Jialu Li

Existing studies on non-destructive visual inspection of wheat kernels mainly focus on single-task variety or defect recognition, limiting their ability to jointly exploit complementary surface information, disentangle source-related and quality-related representations, and mitigate error propagation between prediction tasks. To address these limitations, this study proposes a Source-Aware Multi-Task Grain Network (SAMG-Net) for joint wheat-kernel source recognition and appearance-quality classification. First, a task-guided dual-view multi-scale feature fusion mechanism is developed to capture complementary information from paired UP and DOWN kernel images through cross-view feature interaction, task-specific attention, and adaptive multi-scale aggregation, enabling source recognition and appearance-quality classification to emphasize different morphological, textural, and localized defect characteristics. Next, shared/task-specific representation disentanglement and conditional contrastive learning are integrated to reduce source–quality entanglement by explicitly modeling “same source, different quality” and “same quality, different source” relationships. Furthermore, a confidence-gated soft source-conditioned residual modulation mechanism incorporates calibrated source-probability distributions and prediction confidence into adaptive quality-feature modulation, allowing reliable source information to support appearance-quality classification while limiting the propagation of uncertain source predictions. Experiments on the GrainSet wheat subset demonstrate that SAMG-Net achieves Macro-F1 scores of 0.915 for source recognition and 0.921 for appearance-quality classification, exceeding the strongest baselines by 2.1 and 2.5 percentage points, respectively, with standard deviations of 0.002–0.003 across five independent runs. Additional source–quality combination-holdout, robustness, and view-sensitivity analyses indicate favorable performance under the evaluated internal compositional shifts and controlled visual perturbations, while source-probability calibration reduces the Expected Calibration Error from 0.043 to 0.018. Overall, SAMG-Net provides a dual-view RGB framework for jointly characterizing dataset-defined wheat source categories and visible appearance quality, with potential applications in preliminary grain screening, appearance-based grading, and automated sorting.

With the increasing globalization of supply chains, ensuring food safety has become more complex, necessitating advanced approaches for risk assessment. This study aims to review the transformative role of machine learning (ML) and deep learning (DL) in enabling intelligent food safety management by efficiently analyzing high-quality and nonlinear data. We systematically summarize recent advances in the application of ML and DL, focusing on key areas such as biotoxin detection, heavy metal contamination, analysis of pesticide and veterinary drug residues, and microbial risk prediction. While traditional algorithms including support vector machines and random forests demonstrate strong performance in classification and risk evaluation, unsupervised methods such as K-means and hierarchical cluster analysis facilitate pattern recognition in unlabeled datasets. Furthermore, novel DL architectures, such as convolutional neural networks, recurrent neural networks, and transformers, enable automated feature extraction and multimodal data integration, substantially improving detection accuracy and efficiency. In conclusion, we recommend future work to emphasize model interpretability, multi-modal data fusion, and integration into HACCP systems, thereby supporting intelligent, interpretable, and real-time food safety management.

Crop yield prediction is critical for agricultural decision making and food security. Traditional models struggle to capture the complex interactions among meteorological, soil, and agricultural factors. This study introduces Crossformer, a Transformer-based model with a Local Perception Unit (LPU) for local dependencies and a Cross-Window Attention Mechanism for global dependencies. Experiments on winter wheat, rice, and corn datasets show that Crossformer outperforms CNN, LSTM, and Transformer in Test Loss, R², MSE, and MAE. For instance, on the corn dataset, Crossformer achieves a Test Loss of 0.0271 and an R² of 0.9863, compared to 0.7999 and 0.1634 for LSTM, respectively, demonstrating a substantial improvement in predictive performance. Interpretability analysis highlights the importance of temperature and precipitation in yield prediction, aligning with agricultural insights. The results demonstrate Crossformer’s potential for precision agriculture.