Cross-Temporal Egg Variety and Storage Period Classifications via Multi-Task Deep Learning with Near-Infrared Hyperspectral Imaging

Egg variety and storage duration are key determinants of nutritional value, market pricing, and food safety. The similar external appearance of different varieties increases the risk of mislabeling, while inevitable quality deterioration during storage further complicates reliable assessment. These factors underscore the need for non-destructive, cross-temporal detection. However, prolonged storage induces pronounced spectral drift that degrades conventional models, limiting their effectiveness in real-world quality monitoring. To address this issue, we propose the Multi-Task Cross-Temporal Squeeze-and-Excitation Network (MT-CTSE-Net), a deep learning framework that integrates Convolutional Neural Networks (CNN), Squeeze-and-Excitation (SE) channel attention, and Transformer encoders to jointly perform egg variety identification across storage durations and storage period classification. The model extracts local spectral details, enhances channel-wise feature relevance, and captures long-range dependencies, while inter-task feature sharing improves generalization under temporal variation. Evaluated on near-infrared (1000–2500 nm) spectra from three commercial egg varieties (Enshi selenium-enriched, Mulanhu multigrain, Zhengda lutein), MT-CTSE-Net achieved approximately 86% accuracy (F1-score: 86.1%) in cross-temporal variety classification and about 84.2–86.4% in storage-period prediction—surpassing single-task and benchmark multi-task models. These results demonstrate that MT-CTSE-Net effectively mitigates storage-induced spectral drift and provides a robust pathway for non-destructive quality assessment and temporal monitoring in agri-food supply chains.