Limited-Annotation Seed Segmentation for Analyzing the Impact of Unsound Corn on Storage Quality

Grain quality inspection is crucial for seed stored, with image segmentation playing a key role in this process. However, existing methods face challenges such as high computational costs, expensive data annotation, and data privacy concerns, which hinder the acquisition of large-scale labeled datasets and limit model performance. To overcome these challenges, we propose a novel semi-supervised learning (SSL) paradigm for seed segmentation. Our approach integrates VMUNet and UNet into a unified framework, combining UNet’s capacity for fine-grained detail extraction with VMUNet’s strengths in global semantic model, enabling richer pixel-level feature representation. We introduce an orthogonal attention mechanism into the VMUNet encoder to model feature dependencies across channel, spatial, and scale dimensions, improving information fusion and feature enhancement. Additionally, a perturbation strategy is applied in the dual-branch decoder, combined with consistency regularization, to enhance robustness and generalization. This helps mitigate overfitting and reduces excessive reliance on boundary details during decoding. Experimental results on a corn seed dataset show that the proposed method achieves 91.2% accuracy with 100% labeled data and 91.9% with only 50% labeled data, outperforming fully supervised methods by 0.6%. These results demonstrate the method’s high segmentation performance and practical potential while maintaining data privacy. These results confirm that OAMamba provides an accurate, robust, and annotation-efficient solution for corn seed segmentation, showing strong potential for practical deployment in agricultural intelligent inspection systems.