Single-View 3-D Reconstruction of Jujube Through Diffusion Model and Distributed Computing in Internet of Unmanned Agents

As a key economic crop, jujube’s external morphology directly affects quality grading and market value. However, traditional inspection methods relying on manual sampling or 2-D image analysis suffer from inefficiency and limited feature characterization, particularly in quantifying complex geometric traits, such as irregular wrinkles and localized depressions on jujube surfaces. Existing 3-D reconstruction techniques have been applied in agricultural product inspection but face challenges in widespread adoption due to high costs and low resolution. This study proposes a single-view high-resolution RGB 3-D reconstruction method for jujube based on generative artificial intelligence. Specifically, we designed a two-stage single-view 3-D reconstruction framework and modified the cross-attention layers in the U-shaped network architecture of a stable video diffusion mode to meet the requirements of high-resolution and clear texture reconstruction for jujube. Additionally, we improved training and inference efficiency through parallel computing and achieved automation integration with Unmanned Agents. The proposed method successfully reconstructed 3-D models from single-view $1024\times 1024$ RGB images of jujube. Our model achieves a PSNR of 23.52 and an SSIM of 0.86 on the public dataset. This approach provides a low-cost, high-precision 3-D digital solution for nondestructive phenotyping of jujube, offering practical value for advancing intelligent sorting and quality evaluation in agricultural production.