PanicleNeRF significantly outperformed
traditional SfM-MVS (structure-from-motion and multi-view stereo) methods, such
as COLMAP and Metashape. The panicle length was then accurately extracted with
the rRMSE of 2.94% for indica and 1.75% for japonica rice. The panicle volume
estimated from 3D point clouds strongly correlated with the grain number (R2 =
0.85 for indica and 0.82 for japonica) and grain mass (0.80 for indica and 0.76
for japonica). This method provides a low-cost solution for high-throughput
in-field phenotyping of rice panicles, accelerating the efficiency of rice
breeding.
