The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model’s receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm’s capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Behavior recognition in beef cattle is a crucial component of beef cattle behavior warning and intelligent farming. Traditional beef cattle behavior recognition faces challenges in both difficulty in identification and low accuracy. In this study, the YOLOv8n_BiF_DSC (Fusion of Dynamic Snake Convolution and BiFormer Attention) algorithm was employed for the non-intrusive recognition of beef cattle behavior. The specific steps are as follows: 45 beef cattle were observed using a fixed camera (A LINE OF DEFENSE) and a mobile phone (Huawei Mate20Pro) to collect and filter posture data, yielding usable videos ranging from 1 to 30 min in length. These videos cover nine different behaviors in various scenarios, including standing, lying, mounting, fighting, licking, eating, drinking, walking, and searching. After data augmentation, the dataset comprised 34,560 samples. The convolutional layer (CONV) was improved by introducing variable convolution and dynamic snake-like convolution modules. The dynamic snake-like convolution, which yielded the best results, expanded the model’s receptive field, dynamically perceived key features of beef cattle behavior, and enhanced the algorithm’s feature extraction capability. Attention mechanism modules, including SE (Squeeze-and-Excitation Networks), CBAM (Convolutional Block Attention Module), CA (Coordinate Attention), and BiFormer (Vision Transformer with Bi-Level Routing Attention), were introduced. The BiFormer attention mechanism, selected for its optimal performance, improved the algorithm’s ability to capture long-distance context dependencies. The model’s computational efficiency was enhanced through dynamic and query-aware perception. Experimental results indicated that YOLOv8n_BiF_DSC achieved the best results among all improved algorithms in terms of accuracy, average precision at IoU 50, and average precision at IoU 50:95. The accuracy of beef cattle behavior recognition reached 93.6%, with the average precision at IoU 50 and IoU 50:95 being 96.5% and 71.5%, respectively. This represents a 5.3%, 5.2%, and 7.1% improvement over the original YOLOv8n. Notably, the average accuracy of recognizing the lying posture of beef cattle reached 98.9%. In conclusion, the YOLOv8n_BiF_DSC algorithm demonstrates excellent performance in feature extraction and high-level data fusion, displaying high robustness and adaptability. It provides theoretical and practical support for the intelligent recognition and management of beef cattle.

Animal phenotyping recognition is a pivotal component of precision livestock management, holding significant importance for intelligent farming practices and animal welfare assurance. In recent years, with the rapid advancement of deep learning technologies, the YOLO algorithm—as the pioneering single-stage detection framework—has revolutionized the field of object detection through its efficient and rapid approach and has been widely applied across various agricultural domains. This review focuses on animal phenotyping as the research target structured around four key aspects: (1) the evolution of YOLO algorithms, (2) datasets and preprocessing methodologies, (3) application domains of YOLO algorithms, and (4) future directions. This paper aims to offer readers fresh perspectives and insights into animal phenotyping research.

Identifying chicken breast freshness is an important component of poultry food safety. Traditional methods for chicken breast freshness recognition suffer from issues such as high cost, difficulty in recognition, and low efficiency. In this study, the YOLOv8n_CA_DSC3 algorithm is employed for non-destructive recognition of chicken breast freshness. Specifically, chicken breast samples under different lighting intensities, densities, sampling angles, etc., were collected. Based on the total microbial count (TAC), coliform count (ANC), and pH value of the samples, the freshness of chicken breast is classified into 7 levels: fresh meat, slightly fresh meat 1, slightly fresh meat 2, slightly fresh meat 3, spoiled meat 1, spoiled meat 2, and spoiled meat 3. The dataset was augmented with eight types of data enhancement, resulting in 34,380 samples. The CONV convolutional layers were replaced with the deformable convolution DCNv3 modules to improve network efficiency and key feature extraction of chicken breast through long-range dependencies, adaptive spatial aggregation, and sparse sampling, thereby enhancing algorithm generalization performance. The introduction of the CA attention mechanism module enhances feature fusion between multiple channels and long-distance high-level and low-level data dependencies. Experimental results show that in the improved algorithm, YOLOv8n_CA_DSC3 achieves suboptimal recall rate but optimal precision, average precision at IoU = 0.5, and average precision at IoU = 0.5:0.95. The accuracy of chicken breast freshness recognition is 95.6%, average precision at IoU = 0.5 is 97.5%, and average precision at IoU = 0.5:0.95 is 77.5%, representing improvements of 5.3%, 5.1%, and 6.1%, respectively, compared to the original YOLOv8n. In conclusion, the YOLOv8n_CA_DSC3 algorithm demonstrates good performance in feature extraction and integration of upper and lower layer information for chicken breast freshness, exhibiting high robustness and providing technical support for non-destructive recognition of chicken breast freshness and food safety.

Although blood lead levels (BLLs) in children are gradually decreasing, low-concentration lead exposure can still exert adverse effects. We studied the factors that affect BLLs in children in Shenyang, China. We conducted a cross-sectional study by administering structured questionnaires on family demographics and food intake. The concentrations of lead in venous blood were determined by graphite furnace atomic absorption spectrometry. A total of 273 children aged 1–6 years were enrolled. The geometric mean (geometric standard deviation) of BLLs was 24.94 (12.70) μg/L in boys and 23.75 (11.34) μg/L in girls. The prevalence of BLLs of ≥35 μg/L was 22.7% and was mainly observed in children aged under 3 years. Often hand washing before meals was protective against BLLs ≥20 μg/L (adjusted OR: 0.427, 95%CI: 0.238–0.767, p = 0.004). Consumption of puffed grains and eggs had an adjusted OR (95%CI) for BLLs ≥20 μg/L of 1.714 (1.012–2.901) (p = 0.045) and 1.787 (1.000–3.192) (p = 0.050), respectively. BLLs of children in Shenyang are still higher than in developed countries. Consumption of puffed grains and eggs is associated with higher BLLs. Often hand washing before meals may be protective against high BLLs.

Pig vocalization recognition can support non-invasive monitoring in precision livestock farming, but labelled pig-sound recordings are often limited for specific behaviours or physiological states. Under few-shot conditions, deep models may overfit, whereas traditional acoustic features may not fully describe class-specific time-frequency patterns. This study proposed PSA-AP, a pig-sound adaptation pipeline that uses log-Mel spectrograms and integrates SpecAugment-based domain expansion, ImageNet-pretrained ResNet18 knowledge transfer, and ArcFace-based feature alignment. The method was designed to reduce dependence on limited labelled samples, improve task-adapted representation learning, and enhance inter-class separability in the embedding space. Experiments were conducted on a five-class few-shot pig vocalization classification task, including eat, estrous, farrowing (fap), howl, and oink sounds collected from 10 adult Landrace pigs. Using $K=\{5,10,15,20,25,30\}$ labelled wav files per class and five random seeds, each selected training wav file and each held-out test wav file was converted into one 1.0 s log-Mel spectrogram for model training or evaluation. Final evaluation was based on the last checkpoint of each training run. PSA-AP achieved the best mean Accuracy, Macro-F1, and UAR at every K-shot setting. At $K=30$, PSA-AP reached 90.60% Accuracy, 90.49% Macro-F1, and 90.60% UAR, exceeding Raw by 7.80, 7.82, and 7.80 percentage points, respectively. These results indicate that the proposed integration of domain expansion, knowledge transfer, and feature alignment provides a feasible supervised adaptation strategy for few-shot pig vocalization recognition within the current protocol.