A conditional segmentation-guided network for pomegranate image completion under occlusion.

In agricultural images acquired under natural conditions, pomegranate fruits are often partially occluded by leaves and branches, resulting in missing structural information that compromises the accuracy of yield estimation and automated harvesting. To overcome the challenges of recovering structural integrity in occluded agricultural imagery, we propose the Conditional Segmentation-guided Diffusion Network (CSD-Net). CSD-Net is a lightweight, unified framework, representing the first conditional diffusion model specifically designed for the joint tasks of pomegranate image completion and segmentation. CSD-Net aims to address the structural fidelity limitations of traditional completion methods. It utilizes a shared encoder, a segmentation branch, and an RGB diffusion branch. Crucially, the network leverages the segmentation mask as a key structural prior condition to guide the diffusion generation process. This innovative conditional guidance mechanism ensures high-fidelity reconstruction of fruit structures while maintaining spatial and textural consistency. Experimental results demonstrate that CSD-Net substantially outperforms conventional methods across metrics, achieving 30.37 dB in PSNR and 0.9490 in SSIM. Furthermore, its model size is only 117 MB, striking an effective balance between high completion quality and inference efficiency. This study offers a novel and highly effective solution for mitigating occlusion issues in agricultural visual perception tasks. Upon acceptance of this paper, the source code will be made publicly available at https://github.com/zdkd/PCSN .