Tea has become one of the most important economic crops globally, driven by the growing popularity of tea-based beverages. However, tea production is increasingly threatened by biotic stressors, among which Ectropis grisescens stands out as a major defoliating pest. The larvae of this moth species cause substantial damage to tea plants by feeding on their leaves, thereby reducing yield and affecting the overall quality of the crop. The manual methods are not only time-consuming and labor-intensive but also suffer from low efficiency, high costs, and considerable subjectivity. In this context, the development of intelligent, accurate, and automated early detection techniques for Ectropis grisescens larvae is of vital significance. Such advancements hold the potential to enhance pest management strategies, reduce economic losses, and promote sustainable tea cultivation practices.

The recognition framework was proposed to achieve real-time and fine-grained identification of E. grisescens larvae at four distinct instar stages within complex tea canopy environments. To capture the varying morphological characteristics across developmental stages, a hierarchical three-level detection system was designed, consisting of: (1) full-instar detection covering all instars from the 1st to the 4th, (2) grouped-stage detection that classified larvae into early (1st－2nd) and late (3rd－4th) instar stages, and (3) fine-grained detection targeting each individual instar stage separately. Given the challenges posed by limited, imbalanced, and noisy training data—common issues in field-based entomological image datasets— a semi-automated dataset optimization strategy was introduced to enhance data quality and improve class representation. Building upon this refined dataset, a controllable diffusion model was employed to generate a large number of high-resolution, labeled synthetic images that emulated real-world appearances of Ectropis grisescens larvae under diverse environmental conditions. To ensure the reliability and utility of the generated data, a novel high-quality image filtering strategy was developed that automatically evaluated and selected images containing accurate, detailed, and visually realistic larval instances. The filtered synthetic images were then strategically integrated into the real training dataset, effectively augmenting the data and enhancing the diversity and balance of training samples. This comprehensive data augmentation pipeline led to substantial improvements in the detection performance of multiple YOLO-series models (YOLOv8, YOLOv9, YOLOv10, and YOLOv11).

Experimental results clearly demonstrated that the YOLO series models exhibited strong and consistent performance across a range of detection tasks involving Ectropis grisescens larvae. In the full-instar detection task, which targeted the identification of all larval stages from 1st to 4th instars, the best-performing YOLO model achieved an impressive average mAP@50 of 0.904, indicating a high level of detection precision. In the grouped instar-stage detection task, where larvae were classified into early (1st–2nd) and late (3rd–4th) instar groups, the highest mAP@50 recorded was 0.862, reflecting the model's ability to distinguish developmental clusters with reasonable accuracy. For the more challenging fine-grained individual instar detection task-requiring the model to discriminate among each instar stage independently-the best mAP@50 reached 0.697, demonstrating the feasibility of detailed stage-level classification despite subtle morphological differences. The proposed semi-automated data optimization strategy contributed significantly to performance improvements, particularly for the YOLOv8 model. Specifically, YOLOv8 showed consistent gains in mAP@50 across all three detection tasks, with absolute improvements of 0.024, 0.027, and 0.022 for full-instar, grouped-stage, and fine-grained detection tasks, respectively. These enhancements underscored the effectiveness of the dataset refinement process in addressing issues related to data imbalance and noise. Furthermore, the incorporation of the controllable diffusion model led to a universal performance boost across all YOLO variants. Notably, YOLOv10 exhibited the most substantial gains among the evaluated models, with its average mAP@50 increasing from 0.811 to 0.821 across the three detection tasks. This improvement was statistically significant, as confirmed by a paired t-test (p < 0.05), suggesting that the synthetic images generated by the diffusion model effectively enriched the training data and improved model generalization. Among all evaluated models, YOLOv9 achieved the best overall performance in detecting Ectropis grisescens larvae. It attained top mAP@50 scores of 0.909, 0.869, and 0.702 in the full-instar, grouped-stage, and fine-grained detection tasks, respectively. When averaged across all tasks, YOLOv9 reached a mean mAP@50 of 0.826, accompanied by a macro F₁-Score of 0.767, highlighting its superior balance between precision and recall.

This study demonstrated that the integration of a controllable diffusion model with deep learning enabled accurate field-level instar detection of Ectropis grisescens, providing a reliable theoretical and technical foundation for intelligent pest monitoring in tea plantations.

Fu brick tea is a popular fermented black tea, and its "Jin hua" fermentation process determines the quality, flavor and function of the tea. Therefore, the establishment of a rapid and non-destructive detection method for the fungal fermentation stage is of great significance to improve the quality control and processing efficiency.

The variation trend of Fu brick tea was analyzed through the acquisition of visible-near-infrared (VIS-NIR) and near-infrared (NIR) hyperspectral images during the fermentation stage, and combined with the key quality indexes such as moisture, free amino acids, tea polyphenols, and tea pigments (including theaflavins, thearubigins, and theabrownines), the variation trend was analyzed. This study combined support vector machine (SVM) and convolutional neural network (CNN) to establish quantitative detection of key quality indicators and qualitative identification of the fungal fermentation stage. To enhance model performance, the squeeze-and-excitation (SE) attention mechanism was incorporated, which strengthens the adaptive weight adjustment of feature channels, resulting in the development of the Spectra-SE-CNN model. Additionally, t-distributed stochastic neighbor embedding (t-SNE) was used for feature dimensionality reduction, aiding in the visualization of feature distributions during the fermentation process. To improve the interpretability of the model, the Grad-CAM technique was employed for CNN and Spectra-SE-CNN visualization, helping to identify the key regions the model focuses on.

In the quantitative detection of Fu brick tea quality, the best models were all Spectra-SE-CNN, with R²p of 0.8595, 0.8525 and 0.8383 for moisture, tea pigments and tea polyphenols, respectively, indicating a high correlation and modeling stability. These values suggest that the models were capable of accurately predicting these key quality indicators based on hyperspectral data. However, the R²p for free amino acids was lower (0.6702), which could be attributed to their relatively minor changes during the fermentation process or a weak spectral response, making it more challenging to detect this component reliably with the current hyperspectral imaging approach. The Spectra-SE-CNN model significantly outperformed traditional CNN models, demonstrating the effectiveness of incorporating the SE attention mechanism. The SE attention mechanism enhanced the model's ability to extract and discriminate important spectral features, thereby improving both classification accuracy and generalization. This indicated that the Spectra-SE-CNN model excels not only in feature extraction but also in enhancing the model's robustness to variations in the fermentation stage. Furthermore, t-SNE revealed a clear separation of the different fungal fermentation stages in the low-dimensional space, with distinct boundaries. This visualization highlighted the model's ability to distinguish between subtle spectral differences during the fermentation process. The heatmap generated by Grad-CAM emphasized key regions, such as the fermentation location and edges, providing valuable insights into the specific features the model deemed important for accurate predictions. This improved the model's transparency and helped validate the spectral features that were most influential in identifying the fermentation stages.

A Spectra-SE-CNN model was proposed in this research, which incorporates the SE attention mechanism into a convolutional neural network to enhance spectral feature learning. This architecture adaptively recalibrates channel-wise feature responses, allowing the model to focus on informative spectral bands and suppress irrelevant signals. As a result, the Spectra-SE-CNN achieved improved classification accuracy and training efficiency compared to CNN models, demonstrating the strong potential of deep learning in hyperspectral spectral feature extraction. The findings validate Hyperspectral imaging technology (HIS) enables rapid, non-destructive, and high-resolution assessment of Fu brick tea during its critical fungal fermentation stage and the feasibility of integrating HSI with intelligent algorithms for real-time monitoring of the Fu brick tea fermentation process. Furthermore, this approach offers a pathway for broader applications of hyperspectral imaging and deep learning in intelligent agricultural product monitoring, quality control, and automation of traditional fermentation processes.