Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

FASN plays a critical role in lipid metabolism, which is involved in regulating ovarian follicular development. However, the molecular mechanisms of how FASN regulate the function of ovarian follicular cells still remain elusive. In this study, by overexpression or interference of FASN in pre-hierarchical follicle granulosa cells (phGCs) and hierarchical follicle granulosa cells (hGCs), we analyzed their effects on the granulosa cell transcriptome and metabolome profiles using RNA-Seq and LC-MS/MS, respectively. The results showed that overexpression of FASN promoted proinflammatory factors expression by activating TLR3/IRF7 and TLR3/NF-κB pathways in phGCs, but only by activating TLR3/IRF7 pathways in hGCs. Then, necroptosis and apoptosis were triggered through the JAK/STAT1 pathway (induced by inflammatory factors) and BAK/caspase-7 pathway, respectively. The combined analysis of the metabolome and transcriptome revealed that FASN affected the demand of GCs for 5-hydroxytryptamine (5-HT) by activating the neuroactive ligand-receptor interaction pathway in two categorized GCs and only altering the metabolic pathway of tryptophan in phGCs, and ultimately participated in regulating the physiological function of geese GCs. Taken together, this study showed that the mechanisms of FASN regulating the physiological function of geese phGCs and hGCs were similar, but they also had some different characteristics.