Accurate cattle body detection is crucial for precision livestock farming. However, traditional cattle body detection methods rely on manual observation, which is both time-consuming and labor-intensive. Moreover, computer-vision-based methods suffer prolonged training times and training difficulties. To address these issues, this paper proposes a novel YOLOv5-EMA model for accurate cattle body detection. By incorporating the Efficient Multi-Scale Attention (EMA) module into the backbone of YOLO series detection models, the performance of detecting smaller targets, such as heads and legs, has been significantly improved. The Efficient Multi-Scale Attention (EMA) module utilizes the large receptive fields of parallel sub-networks to gather multi-scale spatial information and establishes mutual dependencies between different spatial positions, enabling cross-spatial learning. This enhancement empowers the model to gather and integrate more comprehensive feature information, thereby improving the effectiveness of cattle body detection. The experimental results confirm the good performance of the YOLOv5-EMA model, showcasing promising results across all quantitative evaluation metrics, qualitative detection findings, and visualized Grad-CAM heatmaps. To be specific, the YOLOv5-EMA model achieves an average precision (mAP@0.5) of 95.1% in cattle body detection, 94.8% in individual cattle detection, 94.8% in leg detection, and 95.5% in head detection. Moreover, this model facilitates the efficient and precise detection of individual cattle and essential body parts in complex scenarios, especially when dealing with small targets and occlusions, significantly advancing the field of precision livestock farming.

This study underscores the paramount importance of facial expressions in pigs, serving as a sophisticated mode of communication to gauge their emotions, physical well-being, and intentions. Given the inherent challenges in deciphering such expressions due to pigs' rudimentary facial muscle structure, we introduced an avant-garde pig facial expression recognition model named CReToNeXt-YOLOv5. The proposed model encompasses several refinements tailored for heightened accuracy and adeptness in detection. Primarily, the transition from the CIOU to the EIOU loss function optimized the training dynamics, leading to precision-driven regression outcomes. Furthermore, the incorporation of the Coordinate Attention mechanism accentuated the model's sensitivity to intricate expression features. A significant innovation was the integration of the CReToNeXt module, fortifying the model's prowess in discerning nuanced expressions. Efficacy trials revealed that CReToNeXt-YOLOv5 clinched a mean average precision (mAP) of 89.4%, marking a substantial enhancement by 6.7% relative to the foundational YOLOv5. Crucially, this advancement holds profound implications for animal welfare monitoring and research, as our findings underscore the model's capacity to revolutionize the accuracy of pig facial expression recognition, paving the way for more humane and informed livestock management practices.

To explore the effects of pulsed electric field treatment on the germination of Scutellaria baicalensis seeds and the growth of seedlings, this study used the response surface methodology to design the working parameters of the pulsed electric field and treated and cultured Scutellaria baicalensis seeds. The results showed that the pulsed electric field treatment was beneficial for the germination of Scutellaria baicalensis seeds, improving the metabolic activity and stress resistance of seedlings. When the pulsed electric field treatment’s parameters were 0.5 kV·cm⁻¹, 120 μs, and 99 pulses, the germination potential of seeds was significantly increased by 29.25% and the germination index significantly increased by 20.65%, compared to the control. From 5th to 15th day, the activities of SOD, POD, and α-amylase in the seedlings, and the contents of Pro, soluble sugars, and soluble proteins were significantly increased, compared to the control. This study provides a theoretical basis for improving the germination and seedling growth of medicinal herbs such as Scutellaria baicalensis and their practical application in production.

The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.