Rice disease identification plays a critical role in ensuring yield stability, enabling precise prevention and control, and promoting agricultural intelligence. However, existing approaches rely heavily on manual inspection, which is labor-intensive and inefficient. Moreover, the significant variability in disease features poses further challenges to accurate recognition. To address these issues, this paper proposes a novel rice leaf disease detection model—RLDD-YOLOv11n. First, the improved RLDD-YOLOv11n integrates the SCSABlock residual attention module into the neck layer to enhance multi-semantic information fusion, thereby improving the detection capability for small disease targets. Second, recognizing the limitations of the native upsampling module in YOLOv11n in reconstructing rice-disease-related features, the CARAFE upsampling module is incorporated. Finally, a rice leaf disease dataset focusing on three common diseases—Bacterial Blight, Rice Blast, and Brown Spot—was constructed. The experimental results demonstrate the effectiveness of the proposed improvements. RLDD-YOLOv11n achieved a mean Average Precision (mAP) of 88.3%, representing a 2.8% improvement over the baseline model. Furthermore, compared with existing mainstream lightweight YOLO models, RLDD-YOLOv11n exhibits a superior detection performance and robustness.

This study aimed to investigate the aroma effects of key volatile compounds in a new type of mulberry leaf Fu brick teas (MTs) and traditional Fu brick teas (FTs). Headspace solid–phase microextraction (HS-SPME), gas chromatography–mass spectrometry (GC-MS), sensory evaluation, and chemometrics were used to determine the differences in key flavour qualities between the two. The results showed that a total of 139 volatile components were identified, with aldehydes, ketones, and alcohols dominating. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) combined with the odour activity value (OAV) showed that seven aroma compounds had an OAV > 10, including 2-(4-methylcyclohex-3-en-1-yl) propan-2-ol with floral and fruity aroma and green attributes, 6-methylhept-5-en-2-one, (E)-6,10-dimethyl-5,9-Undecadien-2-one, (3E,5E)-octa-3,5-dien-2-one, Benzaldehyde, and (E)-3,7,11,15-tetramethylhexadec-2-en-1-ol, which were more abundant in MTs than FTs; Cedrol with sweet aroma attributes was more consistent in MTs than FTs, and we suggest that these odour compounds are important aroma contributors to MTs. Taken together, these findings will provide new insights into the mechanism of formation of the characteristic attributes of aroma in MTs.

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.

The number of rice seedlings and their spatial distribution are the main agronomic components for determining rice yield. However, the above agronomic information is manually obtained through visual inspection, which is not only labor-intensive and time-consuming but also low in accuracy. To address these issues, this paper proposes RS-P2PNet, which automatically counts and locates rice seedlings through point supervision. Specifically, RS-P2PNet first adopts Resnet as its backbone and introduces mixed local channel attention (MLCA) in each stage. This allows the model to pay attention to the task-related feature in the spatial and channel dimensions and avoid interference from the background. In addition, a multi-scale feature fusion module (MSFF) is proposed by adding different levels of features from the backbone. It combines the shallow details and high-order semantic information of rice seedlings, which can improve the positioning accuracy of the model. Finally, two rice seedling datasets, UERD15 and UERD25, with different resolutions, are constructed to verify the performance of RS-P2PNet. The experimental results show that the MAE values of RS-P2PNet reach 1.60 and 2.43 in the counting task, and compared to P2PNet, they are reduced by 30.43% and 9.32%, respectively. In the localization task, the Recall rates of RS-P2PNet reach 97.50% and 96.67%, exceeding those of P2PNet by 1.55% and 1.17%, respectively. Therefore, RS-P2PNet has effectively accomplished the counting and localization of rice seedlings. In addition, the MAE and RMSE of RS-P2PNet on the public dataset DRPD reach 1.7 and 2.2, respectively, demonstrating good generalization.

Rice leaf diseases are critical factors affecting rice yield and quality, and their effective detection is crucial for ensuring stable production. However, existing detection models exhibit limitations in capturing irregular and fine-grained lesion features and are susceptible to interference from complex backgrounds. To address these challenges, this study proposes an Efficient Attention-Guided Network (EAG-YOLOv11n) for rice leaf disease detection. Specifically, an EMA-C3K2 module is proposed to enhance the network’s feature extraction capability. It integrates Efficient Multi-Scale Attention (EMA) into the shallow C3K2 layers, enabling the network to extract richer low-level feature representations. In addition, a Global Local Complementary Attention module (GLC-PSA) is proposed, which integrates a Local Importance Attention (LIA) branch to enhance the local feature representation of the original C2PSA. This design strengthens the perception of lesion regions while effectively suppressing background interference. Furthermore, an Adaptive Threshold Focal Loss (ATFL) is employed to guide the optimization of model parameters during training, alleviating sample imbalance and adaptively emphasizing the learning of challenging samples. Experimental results demonstrate that EAG-YOLOv11n achieves a mean Average Precision (mAP) of 87.3%, representing a 2.7% improvement over the baseline. Furthermore, compared with existing mainstream detection methods, including RT-DETR-L, YOLOv7tiny, YOLOv8n, YOLOv9tiny, YOLOv10n, and YOLOv12n, EAG-YOLOv11n improves mAP by 4.2%, 4.3%, 1.9%, 1.1%, 8.7%, and 2.9%, respectively. Overall, these results highlight its superior effectiveness in rice leaf disease detection, providing reliable technical support for stable yield and sustainable agricultural development.