Paddy rice grain variety classification is essential for quality control, as different rice varieties exhibit significant variations in quality attributes, affecting both food security and market value. The integration of hyperspectral imaging with machine learning presents a promising approach for precise classification, though challenges remain in managing the high dimensionality and variability of spectral data, along with the need for model interpretability. To address these challenges, this study employs a CNN-Transformer model that incorporates Standard Normal Variate (SNV) preprocessing, Competitive Adaptive Reweighted Sampling (CARS) for feature wavelength selection, and interpretability analysis to optimize the classification of hybrid indica paddy rice grain varieties. The results show that the CNN-Transformer model outperforms baseline models, achieving an accuracy of 95.33% and an F1-score of 95.40%. Interpretability analysis reveals that the model’s ability to learn from key wavelength features is significantly stronger than that of the comparison models. The key spectral bands identified for hybrid indica paddy rice grain variety classification lie within the 400–440 nm, 580–700 nm, and 880–960 nm ranges. This study demonstrates the potential of hyperspectral imaging combined with machine learning to advance rice variety classification, providing a powerful and interpretable tool for automated rice quality control in agricultural practices.

TSSC is one of the most important factors affecting loquat flavor, consumer satisfaction, and market competitiveness. To improve the ability to assess the TSSC of loquats, a method leveraging near-infrared spectroscopy and explainable artificial intelligence was proposed. The 900–1700 nm near-infrared spectroscopy of 156 fresh loquat samples was collected and preprocessed using seven preprocessing techniques, significant wavelength extraction utilizing six feature methods to eliminate data redundancy. Linear and nonlinear models were employed to establish the relationship between the feature spectrum and TSSC, with a focus on comparing and analyzing prediction performance. The findings reveal that the combination of 26 spectral bands selected by SPA and the PLSR model yielded the best prediction outcomes (R = 0.9031, RMSEP = 0.6171, RPD = 2.2803). The contribution of key wavelengths can be obtained by SHAP, which explains differences in model prediction accuracy and provides a reference for the application of loquat TSSC determination.

Rhizoma Atractylodis macrocephalae (RAM) is a renowned food–medicine homologous herb in China, the quality and efficacy of which are inherently linked to its geographical origin. However, traditional origin identification methods for RAM are time-consuming, laborious, and destructive. This study introduces an innovative framework integrating hyperspectral imaging (HSI), broad learning system (BLS), and SHapley Additive exPlanations (SHAP) for RAM origin identification. RAM samples were collected from three origins, 100 samples from per origin, and imaged using a visible and short-wave near-infrared HSI system. BLS was used to build identification models with full and important wavelengths, and compared against seven traditional algorithms, including K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), gradient boosting decision tree, (GBDT), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Additionally, SHAP was used to enhance interpretability and identify important wavelengths highly correlated with RAM origin. Results showed that the full-wavelength BLS model achieved a test accuracy of 95.56%, which outperformed other models including KNN (77.78%), RF (85.56%), GBDT (88.89%), AdaBoost (90.00%), BPNN (91.11%), XGBoost (92.22%), and SVM (94.44%). SHAP identified important wavelengths similar to traditional methods (competitive adaptive reweighted sampling and successive projections algorithm), and the BLS model using SHAP-selected top 25 wavelengths achieved 94.44% accuracy with minimal performance loss. This study not only provides a rapid and accurate approach for RAM origin identification but also establishes a promising data-driven paradigm for non-destructive geographical origin traceability of other traditional Chinese medicines.

Microwave imaging is emerging as an alternative to conventional medical diagnostic techniques. Traditional analytical and numerical methods fail to adequately address these fundamental challenges: they often rely on strict linear approximations or simplified physical models, leading to low reconstruction accuracy, poor robustness, and limited generalization ability in complex clinical scenarios. As a result, they cannot meet the high-precision requirements of practical stroke microwave imaging. To further improve the accuracy of microwave imaging algorithms in recognizing stroke regions and solving the backscattering problem, this study employs a combination of methods with deep learning. It presents the Multi-Scale Attention Network (MSA-Net) for microwave imaging. The network is based on the EGE-UNet network structure with improved multi-axis Hadamard attention, incorporating null-space pyramid pooling and introducing a deep supervisory mechanism to improve the network performance further. To combine microwave imaging with deep learning, firstly, a large amount of microwave data need to be simulated with HFSS, in which the simulation model is a human brain stroke model constructed by an HFSS simulation system. Secondly, the microwave data obtained from the simulation are converted into a tensor format. Then, the tensor data are input into the MSA-Net neural network, which generates a binary mask image that can be used to detect the size and location of the stroke. This study also prompts the model to converge faster by sparsifying the microwave data to improve training efficiency. The method has been tested using simulation data, and based on the comparison experiments with other networks, MSA-Net is more accurate in detecting the location and the bleed size. The experimental results show that the proposed method is superior for stroke imaging. The experimental results show that the proposed model achieves a 1.08 improvement in peak signal-to-noise ratio and a 0.017 reduction in learned perceptual image block similarity, fully validating the effectiveness of the structural optimization strategy proposed in this paper.