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.

Chinese Herbal Medicine (CHM) classification is a promising research issue in Intelligent Medicine. However, the small available Chinese Herbal datasets and the traditional CHM classification model lead to huge challenge for obtaining the promising classification results. To tackle the above challenges, a novel large CHM classification (CHMC) dataset has been firstly established, which includes 100 classes with about 10,000 samples. This dataset contains a wide range of medicinal materials and natural background. Further, the promising EfficientNetB4 model is proposed to perform the CHM classification. EfficientNet can uniformly scales up the depth, width and resolution of the model, which will obtain better accuracy as it balance all dimensions of the network, including depth, width, and resolution, respectively. To validate the superiority of the EfficientNet and the effectiveness of CHMC dataset, extensive experiments have been conducted, verifying that the EfficientNetB4 is optimal for CHM classification, with 5% improvement of the existing model. In addition, this model has achieved state-of-the art CHM classification performance, with TOP-1 accuracy of 83.1%, and TOP-5 accuracy of 92.50%.

Pork is the most widely consumed meat product in the world, and achieving accurate detection of individual pigs is of great significance for intelligent pig breeding and health monitoring. Improved pig detection has important implications for improving pork production and quality, as well as economics. However, most of the current approaches are based on manual labor, resulting in unfeasible performance. In order to improve the efficiency and effectiveness of individual pig detection, this paper describes the development of an attention module enhanced YOLOv3-SC model (YOLOv3-SPP-CBAM. SPP denotes the Spatial Pyramid Pooling module and CBAM indicates the Convolutional Block Attention Module). Specifically, leveraging the attention module, the network will extract much richer feature information, leading the improved performance. Furthermore, by integrating the SPP structured network, multi-scale feature fusion can be achieved, which makes the network more robust. On the constructed dataset of 4019 samples, the experimental results showed that the YOLOv3-SC network achieved 99.24% mAP in identifying individual pigs with a detection time of 16 ms. Compared with the other popular four models, including YOLOv1, YOLOv2, Faster-RCNN, and YOLOv3, the mAP of pig identification was improved by 2.31%, 1.44%, 1.28%, and 0.61%, respectively. The YOLOv3-SC proposed in this paper can achieve accurate individual detection of pigs. Consequently, this novel proposed model can be employed for the rapid detection of individual pigs on farms, and provides new ideas for individual pig detection.

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.

Chinese herbal medicine classification is an important research task in intelligent medicine, which has been applied widely in the fields of smart medicinal material sorting and medicinal material recommendation. However, most current mainstream methods are semi-automatic, with low efficiency and poor performance. To tackle this problem, a novel Chinese herbal medicine classification method based on mutual learning has been proposed. Specifically, two small student networks are designed for collaborative learning, and each of them collects knowledge learned from the other one respectively. Consequently, student networks obtain rich and reliable features, which will further improve the performance of Chinese herbal medicinal classification. In order to validate the performance of the proposed model, a dataset with 100 Chinese herbal classes (about 10,000 samples) was utilized and extensive experiments were performed. Experimental results verify that the proposed method is superior to those of the latest models with equivalent or even fewer parameters, specifically, obtaining 3∼5.4% higher accuracy rate and 13∼37% lower loss. Moreover, the mutual learning model achieves 80.8% Chinese herbal medicine classification accuracy.

Cross-modal bird image–audio mutual generation has appealing potential benefits for bird classification. To achieve promising cross-modal bird visual–audio mutual generation, we propose an attention enhanced cross-modal cycle adversarial generation network. Specifically, the attention module endows our model with long-term intra-modality dependency and inter-modality dependency capabilities, which can provide more information during the generation process and further improve the generation performance. Moreover, because there was no dataset concerning bird visual–audio mutual generation, the authors established a novel bird cross-modal generation dataset, called Bird_Crossmodal_Generation (BCG). Based on BCG, our model obtains promising performance and achieves significant improvement under both inception score and Frechet inception distance criteria. The experimental results validate the feasibility of the proposed task and the superiority of our model. Additionally, this investigation provides a basis for more researchers to develop cross-modality methods for bird visual–audio generation.