Tea polyphenols (TPs) are a critical indicator for evaluating the quality of tea leaves and are esteemed for their beneficial effects. The non-destructive detection of this component is essential for enhancing precise control in tea production and improving product quality. This study developed an enhanced PKO-SVR (support vector regression based on the Pied Kingfisher Optimization Algorithm) model for rapidly and accurately detecting tea polyphenol content in Fu brick tea using hyperspectral reflectance data. During this experiment, chemical analysis determined the tea polyphenol content, while hyperspectral imaging captured the spectral data. Data preprocessing techniques were applied to reduce noise interference and improve the prediction model. Additionally, several other models, including K-nearest neighbor (KNN) regression, neural network regression (BP), support vector regression based on the sparrow algorithm (SSA-SVR), and support vector regression based on particle swarm optimization (PSO-SVR), were established for comparison. The experiment results demonstrated that the improved PKO-SVR model excelled in predicting the polyphenol content of Fu brick tea (R² = 0.9152, RMSE = 0.5876, RPD = 3.4345 for the test set) and also exhibited a faster convergence rate. Therefore, the hyperspectral data combined with the PKO-SVR algorithm presented in this study proved effective for evaluating Fu brick tea’s polyphenol content.

Detecting rice leaf diseases in real-world field environments remains challenging due to varying lesion sizes, diverse lesion morphologies, complex backgrounds, and the limited availability of high-quality annotated datasets. Existing detection models often suffer from performance degradation under these conditions, particularly when training data lack sufficient diversity and structural realism. To address these challenges, this paper proposes a Latent Diffusion Inpainting (LDI) data augmentation method and an improved lightweight detection model, LGH-YOLOv12n. Unlike conventional diffusion-based augmentation methods that generate full images or random patches, LDI performs category-aware latent inpainting, synthesizing realistic lesion patterns by jointly conditioning on background context and disease categories, thereby enhancing data diversity while preserving scene consistency. Furthermore, LGH-YOLOv12n improves upon the YOLOv12n baseline by introducing GSConv in the backbone to reduce channel redundancy and enhance lesion localization, and integrating Hierarchical Multi-head Attention (HMHA) into the neck network to better distinguish disease features from complex field backgrounds. Experimental results demonstrate that LGH-YOLOv12n achieves an F1 of 86.1% and an mAP@50 of 88.3%, outperforming the YOLOv12n model trained without data augmentation by 3.3% and 5.0%, respectively. Moreover, when trained on the LDI-augmented dataset, LGH-YOLOv12n consistently outperforms YOLOv8n, YOLOv10n, YOLOv11n, and YOLOv12n, with mAP@50 improvements of 4.6%, 5.2%, 1.9%, and 2.1%, respectively. These results indicate that the proposed LDI augmentation and LGH-YOLOv12n model provide an effective and robust solution for rice leaf disease detection in complex field environments.