Search Results (34)

Daily behavioral analysis of group-housed pigs provides critical insights into early warning systems for pig health issues and animal welfare in smart pig farming. In this study, our main objective was to develop an automated method for monitoring and analyzing the behavior of group-reared pigs to detect health problems and improve animal welfare promptly. We have developed the method named Pig-ByteTrack. Our approach addresses target detection, Multi-Object Tracking (MOT), and behavioral time computation for each pig. The YOLOX-X detection model is employed for pig detection and behavior recognition, followed by Pig-ByteTrack for tracking behavioral information. In 1 min videos, the Pig-ByteTrack algorithm achieved Higher Order Tracking Accuracy (HOTA) of 72.9%, Multi-Object Tracking Accuracy (MOTA) of 91.7%, identification F1 Score (IDF1) of 89.0%, and ID switches (IDs) of 41. Compared with ByteTrack and TransTrack, the Pig-ByteTrack achieved significant improvements in HOTA, IDF1, MOTA, and IDs. In 10 min videos, the Pig-ByteTrack achieved the results with 59.3% of HOTA, 89.6% of MOTA, 53.0% of IDF1, and 198 of IDs, respectively. Experiments on video datasets demonstrate the method’s efficacy in behavior recognition and tracking, offering technical support for health and welfare monitoring of pig herds. Full article

While the pig industry is crucial in global meat consumption, accounting for 34% of total consumption, respiratory diseases in pigs can cause substantial economic losses to pig farms. To alleviate this issue, we propose an advanced audio-visual monitoring system for the early detection of coughing, a key symptom of respiratory diseases in pigs, that will enhance disease management and animal welfare. The proposed system is structured into three key modules: the cough sound detection (CSD) module, which detects coughing sounds using audio data; the pig object detection (POD) module, which identifies individual pigs in video footage; and the coughing pig detection (CPD) module, which pinpoints which pigs are coughing among the detected pigs. These modules, using a multimodal approach, detect coughs from continuous audio streams amidst background noise and accurately pinpoint specific pens or individual pigs as the source. This method enables continuous 24/7 monitoring, leading to efficient action and reduced human labor stress. It achieved a substantial detection accuracy of 0.95 on practical data, validating its feasibility and applicability. The potential to enhance farm management and animal welfare is shown through proposed early disease detection. Full article

Thoracostomy and chest tube placement are key procedures in treating pleural diseases involving the accumulation of fluids (e.g., malignant effusions, serous fluid, pus, or blood) or air (pneumothorax) in the pleural cavity. Initially described by Hippocrates and refined through the centuries, chest drainage achieved a historical milestone in the 19th century with the creation of closed drainage systems to prevent the entry of air into the pleural space and reduce infection risk. The introduction of plastic materials and the Heimlich valve further revolutionized chest tube design and function. Technological advancements led to the availability of various chest tube designs (straight, angled, and pig-tail) and drainage systems, including PVC and silicone tubes with radiopaque stripes for better radiological visualization. Modern chest drainage units can incorporate smart digital systems that monitor and graphically report pleural pressure and evacuated fluid/air, improving patient outcomes. Suction application via wall systems or portable digital devices enhances drainage efficacy, although careful regulation is needed to avoid complications such as re-expansion pulmonary edema or prolonged air leak. To prevent recurrent effusion, particularly due to malignancy, pleurodesis agents can be applied through the chest tube. In cases of non-expandable lung, maintaining a long-term chest drain may be the most appropriate approach and procedures such as the placement of an indwelling pleural catheter can significantly improve quality of life. Continued innovations and rigorous training ensure that chest tube insertion remains a cornerstone of effective pleural disease management. This review provides a comprehensive overview of the historical evolution and modern advancements in pleural drainage. By addressing both current technologies and procedural outcomes, it serves as a valuable resource for healthcare professionals aiming to optimize pleural disease management and patient care. Full article

Smart farming technologies to track and analyze pig behaviors in natural environments are critical for monitoring the health status and welfare of pigs. This study aimed to develop a robust multi-object tracking (MOT) approach named YOLOv8 + OC-SORT(V8-Sort) for the automatic monitoring of the different behaviors of group-housed pigs. We addressed common challenges such as variable lighting, occlusion, and clustering between pigs, which often lead to significant errors in long-term behavioral monitoring. Our approach offers a reliable solution for real-time behavior tracking, contributing to improved health and welfare management in smart farming systems. First, the YOLOv8 is employed for the real-time detection and behavior classification of pigs under variable light and occlusion scenes. Second, the OC-SORT is utilized to track each pig to reduce the impact of pigs clustering together and occlusion on tracking. And, when a target is lost during tracking, the OC-SORT can recover the lost trajectory and re-track the target. Finally, to implement the automatic long-time monitoring of behaviors for each pig, we created an automatic behavior analysis algorithm that integrates the behavioral information from detection and the tracking results from OC-SORT. On the one-minute video datasets for pig tracking, the proposed MOT method outperforms JDE, Trackformer, and TransTrack, achieving the highest HOTA, MOTA, and IDF1 scores of 82.0%, 96.3%, and 96.8%, respectively. And, it achieved scores of 69.0% for HOTA, 99.7% for MOTA, and 75.1% for IDF1 on sixty-minute video datasets. In terms of pig behavior analysis, the proposed automatic behavior analysis algorithm can record the duration of four types of behaviors for each pig in each pen based on behavior classification and ID information to represent the pigs’ health status and welfare. These results demonstrate that the proposed method exhibits excellent performance in behavior recognition and tracking, providing technical support for prompt anomaly detection and health status monitoring for pig farming managers. Full article

African Swine Fever (ASF) is a severe viral disease that has significantly impacted the pig farming industry in China. It first broke out in China in 2018 and quickly spread to multiple provinces, significantly affecting the production efficiency of the pig farming industry. This study utilized pig production data from 17 provinces in China from 2010 to 2022 and applied the Malmquist production efficiency index and panel regression methods to assess the impact of the ASF epidemic on the efficiency of the pig farming industry. The results indicated that the outbreak of ASF significantly reduced overall production efficiency, which magnified the vulnerabilities of the production system. Although there was a general decline in technological change and pure technical efficiency, the increase in scale efficiency suggested effective resource optimization by farmers under resource-constrained conditions. In light of these findings, it is recommended to strengthen biosecurity education and epidemic prevention measures in the pig farming industry and to enhance technological innovation and the application of smart technologies to improve production efficiency and disease response capabilities. Additionally, timely adjustments in farming scale and resource optimization will be key to addressing future challenges. Through these strategies, the pig farming industry can maintain stable production efficiency during future epidemics and push towards a more efficient and refined production model. Full article

Since pig vocalization is an important indicator of monitoring pig conditions, pig vocalization detection and recognition using deep learning play a crucial role in the management and welfare of modern pig livestock farming. However, collecting pig sound data for deep learning model training takes time and effort. Acknowledging the challenges of collecting pig sound data for model training, this study introduces a deep convolutional neural network (DCNN) architecture for pig vocalization and non-vocalization classification with a real pig farm dataset. Various audio feature extraction methods were evaluated individually to compare the performance differences, including Mel-frequency cepstral coefficients (MFCC), Mel-spectrogram, Chroma, and Tonnetz. This study proposes a novel feature extraction method called Mixed-MMCT to improve the classification accuracy by integrating MFCC, Mel-spectrogram, Chroma, and Tonnetz features. These feature extraction methods were applied to extract relevant features from the pig sound dataset for input into a deep learning network. For the experiment, three datasets were collected from three actual pig farms: Nias, Gimje, and Jeongeup. Each dataset consists of 4000 WAV files (2000 pig vocalization and 2000 pig non-vocalization) with a duration of three seconds. Various audio data augmentation techniques are utilized in the training set to improve the model performance and generalization, including pitch-shifting, time-shifting, time-stretching, and background-noising. In this study, the performance of the predictive deep learning model was assessed using the k-fold cross-validation (k = 5) technique on each dataset. By conducting rigorous experiments, Mixed-MMCT showed superior accuracy on Nias, Gimje, and Jeongeup, with rates of 99.50%, 99.56%, and 99.67%, respectively. Robustness experiments were performed to prove the effectiveness of the model by using two farm datasets as a training set and a farm as a testing set. The average performance of the Mixed-MMCT in terms of accuracy, precision, recall, and F1-score reached rates of 95.67%, 96.25%, 95.68%, and 95.96%, respectively. All results demonstrate that the proposed Mixed-MMCT feature extraction method outperforms other methods regarding pig vocalization and non-vocalization classification in real pig livestock farming. Full article

Vietnam heavily relies on pork as its primary source of animal protein. Traditional farming methods, characterized by small-scale operations, dominate the industry. However, challenges such as rising feed costs, disease outbreaks, and market volatility are prompting many farmers to abandon their businesses. Recognizing the pivotal role of the swine sector in both economic development and nutrition, authorities must intervene to prevent its collapse. In developed nations, smart pig farming, utilizing technologies like sensors and cameras for data collection and real-time decision-making, has significantly improved health and productivity. These technologies can detect subtle indicators of animal well-being, enabling prompt intervention. This review aims to analyze the drivers of Vietnam’s swine farming, identify existing production system flaws, and explore innovative precision farming methods worldwide. Embracing precision farming promises to enhance Vietnam’s competitiveness in export markets and bolster consumer confidence. However, reliance solely on expensive foreign technologies may benefit large-scale farms, leaving smaller ones behind. Therefore, fostering local innovation and validating cost-effective solutions will be crucial for the sustainable growth of small- and medium-scale pig farming in Vietnam. Full article

This paper presents a deep-learning-based system for classifying pig postures, aiming to improve the management of sustainable smart pigsties. The classification of pig postures is a crucial concern for researchers investigating pigsty environments and for on-site pigsty managers. To address this issue, we developed a comprehensive system framework for pig posture classification within a pigsty. We collected image datasets from an open data sharing site operated by a public organization and systematically conducted the following steps: object detection, data labeling, image preprocessing, model development, and training. These processes were carried out using the acquired datasets to ensure comprehensive and effective training for our pig posture classification system. Subsequently, we analyzed and discussed the classification results using techniques such as Grad-CAM. As a result of visual analysis through Grad-CAM, it is possible to identify image features when posture is correctly classified or misclassified in a pig image. By referring to these results, it is expected that the accuracy of pig posture classification can be further improved. Through this analysis and discussion, we can identify which features of pig postures in images need to be emphasized to improve the accuracy of pig posture classification. The findings of this study are anticipated to significantly improve the accuracy of pig posture classification. In practical applications, the proposed pig posture classification system holds the potential to promptly detect abnormal situations in pigsties, leading to prompt responses. Ultimately, this can greatly contribute to increased productivity in pigsty operations, fostering efficiency enhancements in pigsty management. Full article

Identifying individual pigs is crucial for efficient breeding, health management, and disease control in modern farming. Traditional animal face identification methods are labor-intensive and prone to inaccuracies, while existing CNN-based pig face recognition models often struggle with high computational demands, large sizes, and reliance on extensive labeled data, which limit their practical application. This paper addresses these challenges by proposing a novel, decoupled approach to pig face recognition that separates detection from identification. This strategy employs a detection model as a pre-processing step, significantly reducing the need for extensive re-annotation for new datasets. Additionally, the paper introduces a method that integrates offline knowledge distillation with a lightweight pig face recognition model, aiming to build an efficient and embedding-friendly system. To achieve these objectives, the study constructs a small-scale, high-quality pig face detection dataset consisting of 1500 annotated images from a selection of 20 pigs. An independent detection model, trained on this dataset, then autonomously generates a large-scale pig face recognition dataset with 56 pig classes. In the face recognition stage, a robust teacher model guides the student model through a distillation process informed by a knowledge distillation loss, enabling the student model to learn relational features from the teacher. Experimental results confirm the high accuracy of the pig face detection model on the small-scale detection dataset and the ability to generate a large-scale dataset for pig face recognition on unlabeled data. The recognition experiments further verify that the distilled lightweight model outperforms its non-distilled counterparts and approaches the performance of the teacher model. This scalable, cost-effective solution shows significant promise for broader computer vision applications beyond agriculture. Full article

Background: The SmartPill, a multisensor ingestible capsule, is marketed for intestinal motility disorders. It includes a pressure sensor, which could be used to study intra-abdominal pressure (IAP) variations. However, the validation data are lacking for this use. Material and Methods: An experimental study was conducted on anesthetized pigs with stepwise variations of IAP (from 0 to 15 mmHg by 3 mmHg steps) generated by laparoscopic insufflation. A SmartPill, inserted by endoscopy, provided intragastric pressure data. These data were compensated to take into account the intrabdominal temperature. They were compared to the pressure recorded by intragastric (IG) and intraperitoneal (IP) wired sensors by statistical Spearman and Bland–Altmann analysis. Results: More than 4500 pressure values for each sensor were generated on two animals. The IG pressure values obtained with the SmartPill were correlated with the IG pressure values obtained with the wired sensor (respectively, Spearman ρ coefficients 0.90 ± 0.08 and 0.72 ± 0.25; bias of −28 ± −0.3 mmHg and −29.2 ± 0.5 mmHg for pigs 1 and 2). The intragastric SmartPill values were also correlated with the IAP measured intra-peritoneally (respectively, Spearman ρ coefficients 0.49 ± 0.18 and 0.57 ± 0.30; bias of −29 ± 1 mmHg and −31 ± 0.7 mmHg for pigs 1 and 2). Conclusions: The SmartPill is a wireless and painless sensor that appears to correctly monitor IAP variations. Full article

Efficiently managing temperature and humidity in a pig house is crucial for enhancing animal welfare. This research endeavors to develop an intelligent temperature and humidity control system grounded in a three-way decision and clustering algorithm. To establish and validate the effectiveness of this intelligent system, experiments were conducted to compare its performance against a naturally ventilated pig house without any control system. Additionally, comparisons were made with a threshold-based control system to evaluate the duration of temperature anomalies. The experimental findings demonstrate a substantial improvement in temperature regulation within the experimental pig house. Over a 24 h period, the minimum temperature increased by 4 °C, while the maximum temperature decreased by 8 °C, approaching the desired range. Moreover, the average air humidity decreased from 73.4% to 68.2%. In summary, this study presents a precision-driven intelligent control strategy for optimizing temperature and humidity management in pig housing facilities. Full article

This study aims to demonstrate an integrated methodology for the valorisation of bakery former food products (FFP) as an ingredient of pig feed diets. The methodology involves: conducting a needs analysis and a full path traceability scheme based on Global Standards 1 (GS1) Organisation (Brussels, Belgium) standards, designing digital tools to support the implementation of the traceability scheme, and assessing the valorisation of FFP and, more specifically, of bakery by-products in bakery meal (BM) production, and its implementation in pig feed diet. BM production comprises various bakery by-products, which were collected, unpacked, grinded, and thermally treated. Physicochemical and microbiological analyses were conducted on BM samples, mainly focusing on nutrient composition, and the presence of aflatoxins, mycotoxins, and pathogenic microorganisms. The BM was then fed to finishing pigs (at an inclusion rate of 20% w/w), in parallel to a control group fed with a conventional pig feed diet. The animals in both dietary groups were evaluated for growth performance, and meat samples were analysed for specific quality parameters and sensory characteristics. The results show that the addition of 20% w/w BM does not significantly affect the growth performance or the meat quality of the pigs. Moreover, a sensory evaluation revealed minor differences in the sensory characteristics of the meat samples, denoting that the BM addition does not seem to dwindle the final meat product. Full article

Aggressive behavior among pigs is a significant social issue that has severe repercussions on both the profitability and welfare of pig farms. Due to the complexity of aggression, recognizing it requires the consideration of both spatial and temporal features. To address this problem, we proposed an efficient method that utilizes the temporal shift module (TSM) for automatic recognition of pig aggression. In general, TSM is inserted into four 2D convolutional neural network models, including ResNet50, ResNeXt50, DenseNet201, and ConvNext-t, enabling the models to process both spatial and temporal features without increasing the model parameters and computational complexity. The proposed method was evaluated on the dataset established in this study, and the results indicate that the ResNeXt50-T (TSM inserted into ResNeXt50) model achieved the best balance between recognition accuracy and model parameters. On the test set, the ResNeXt50-T model achieved accuracy, recall, precision, F1 score, speed, and model parameters of 95.69%, 95.25%, 96.07%, 95.65%, 29 ms, and 22.98 M, respectively. These results show that the proposed method can effectively improve the accuracy of recognizing pig aggressive behavior and provide a reference for behavior recognition in actual scenarios of smart livestock farming. Full article

Analysis of pig posture is significant for improving the welfare and yield of captive pigs under different conditions. Detection of pig postures, such as standing, lateral lying, sternal lying, and sitting, can facilitate a comprehensive assessment of the psychological and physiological conditions of pigs, prediction of their abnormal or detrimental behavior, and evaluation of the farming conditions to improve pig welfare and yield. With the introduction of smart farming into the farming industry, effective and applicable posture detection methods become indispensable for realizing the above purposes in an intelligent and automatic manner. From early manual modeling to traditional machine vision, and then to deep learning, multifarious detection methods have been proposed to meet the practical demand. Posture detection methods based on deep learning show great superiority in terms of performance (such as accuracy, speed, and robustness) and feasibility (such as simplicity and universality) compared with most traditional methods. It is promising to popularize deep learning technology in actual commercial production on a large scale to automate pig posture monitoring. This review comprehensively introduces the data acquisition methods and sub-tasks for pig posture detection and their technological evolutionary processes, and also summarizes the application of mainstream deep learning models in pig posture detection. Finally, the limitations of current methods and the future directions for research will be discussed. Full article

Gastrointestinal (GI) complications, including motility disorders, metabolic deficiencies, and changes in gut microbiota following spinal cord injury (SCI), are associated with poor outcomes. After SCI, the autonomic nervous system becomes unbalanced below the level of injury and can lead to severe GI dysfunction. The SmartPill™ is a non-invasive capsule that, when ingested, transmits pH, temperature, and pressure readings that can be used to assess effects in GI function post-injury. Our minipig model allows us to assess these post-injury changes to optimize interventions and ultimately improve GI function. The aim of this study was to compare pre-injury to post-injury transit times, pH, and pressures in sections of GI tract by utilizing the SmartPill™ in three pigs after SCI at 2 and 6 weeks. Tributyrin was administered to two pigs to assess the influences on their gut microenvironment. We observed prolonged GET (Gastric Emptying Time) and CTT (Colon Transit Time), decreases in contraction frequencies (Con freq) in the antrum of the stomach, colon, and decreases in duodenal pressures post-injury. We noted increases in Sum amp generated at 2 weeks post-injury in the colon, with corresponding decreases in Con freq. We found transient changes in pH in the colon and small intestine at 2 weeks post-injury, with minimal effect on stomach pH post-injury. Prolonged GETs and CTTs can influence the absorptive profile in the gut and contribute to pathology development. This is the first pilot study to administer the SmartPill™ in minipigs in the context of SCI. Further investigations will elucidate these trends and characterize post-SCI GI function. Full article