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1
Zhang, Kaifeng, Dan Li, Jiayun Huang, and Yifei Chen. "Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks." Sensors 20, no. 4 (2020): 1085. http://dx.doi.org/10.3390/s20041085.
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The detection of pig behavior helps detect abnormal conditions such as diseases and dangerous movements in a timely and effective manner, which plays an important role in ensuring the health and well-being of pigs. Monitoring pig behavior by staff is time consuming, subjective, and impractical. Therefore, there is an urgent need to implement methods for identifying pig behavior automatically. In recent years, deep learning has been gradually applied to the study of pig behavior recognition. Existing studies judge the behavior of the pig only based on the posture of the pig in a still image frame, without considering the motion information of the behavior. However, optical flow can well reflect the motion information. Thus, this study took image frames and optical flow from videos as two-stream input objects to fully extract the temporal and spatial behavioral characteristics. Two-stream convolutional network models based on deep learning were proposed, including inflated 3D convnet (I3D) and temporal segment networks (TSN) whose feature extraction network is Residual Network (ResNet) or the Inception architecture (e.g., Inception with Batch Normalization (BN-Inception), InceptionV3, InceptionV4, or InceptionResNetV2) to achieve pig behavior recognition. A standard pig video behavior dataset that included 1000 videos of feeding, lying, walking, scratching and mounting from five kinds of different behavioral actions of pigs under natural conditions was created. The dataset was used to train and test the proposed models, and a series of comparative experiments were conducted. The experimental results showed that the TSN model whose feature extraction network was ResNet101 was able to recognize pig feeding, lying, walking, scratching, and mounting behaviors with a higher average of 98.99%, and the average recognition time of each video was 0.3163 s. The TSN model (ResNet101) is superior to the other models in solving the task of pig behavior recognition.
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Hao, Wangli, Kai Zhang, Li Zhang, et al. "TSML: A New Pig Behavior Recognition Method Based on Two-Stream Mutual Learning Network." Sensors 23, no. 11 (2023): 5092. http://dx.doi.org/10.3390/s23115092.
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Changes in pig behavior are crucial information in the livestock breeding process, and automatic pig behavior recognition is a vital method for improving pig welfare. However, most methods for pig behavior recognition rely on human observation and deep learning. Human observation is often time-consuming and labor-intensive, while deep learning models with a large number of parameters can result in slow training times and low efficiency. To address these issues, this paper proposes a novel deep mutual learning enhanced two-stream pig behavior recognition approach. The proposed model consists of two mutual learning networks, which include the red–green–blue color model (RGB) and flow streams. Additionally, each branch contains two student networks that learn collaboratively to effectively achieve robust and rich appearance or motion features, ultimately leading to improved recognition performance of pig behaviors. Finally, the results of RGB and flow branches are weighted and fused to further improve the performance of pig behavior recognition. Experimental results demonstrate the effectiveness of the proposed model, which achieves state-of-the-art recognition performance with an accuracy of 96.52%, surpassing other models by 2.71%.
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Tu, Shuqin, Jiaying Du, Yun Liang, et al. "Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach." Animals 14, no. 19 (2024): 2828. http://dx.doi.org/10.3390/ani14192828.
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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.
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Tu, Shuqin, Qiantao Zeng, Yun Liang, et al. "Automated Behavior Recognition and Tracking of Group-Housed Pigs with an Improved DeepSORT Method." Agriculture 12, no. 11 (2022): 1907. http://dx.doi.org/10.3390/agriculture12111907.
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Pig behavior recognition and tracking in group-housed livestock are effective aids for health and welfare monitoring in commercial settings. However, due to demanding farm conditions, the targets in the pig videos are heavily occluded and overlapped, and there are illumination changes, which cause error switches of pig identify (ID) in the tracking process and decrease the tracking quality. To solve these problems, this study proposed an improved DeepSORT algorithm for object tracking, which contained three processes. Firstly, two detectors, YOLOX-S and YOLO v5s, were developed to detect pig targets and classify four types of pig behaviors including lying, eating, standing, and other. Then, the improved DeepSORT was developed for pig behavior tracking and reducing error changes of pig ID by improving trajectory processing and data association. Finally, we established the public dataset annotation of group-housed pigs, with 3600 images in a total from 12 videos, which were suitable for pig tracking applications. The advantage of our method includes two aspects. One is that the trajectory processing and data association are improved by aiming at pig-specific scenarios, which are indoor scenes, and the number of pig target objects is stable. This improvement reduces the error switches of pig ID and enhances the stability of the tracking. The other is that the behavior classification information from the detectors is introduced into the tracking algorithm for behavior tracking. In the experiments of pig detection and behavior recognition, the YOLO v5s and YOLOX-S detectors achieved a high precision rate of 99.4% and 98.43%, a recall rate of 99% and 99.23, and a mean average precision (mAP) rate of 99.50% and 99.23%, respectively, with an AP.5:.95 of 89.3% and 87%. In the experiments of pig behavior tracking, the improved DeepSORT algorithm based on YOLOX-S obtained multi-object tracking accuracy (MOTA), ID switches (IDs), and IDF1 of 98.6%,15, and 95.7%, respectively. Compared with DeepSORT, it improved by 1.8% and 6.8% in MOTA and IDF1, respectively, and IDs had a significant decrease, with a decline of 80%. These experiments demonstrate that the improved DeepSORT can achieve pig behavior tracking with stable ID values under commercial conditions and provide scalable technical support for contactless automated pig monitoring.
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He, Zejun, Yunfei Jia, and Yifan Ji. "Analysis of Influencing Factors and Mechanism of Farmers’ Green Production Behaviors in China." International Journal of Environmental Research and Public Health 20, no. 2 (2023): 961. http://dx.doi.org/10.3390/ijerph20020961.
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The green production behavior of pig farmers is the basis for high-quality development in animal husbandry. In order to solve the problem of poor green production behaviors in small- and medium-sized pig farmers, it is necessary to analyze the influencing factors and how they interact with each other. The Rational Peasant Theory and Prospect Theory were used in this paper to analyze the occurrence motivation of the green production behaviors of small- and medium-sized pig farmers. The Logit model and the ISM analysis method were used to test the influencing factors and their mechanisms. This was conducted using data from a study of 747 small- and medium-sized pig farmers in Henan Province. The results show that the green production behaviors of small- and medium-sized pig farmers are motivated by internal expected return, affected by the monitoring pressure from external stakeholders and limited by their own resource capacity; the influencing factors of different green production behaviors are different, and there are more influencing factors of scientific disease control, standardized management and waste recycling than of rational feeding. The following shows how the influencing factors on pig farmers’ green production behavior interact with one another: level of education → external pressure, farming conditions and operating characteristics → cognition of return → green production behavior (i.e., cognition of return is the direct factor; external pressure, farming conditions and operating characteristics are indirect factors; and level of education is the underlying factor). Some measures should be implemented to promote green production behaviors, such as the continuation of the support for green production, the strengthening of supervision and publicity, the increasing of investment in technology and equipment, and the improving of the green production literacy of farmers. In conclusion, this paper deepens the understanding of the mechanism of green production behaviors of small- and medium-sized pig farmers, and provides the theoretical basis and concrete measures for the government and for pig farmers.
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Mluba, Hassan Seif, Othmane Atif, Jonguk Lee, Daihee Park, and Yongwha Chung. "Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring." Sensors 24, no. 7 (2024): 2185. http://dx.doi.org/10.3390/s24072185.
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The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs’ health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs’ health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs’ health and welfare.
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Zhang, Ye, Xuesong Yang, Fang Sun, et al. "Emotional “Contagion” in Piglets after Sensory Avoidance of Rewarding and Punishing Treatment." Animals 14, no. 7 (2024): 1110. http://dx.doi.org/10.3390/ani14071110.
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In the pig farming industry, it is recommended to avoid groups when treating individuals to reduce adverse reactions in the group. However, can this eliminate the adverse effects effectively? Piglets were assigned to the Rewarding Group (RG), the Punishing Group (PG), and the Paired Control Group (PCG). There were six replicates in each group, with two paired piglets per replicate. One piglet of the RG and PG was randomly selected as the Treated pig (TP), treated with food rewards or electric shock, and the other as the Naive pig (NP). The NPs in the RG and PG were unaware of the treatment process, and piglets in the PCG were not treated. The behavior and heart rate changes of all piglets were recorded. Compared to the RG, the NPs in the PG showed longer proximity but less contact behavior, and the TPs in the PG showed more freezing behavior. The percentage change in heart rate of the NPs was synchronized with the TPs. This shows that after sensory avoidance, the untreated pigs could also feel the emotions of their peers and their emotional state was affected by their peers, and the negative emotions in the pigs lasted longer than the positive emotions. The avoidance process does not prevent the transfer of negative emotions to peers via emotional contagion from the stimulated pig.
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Wang, Lei, and Zi-Gang Yang. "Analysis on Risk Avoidance Behaviors and Influential Factors of Epidemic Disease among Pig Farmers." African and Asian Studies 21, no. 1-2 (2022): 90–111. http://dx.doi.org/10.1163/15692108-12341531.
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Abstract The initial outbreak, high mortality and wide range of influence of African swine fever in China have an impact on the stable development of China’s pig market price and industry. Choosing the correct risk aversion behavior of pig farmers is the only way to promote the development of modern pig breeding industry and protect people’s livelihood. Studying the epidemic risk aversion behavior of pig farmers and its influencing factors is conducive to enhance the ability of farmers to resist risks and improve the efficiency and production level of pig breeding. Taking pig farmers in three northeastern provinces as the survey object, this paper analyzes the willingness of pig farmers to take epidemic prevention and control measures by using multivariate ordered logistic and multiple logistic models, and discusses the main factors affecting the epidemic risk avoidance behavior of pig farmers. The study found that factors such as pig breeding scale, understanding of pig insurance and epidemic prevention and control knowledge, risk preference and so on can significantly affect the willingness of pig farmers to take epidemic prevention and control measures. Education, specialization and breeding prospect judgment can significantly affect the choice of epidemic risk aversion behavior of pig farmers.
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Andersen, Heidi Mai-Lis, Anne Grete Kongsted, and Malene Jakobsen. "Pig elimination behavior—A review." Applied Animal Behaviour Science 222 (January 2020): 104888. http://dx.doi.org/10.1016/j.applanim.2019.104888.
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Winkel, Carolin, Marie von Meyer-Höfer, and Heinke Heise. "Understanding German Pig Farmers’ Intentions to Design and Construct Pig Housing for the Improvement of Animal Welfare." Animals 10, no. 10 (2020): 1760. http://dx.doi.org/10.3390/ani10101760.
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Improving farm animal welfare requires modifications to the behavior of many stakeholders. Investments in more animal-friendly barns to improve animal welfare have already been made by some farmers. However, more farmers must be persuaded to modernize their barns. The marketing of animal-friendly products is the responsibility of retailers, and consumers have to purchase these products. Currently, little is known about what (and how) underlying psychological factors influence a farmer’s intention to construct pig housing to improve farm animal welfare. Pig farmers in Germany were questioned via an online questionnaire in May 2020 (n = 424). Based on the Theory of Planned Behavior (TPB), partial least squares path modeling was used. The constructs: attitude, subjective norm, direct and indirect experience associated with the construction of pig housing substantially influenced the farmers’ behaviors. As expected, the impact of perceived behavioral control on intention was negative but was also very low and only slightly significant. Contrary to expectations, the perceived behavioral control had no significant influence on farmers’ behaviors. Pig farmers who have already rebuilt their pigs’ housing should be motivated to share their experiences to influence their colleagues’ intentions to construct. Our results will encourage policy makers to consider the important role of the different psychological and intrinsic factors influencing pig farmers. Thus, the sustainability of pig farming can be improved by giving politicians a better understanding of farmers’ behaviors.
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Li, Dan, Kaifeng Zhang, Zhenbo Li, and Yifei Chen. "A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs." Sensors 20, no. 8 (2020): 2381. http://dx.doi.org/10.3390/s20082381.
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The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig’s behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig’s multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.
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Zeng, Yaqiong, Hao Wang, Rongdan Ruan, et al. "Effect of Stocking Density on Behavior and Pen Cleanliness of Grouped Growing Pigs." Agriculture 12, no. 3 (2022): 418. http://dx.doi.org/10.3390/agriculture12030418.
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In recent years, animal cleanliness during production has gradually attracted increasing attention. Although pigs by nature tend to excrete in dark and humid corners, in the prevalent highly intensive pig production, excessive stocking density often restricts the ability of pigs to excrete at designated points, thereby leading to pollution of the pens. To study the effect of stocking density on pen cleanliness and the relevant pig behavior, a total of 216 Landrace × Yorkshire × Duroc hybrid pigs were randomly grouped at 0.5, 0.7, or 0.9 m2 per pig at 59 ± 3 days of age. The temporal and spatial distributions of lying and excreting behavior of pigs were monitored on days 1, 7, and 35 after transfer, and the cleanliness of pig pens under three stocking densities was scored. The results showed that there were no significant differences in excreting or lying time rhythms among the three treatments. In the initial and stable periods of group transfer, the excretion rate of pigs on slatted floors was significantly higher than that on solid floors at 0.9 m2/pig (p < 0.01). During the group stabilization stage and at the end of the experiment, the lying rate of pigs on solid floors under 0.9 m2/pig was 10.81%, that is, 7.43% higher than that of 0.7 m2/pig, and the differences were significant. Judging from the pollution score of the solid floors, the three stocking densities all showed more serious pollution at the corners, and the pollution score of the pens with a density of 0.9 m2/pig was lower than that of the pens with a density of 0.5 and 0.7 m2/pig. The analysis of whether pigs had corresponding behaviors in specific functional areas showed that pigs at a density of 0.9 m2/pig had a 10.14% lower lying rate on slatted floors (the expected excretion area) than on solid floors, whereas the densities of 0.5 and 0.7 m2/pig showed the opposite pattern. Pigs at a density of 0.9 m2/pig had lower excretion rates in both corners of the solid floors (the desired lying area) than that of the 0.5 m2/pig group (p < 0.05) and 0.7 m2/pig group (p > 0.05). These results indicate that when the effective occupied space of pigs was larger, specific behaviors were more likely to occur in the set functional areas, and the cleanliness of the pen was higher. Under the conditions of this experiment, the recommended stocking density for growing pigs was 0.9 m2/pig. Of course, a larger space may be more beneficial to animal health and welfare, but the economic costs must also be considered.
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Pu, Ying, Yaqin Zhao, Hao Ma, and Junxiong Wang. "A Lightweight Pig Aggressive Behavior Recognition Model by Effective Integration of Spatio-Temporal Features." Animals 15, no. 8 (2025): 1159. https://doi.org/10.3390/ani15081159.
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With the rise of smart agriculture and the expansion of pig farming, pig aggressive behavior recognition is crucial for maintaining herd health and improving farming efficiency. The differences in background and light variation in different barns can lead to the missed detection and false detection of pig aggressive behaviors. Therefore, we propose a deep learning-based pig aggressive behavior recognition model, in order to improve the adaptability of the model in complex pig environments. This model, combined with MobileNetV2 and Autoformer, can effectively extract local detail features of pig aggression and temporal correlation information of video frame sequences. Both Convolutional Block Attention Module (CBAM) and Advanced Filtering Feature Fusion Pyramid Network (HS-FPN) are integrated into the lightweight convolutional network MobileNetV2, which can more accurately capture key visual features of pig aggression and enhance the ability to detect small targets. We extract temporal correlation information between consecutive frames by the improved Autoformer. The Gate Attention Unit (GAU) is embedded into the Autoformer encoder in order to focus on important features of pig aggression while reducing computational latency. Experimental validation was implemented on public datasets, and the results showed that the classification recall, precision, accuracy, and F1-score of the model proposed in this paper reach 98.08%, 94.44%, 96.23%, and 96.23%, and the parameter quantity is optimized to 10.41 M. Compared with MobileNetV2-LSTM and MobileNetV2-GRU, the accuracy has been improved by 3.5% and 3.0%, respectively. Therefore, this model achieves a balance between recognition accuracy and computational complexity and is more suitable for automatic pig aggression recognition in practical farming scenarios, providing data support for scientific feeding and management strategies in pig farming.
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Melfsen, Andreas, Arvid Lepsien, Jan Bosselmann, Agnes Koschmider, and Eberhard Hartung. "Describing Behavior Sequences of Fattening Pigs Using Process Mining on Video Data and Automated Pig Behavior Recognition." Agriculture 13, no. 8 (2023): 1639. http://dx.doi.org/10.3390/agriculture13081639.
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This study aimed to demonstrate the application of process mining on video data of pigs, facilitating the analysis of behavioral patterns. Video data were collected over a period of 5 days from a pig pen in a mechanically ventilated barn and used for analysis. The approach in this study relies on a series of individual steps to allow process mining on this data set. These steps include object detection and tracking, spatiotemporal activity recognition in video data, and process model analysis. Each step gives insights into pig behavior at different time points and locations within the pen, offering increasing levels of detail to describe typical pig behavior up to process models reflecting different behavior sequences for clustered datasets. Our data-driven approach proves suitable for the comprehensive analysis of behavioral sequences in conventional pig farming.
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Kittawornrat, Apisit, and Jeffrey J. Zimmerman. "Toward a better understanding of pig behavior and pig welfare." Animal Health Research Reviews 12, no. 1 (2010): 25–32. http://dx.doi.org/10.1017/s1466252310000174.
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AbstractPork production began to flourish in the USA after the practice of finishing pigs on corn was popularized in the late 1600s. By the 1840s, there were 35 million pigs and 20 million people in the USA and Cincinnati was the world's largest pork market. Between 1890 and the present, the total number of pigs in the USA has remained at 50–60 million, but dramatic changes in swine husbandry over the course of the 20th century have metamorphosed pig production from small, extensive (outdoor), labor-dependent enterprises into large, intensive (indoor), capital-dependent, production systems. This development has led to debate concerning the impact of swine production on animal/human health, the environment, and the welfare of the animals under our care. In a very tangible way, the future of pork production depends on effectively addressing the public's concerns regarding animal welfare and health. Here, we review basic sensory and behavioral aspects of swine with the objective of reaching a better understanding of pig behavior and pig welfare. The premise of this discussion is that safeguarding animal welfare and health is good for pigs, pork producers and the animal-conscious public.
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Wang, Chao, Qian Han, Runze Liu, et al. "Equipping Farrowing Pens with Straw Improves Maternal Behavior and Physiology of Min-Pig Hybrid Sows." Animals 10, no. 1 (2020): 105. http://dx.doi.org/10.3390/ani10010105.
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This study investigated the effects of two factors, enriched environment (EE) and different crossbreeds, on the maternal behavior and physiology of Min-pig hybrid sows. The analysis was performed on a total of 72 multiparous sows, including Duroc × Min pig (DM), Landrace × Min pig (LM), and Landrace × Yorkshire (LY) sows, using a total of 24 sows per cross. The sows were housed in two different farrowing pens, one with straw (EE) and one without straw (barren environment (BE)). The results showed that nest-building behavior, including the frequency, total duration, and bout duration, was significantly higher in EE sows than in BE sows (p < 0.01). The frequency and duration of prepartum nest-building behavior were higher in DM and LM sows than in LY sows (p < 0.0001). During the first three days postpartum, EE sows spent a shorter time in ventral recumbency compared with BE sows (p < 0.05). The oxytocin (p < 0.05) and prolactin (p < 0.01) concentrations of EE sows were significantly higher than in BE sows; however, the concentration of cortisol followed the opposite (p < 0.01). The concentration of oxytocin was significantly higher in DM and LM sows than in LY sows (p < 0.01). In conclusion, both EE increased the expression of hormones related to parental behaviors and prenatal nesting and nursing behavior of sows. Furthermore, an EE can also reduce stress in sows. Min-pig hybrids may inherit highly advantageous characteristics of maternal behavior of Min-pig sows.
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Tu, Shuqin, Haoxuan Ou, Liang Mao, Jiaying Du, Yuefei Cao, and Weidian Chen. "Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack." Animals 14, no. 22 (2024): 3299. http://dx.doi.org/10.3390/ani14223299.
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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.
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Kim, Jun Ho, Ji-Qin Ni, Wonders Ogundare, et al. "Sow and Piglet Behavior Characterization Using Visual Observation, Sensor Detection, and Video Recording." Applied Sciences 15, no. 6 (2025): 3018. https://doi.org/10.3390/app15063018.
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Animal behaviors are key signs of animals’ stress, disease, and overall well-being. This study was conducted in an experimental farrowing building using eighteen sow pens: nine exposed to natural heat stress under summer indoor temperatures and nine receiving cooling treatments via innovative cooling pads. Sow and piglet behaviors were recorded in an ethogram through direct visual observation every 5 min for 48 h. Passive infrared detectors were used for continuous pig behavior monitoring every sec. Zmodo wireless cameras were used for video monitoring to validate sensor detection results. Visual observation revealed distinct pig behaviors between the treatments. The sows had peak times in eating, standing, and drinking approximately from 05:00 to 12:00 and from 16:00 to 22:00. The sows under heat stress spent 49.3% more time lying (p < 0.01). They spent 10.7% less time sleeping (p < 0.05). Piglets under heat stress spent more time sleeping but less time nursing. The sensor outputs and pig moving behaviors (i.e., sow eating + standing + drinking + sitting + piglet walking) had a strong positive correlation (ρ = 0.81 for heat stress and ρ = 0.74 for cooling). In contrast, there were strong-to-moderate negative correlations (ρ = −0.77 for heat stress and ρ = −0.56 for cooling) between the sensor outputs and sow on-body behaviors (i.e., sow lying + nursing + sleeping). Video recordings validated the response and sensitivity of the sensors, with them able to quickly capture changes in pig behaviors and provide behavioral information about the nuanced pig movements.
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Lei, Kaidong, Bugao Li, Shan Zhong, et al. "Research on Video Behavior Detection and Analysis Model for Sow Estrus Cycle Based on Deep Learning." Agriculture 15, no. 9 (2025): 975. https://doi.org/10.3390/agriculture15090975.
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Against the backdrop of precision livestock farming, sow behavior analysis holds significant theoretical and practical value. Traditional production methods face challenges such as low production efficiency, high labor intensity, and increased disease prevention risks. With the rapid advancement of optoelectronic technology and deep learning, more technologies are being integrated into smart agriculture. Intelligent large-scale pig farming has become an effective means to improve sow quality and productivity, with behavior recognition technology playing a crucial role in intelligent pig farming. Specifically, monitoring sow behavior enables an effective assessment of health conditions and welfare levels, ensuring efficient and healthy sow production. This study constructs a 3D-CNN model based on video data from the sow estrus cycle, achieving analysis of SOB, SOC, SOS, and SOW behaviors. In typical behavior classification, the model attains accuracy, recall, and F1-score values of (1.00, 0.90, 0.95; 0.96, 0.98, 0.97; 1.00, 0.96, 0.98; 0.86, 1.00, 0.93), respectively. Additionally, under conditions of multi-pig interference and non-specifically labeled data, the accuracy, recall, and F1-scores for the semantic recognition of SOB, SOC, SOS, and SOW behaviors based on the 3D-CNN model are (1.00, 0.90, 0.95; 0.89, 0.89, 0.89; 0.91, 1.00, 0.95; 1.00, 1.00, 1.00), respectively. These findings provide key technical support for establishing the classification and semantic recognition of typical sow behaviors during the estrus cycle, while also offering a practical solution for rapid video-based behavior detection and welfare monitoring in precision livestock farming.
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Siegford, Janice M. "48 Does Automated Behavioral Monitoring Inevitably Lead to Improved Pig welfare?" Journal of Animal Science 101, Supplement_3 (2023): 317–18. http://dx.doi.org/10.1093/jas/skad281.377.
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Abstract Academics, producers, veterinarians, technology developers, and even the general public seem to be talking about using technology to solve agricultural problems. The starting point for many proposed technological solutions to help manage pigs is the automated detection of specific behaviors. These behavioral data then need to be translated into actionable information to assess welfare, and ideally to provide a management solution to ensure good welfare for the monitored pigs. However, detecting behavior of animals, particularly of relatively homogeneous animals living in large groups at high densities, such as pigs on commercial farms, is not a trivial problem. Most functional behaviors are complex and made of multiple smaller elements, including postures and simple motions. Training technology to detect behaviors with accuracy that mimics a human expert is difficult, due to behavioral complexity as well as to variation in how each individual does a behavior and how physical and social environmental context affect performance of a behavior. Disappointingly, many technologies that claim to detect performance of behaviors are in fact only detecting the proximity of the pigs to resources or to each other and then assuming that pig is interacting with the resource or social partner in a particular way. However, a pig standing near a feeder is not always eating, nor is a pig near another pig always deliberately interacting with that other animal. It is necessary to closely evaluate automated behavior detection solutions to determine if these do directly detect the behavioral outcomes of interest. The next problem is connecting the detected behavior to a specific welfare outcome. Animals typically perform behaviors for biologically motivated reasons related to maintaining physiological homeostasis, responding to disease challenges, or to expressing their emotional state. Yet making a direct link between performance of a certain behavior and a subsequent welfare state is not straightforward. For example, grooming is often thought of as a comfort behavior, but animals that are distressed may groom as a displacement behavior. Therefore, context and other measures are needed to complement the behavioral information before drawing a welfare conclusion. A final problem to consider is whether detection of a welfare problem inevitably leads to improved welfare. In some cases, such as when a technology identifies the start of an outbreak of treatable illness, action is possible. In other cases, the technology may detect a more intractable problem, such as lameness in a sow. If we cannot effectively cure the underlying problem or eliminate the associated pain or distress, does this raise ethical dilemmas or reduce trust in the swine industry? While technologies do hold promise to better monitor and manage pigs in labor efficient ways, we must mindfully develop solutions that monitor pigs' behavior and cautiously evaluate their ultimate impacts on pig welfare.
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Han, Junjie, Janice Siegford, Dirk Colbry, et al. "10 Deep Learning for Multi-Behavioral Video Classification of Interactive Behaviors of Pigs in Single-Spaced Automatic Feeders." Journal of Animal Science 100, Supplement_2 (2022): 13. http://dx.doi.org/10.1093/jas/skac064.020.
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Abstract Automatic pig feeders record individual feeding behavior traits that can be used in genetic selection. However, these feeders cannot measure how feeding traits are affected by behavioral interactions between animals. Moreover, recording behavioral interactions can improve the estimation of social genetic effects. This motivated our assessment of computer vision to classify 4 types of interactions at the feeder: head-to-body contact (HB) including head knocking, tail biting, pushing, gentle nosing and casual contact between head/ears of a pig with a feeding pig; levering (L) where the feeding pig was lifted from behind by another pig; mounting (M) where the feeding pig was mounted by another pig; and no-contact (NC) when a second pig entered the feeder without touching the feeding pig. Behavior at the feeder was filmed twice, 3 weeks apart, for 2 consecutive days each week in 6 groups of grow-finish pigs (10 per group) housed in pens equipped with FIRE feeders. Video segments that involved 2 pigs in the feeder were selected, and labeled as HB (n=10,114), L (n=925), M (n=1,242) and NC (n=3,398). Due to the sparse data available for training and the complex nature of the behavioral interactions, we utilized pretrained convolutional neural networks for automatic spatial feature extraction followed by long short-term memory for temporal feature extraction from sequences of frames. Focal loss, a loss function that assigns different weights to hard/easily misclassified examples to handle class imbalance problem, was used in this study. Accuracy, recall, and precision of behavior classification were obtained from 5 random cross-validations. The overall accuracy was 0.967±0.002. The average recalls for HB, L, M, and NC were 0.977±0.004, 0.866±0.047, 0.969±0.011, and 0.964±0.009 respectively, while average precisions were 0.976±0.002, 0.901±0.029, 0.956±0.018, and 0.963±0.012. The proposed algorithm accurately classified multiple interactive behaviors in an automated feeding stall from digital videos.
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Wang, Shunli, Honghua Jiang, Yongliang Qiao, Shuzhen Jiang, Huaiqin Lin, and Qian Sun. "The Research Progress of Vision-Based Artificial Intelligence in Smart Pig Farming." Sensors 22, no. 17 (2022): 6541. http://dx.doi.org/10.3390/s22176541.
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Pork accounts for an important proportion of livestock products. For pig farming, a lot of manpower, material resources and time are required to monitor pig health and welfare. As the number of pigs in farming increases, the continued use of traditional monitoring methods may cause stress and harm to pigs and farmers and affect pig health and welfare as well as farming economic output. In addition, the application of artificial intelligence has become a core part of smart pig farming. The precision pig farming system uses sensors such as cameras and radio frequency identification to monitor biometric information such as pig sound and pig behavior in real-time and convert them into key indicators of pig health and welfare. By analyzing the key indicators, problems in pig health and welfare can be detected early, and timely intervention and treatment can be provided, which helps to improve the production and economic efficiency of pig farming. This paper studies more than 150 papers on precision pig farming and summarizes and evaluates the application of artificial intelligence technologies to pig detection, tracking, behavior recognition and sound recognition. Finally, we summarize and discuss the opportunities and challenges of precision pig farming.
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Sun, Jiangqi, Zongzheng Liu, Juan Ai, and Zhaojiu Chen. "Effect of pig breeding scale on manure resource utilization-The moderating effect based on technology cognition." PLOS ONE 20, no. 1 (2025): e0314410. https://doi.org/10.1371/journal.pone.0314410.
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The utilization of manure resources is an important measure to promote the development of agricultural green low-carbon cycle and solve the challenges associated with the current large-scale development of the livestock and poultry breeding industry. Based on the survey data of pig farmers in Qingdao, Shandong Province, China, this paper constructs a theoretical analysis framework of pig breeding scale and technical cognition on the utilization behavior of livestock and poultry manure resources of pig farmers. The binary Logit model and the moderating effect model are used to deeply explore the scale effect of breeding scale on the utilization behavior of pig farmers’ manure resources, and the moderating effect of technical cognition on the influence of breeding scale on the utilization behavior of manure resources. First, at the present stage, pig farmers show certain differences in the resource utilization of manure. Due to the differences in the personal characteristics, family characteristics, and breeding characteristics of pig farmers, the influencing factors of resource utilization of pig farmers of different scales are different; Second, the scale of pig breeding has a significant positive promoting effect on the resource utilization of manure, increasing the probability of pig farmers to treat manure, guiding retail and small-scale farmers to moderately expand the scale of breeding, gradually moving to large-scale breeding, realizing centralized management and resource utilization of manure, and reducing the unit cost of manure treatment. Third, technical ease of use has a positive regulatory effect on pig breeding scale and manure resource utilization behavior. When pig farmers perceive that the technology of manure resource utilization is easy to use, they will increase the probability of participating in the resource utilization of manure, reduce the environmental pollution caused by improper disposal of manure, and promote the low-carbon and circular development of livestock and poultry industry. Based on the above findings, this paper aims to provide practical enlightenment for policy makers and researchers to strengthen the environmental governance and sustainable development of livestock industry.
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Griffin, Mackenzie, Jayden Lawrence, Marley M. Knowles, Michael Barrowclough, and Justin W. Rickard. "41 Effect of space allocation on feeding behavior in grow-finish pigs." Journal of Animal Science 103, Supplement_1 (2025): 2–3. https://doi.org/10.1093/jas/skaf102.002.
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Abstract The objective of this study was to evaluate the feeding behavior of grow-finish pigs in pens with two different floor space allowances. A total of 159 commercial crossbred barrows (n = 90; 25.87 ± 7.43 kg) and gilts (n = 69; 28.74 ± 4.08 kg) were housed in single gender pens and fed a common diet for approximately 14 weeks. At the start of the trial pigs were randomly assigned to pens providing one of two space allocations: 3.05m2/pig or 2.29m2/pig with 3 replicate pens per treatment. At allotment each pig was tagged in the ear with a unique colored ear tag for individual identification. Feed was delivered in standard 3-hole box feeders and water was delivered via nipple waterers. Pigs had ad libitum access to feed and water throughout the trial. Final body weight averaged 119.15 ± 31.54 kg (barrows) and 122.84 ± 12.55 kg (gilts). Video monitoring of trial pens was accomplished with cameras mounted to the barn ceiling and 12 h recordings were taken during week 1 (first week on trial following a 7 d acclimation period) and week 14 (last week before marketing at 97 d) to evaluate feeding behavior and activity at both the beginning and end of the grow-finish period. Behaviors of interest included activity (walking, standing, moving, drinking), the number of times each pig entered the feeder, total time spent eating and eating type (nibbler vs. gorger). Data were analyzed as a completely randomized design using the MIXED procedure in SAS. In week 1, increased floor space tended to increase the number of times pigs entered the feeder (P = 0.07) and significantly increased (P = 0.008) the total time spent eating by ~14%. However, these trends were not present at week 14. Floor space allocation did not influence eating type or alter activity level (P &gt; 0.05) in either week 1 or week 14. No significant differences in feeding behavior resulted from an interaction between space allocation and gender. In week 1, gender did not influence the number of times pigs entered the feeder. However, barrows spent more time eating than gilts (84 vs. 63 min) (P &lt; 0.0001). At week 14 these differences were not present (P &gt; 0.05). As space allocation in swine production becomes increasingly scrutinized it will be important to evaluate behavior changes in the context of performance. In this study, increasing floor space impacted grow-finish pig feeding behavior early in the finishing period, but those behaviors were not observed at the end of finishing. Future research in this direction will attempt to identify at which point in production those behaviors change.
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Yuan, Feiyan, Hang Zhang, and Tonghai Liu. "Stress-Free Detection Technologies for Pig Growth Based on Welfare Farming: A Review." Applied Engineering in Agriculture 36, no. 3 (2020): 357–73. http://dx.doi.org/10.13031/aea.13329.
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Abstract. The detection of pig growth and monitoring of abnormal behaviors are key steps in pig breeding management. Using conventional methods to obtain information on growth and abnormal behavior causes stress to pigs, directly affects the number of live pigs for market, and decreases the quality of the pork. Moreover, this approach requires considerable labor, reduces economic returns, and does not meet the requirements of high-welfare farming. Compared to the conventional methods for obtaining growth parameters and data on abnormal behaviors, modern information technology provides a new method for stress-free growth detection and behavior monitoring in farmed pigs. This article first summarizes the importance of body size, body mass, and abnormal behaviors as well as the correlations among these factors. For the research on growth detection and behavior monitoring based on computer vision, radio frequency identification (RFID) and sensor technology, methods of detecting increases in body size and body mass and methods of monitoring abnormal behaviors are summarized separately. Through computer-computer vision technology, we found that the data sampling for growth and abnormal behaviors of the pigs was achieved without contact monitoring but, rather, occurred at the expense of complex data calculation and a higher illumination requirement during data collection. However, with the development of depth camera technology and improved product performance, technology based on high-precision depth cameras reduces the amount of data processing and complexity, making it possible to obtain real-time data on pig growth and abnormal behaviors. Moreover, with the advantages of no contact and no stress, the method conforms to the requirements of welfare farming. Keywords: Abnormal behaviors, Stress-free detection, Welfare farming.
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Mugonya, J., S. W. Kalule, and E. K. Ndyomugyenyi. "Utilisation of labour among pig farmers in northern Uganda." African Crop Science Journal 28, s1 (2020): 237–46. http://dx.doi.org/10.4314/acsj.v28i1.18s.
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In many sub-Saharan countries, pig (Susscrofa domesticus) production is increasingly an important food security and income generating activity for smallholder farmers. This is attributed to the high prospects for vigilance of the pork market, driven by urbanisation, population growth and dietary transition towards more animal protein per capita. Therefore, increasing pig production is one of the viable pathways to get smallholder farmers out of poverty and food insecurity. Although there are extensive studies about the elements of pig production, such as feeding, breeding and space requirements; little work has been done on distribution of innovation behaviour and the socio-economic factors that influence labour utilisation in the region. The objective of this study was to determine the socio-economic factors that influence labour (family or hired) utilisation and distribution of innovation behaviour among pig farmers in Northern Uganda. Through a cross sectional survey and descriptive analysis, we characterised smallholder pig farmers in the northern Uganda by type of labour used for pig production, and explored the distribution of the dimensions of innovation behaviour (exploration, experimentation, adaptation and modification) among them. Results revealed that young educated farmers with non-farm employment, a smaller household size, belonging to a farmer group and who had many pigs were more likely to use hired labour than those with counter characteristics. There were significant differences in the number of farmers who exhibited the different dimensions of innovation behavior. Therefore, interventions to boost pig production through the use of hired labour should consider the socio-economic differences among farmers which determine labour constraints they face.
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McGlone, John J., and Julie L. Morrow. "Reduction of Pig Agonistic Behavior by Androstenone." Journal of Animal Science 66, no. 4 (1988): 880. http://dx.doi.org/10.2527/jas1988.664880x.
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Suzaki, You, Hiroshi Wada, Kenji Ohyama, Toshimitsu Kobayasi, Kouji Houzawa, and Tomonori Takasaka. "Dynamic Behavior of Guinea Pig Middle Ear." Nippon Jibiinkoka Gakkai Kaiho 100, no. 3 (1997): 342–50. http://dx.doi.org/10.3950/jibiinkoka.100.342.
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Mao, Rui, Dongzhen Shen, Ruiqi Wang, et al. "An Integrated Gather-and-Distribute Mechanism and Attention-Enhanced Deformable Convolution Model for Pig Behavior Recognition." Animals 14, no. 9 (2024): 1316. http://dx.doi.org/10.3390/ani14091316.
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The behavior of pigs is intricately tied to their health status, highlighting the critical importance of accurately recognizing pig behavior, particularly abnormal behavior, for effective health monitoring and management. This study addresses the challenge of accommodating frequent non-rigid deformations in pig behavior using deformable convolutional networks (DCN) to extract more comprehensive features by incorporating offsets during training. To overcome the inherent limitations of traditional DCN offset weight calculations, the study introduces the multi-path coordinate attention (MPCA) mechanism to enhance the optimization of the DCN offset weight calculation within the designed DCN-MPCA module, further integrated into the cross-scale cross-feature (C2f) module of the backbone network. This optimized C2f-DM module significantly enhances feature extraction capabilities. Additionally, a gather-and-distribute (GD) mechanism is employed in the neck to improve non-adjacent layer feature fusion in the YOLOv8 network. Consequently, the novel DM-GD-YOLO model proposed in this study is evaluated on a self-built dataset comprising 11,999 images obtained from an online monitoring platform focusing on pigs aged between 70 and 150 days. The results show that DM-GD-YOLO can simultaneously recognize four common behaviors and three abnormal behaviors, achieving a precision of 88.2%, recall of 92.2%, and mean average precision (mAP) of 95.3% with 6.0MB Parameters and 10.0G FLOPs. Overall, the model outperforms popular models such as Faster R-CNN, EfficientDet, YOLOv7, and YOLOv8 in monitoring pens with about 30 pigs, providing technical support for the intelligent management and welfare-focused breeding of pigs while advancing the transformation and modernization of the pig industry.
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Nieckele, A. O., A. M. B. Braga, and L. F. A. Azevedo. "Transient Pig Motion Through Gas and Liquid Pipelines." Journal of Energy Resources Technology 123, no. 4 (2001): 260–69. http://dx.doi.org/10.1115/1.1413466.
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Simulation of the transient motion of pigs through liquid and gas pipelines is presented. The differential form of the mass and linear momentum equations for compressible liquid and gas flows were solved by a finite difference numerical technique. The fluid flow equations were combined with a linear momentum equation for the pig and a model for bypass flow through the pig. The pig/wall contact forces were simulated by a stick/slip model. The contact forces developed by disk pigs and the pipe wall were predicted by a postbuckling finite element analysis of the discs. Test cases representing typical pigging operations were studied using the numerical model developed. The fluid flow and pig behavior predicted by the model presented a reasonable behavior, and contributed for a better understanding of the pig dynamics through gas and liquid pipelines.
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Ji, Hengyi, Jionghua Yu, Fengdan Lao, Yanrong Zhuang, Yanbin Wen, and Guanghui Teng. "Automatic Position Detection and Posture Recognition of Grouped Pigs Based on Deep Learning." Agriculture 12, no. 9 (2022): 1314. http://dx.doi.org/10.3390/agriculture12091314.
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The accurate and rapid detection of objects in videos facilitates the identification of abnormal behaviors in pigs and the introduction of preventive measures to reduce morbidity. In addition, accurate and effective pig detection algorithms provide a basis for pig behavior analysis and management decision-making. Monitoring the posture of pigs can enable the detection of the precursors of pig diseases in a timely manner and identify factors that impact pigs’ health, which helps to evaluate their health status and comfort. Excessive sitting represents abnormal behavior when pigs are frustrated in a restricted environment. The present study focuses on the automatic recognition of standing posture and lying posture in grouped pigs, which shows a lack of recognition of sitting posture. The main contributions of this paper are as follows: A human-annotated dataset of standing, lying, and sitting postures captured by 2D cameras during the day and night in a pig barn was established, and a simplified copy, paste, and label smoothing strategy was applied to solve the problem of class imbalance caused by the lack of sitting postures among pigs in the dataset. The improved YOLOX has an average precision with an intersection over union threshold of 0.5 (AP0.5) of 99.5% and average precision with an intersection over union threshold of 0.5–0.95 (AP0.5–0.95) of 91% in pig position detection; an AP0.5 of 90.9% and an AP0.5–0.95 of 82.8% in sitting posture recognition; a mean average precision with intersection over union threshold of 0.5 (mAP0.5) of 95.7% and a mean average precision with intersection over union threshold of 0.5–0.95 (mAP0.5–0.95) of 87.2% in all posture recognition. The method proposed in our study can improve the position detection and posture recognition of grouped pigs effectively, especially for pig sitting posture recognition, and can meet the needs of practical application in pig farms.
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Xin, Wenshui, Xinjian Li, Feng Zhang, et al. "A multi-population survey on swine feeding behavior with electronic feeding devices." Archives Animal Breeding 59, no. 4 (2016): 445–52. http://dx.doi.org/10.5194/aab-59-445-2016.
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Abstract. In this study, we used electronic feeding station observations of pigs to investigate their growth performance and feeding behavior. These pigs were raised in Xinda Livestock Company Ltd. in Henan, China, and followed the Chinese directive for the protection of farm animals. Electronic feeding stations are often used in pig breeding and for identifying loci associated with feed consumption. Moreover, they can also provide much valuable information on pig behavior that could be directly used to improve production efficiency. This study involves three different pig population feeding-intake data from 383 Yorkshire, 243 Landrace and 197 Duroc breeds to investigate their growth performance and feeding behavior. Similar patterns of growth performance (slow–fast–slow) were observed in the three breeds, while the Duroc have a slightly faster average growth rate throughout the whole test period. Study of the number of visits to the feeding station over 24 h detected two peaks of feeding intake activity at 08:00–09:00 and 14:00–15:00. During these two peak feeding times, lower feed intake and less occupation time were observed, implicating fiercer competition at these time periods. The effective intake time for Duroc, Landrace and Yorkshire populations was 19, 16 and 19 min, respectively, suggesting that the ultimate feed intake time for each individual can be set up to 20 min for each visit. Studies on the ADFI (average daily feed intake) showed a significant difference (P value = 0.000009) between seasons and consistent patterns for these three breeds. The present study provides a detailed survey on pig feed intake behaviors across different populations and feeding seasons.
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Huang, Li, Lijia Xu, Yuchao Wang, Yingqi Peng, Zhiyong Zou, and Peng Huang. "Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism." Computational Intelligence and Neuroscience 2022 (July 16, 2022): 1–12. http://dx.doi.org/10.1155/2022/1759542.
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Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision P, the recall R, the AP (i.e., the area of P-R curve) and the MAP (i.e., the average value of AP). The experiment results show that the AP values of HE-Yolo reach 99.25%, 98.41%, 94.43%, and 97.63%, respectively, in the recognition of four pig-posture behaviors of standing, sitting, prone and sidling of the test set. Compared with other models such as Yolo v3, SSD, and faster R–CNN, the mAP value of HE-Yolo is increased by 5.61%, 4.65%, and 0.57%, respectively, and the single-frame recognition time of HE-Yolo is only 0.045 s. In the recognition of images with foreign body occlusion and pig adhesion, the mAP values of HE-Yolo are increased by 4.04%, 4.94%, and 1.76%, respectively, while compared with other models. Under different lighting conditions, the mAP value of HE-Yolo is also higher than that of other models. The experimental results show that HE-Yolo can recognize the pig-posture behaviors with high precision, and it shows good generalization ability and luminance robustness, which provides technical support for the recognition of pig-posture behaviors and real-time monitoring of physiological health of the enclosure pigs.
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Kim, Hyun-Soo, Yu Sung Edward Kim, Fania Ardelia Devira, and Mun Yong Yi. "Application of Anomaly Detection to Identifying Aggressive Pig Behaviors Using Reconstruction Loss Inversion." AgriEngineering 6, no. 4 (2024): 4442–59. http://dx.doi.org/10.3390/agriengineering6040252.
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Increasing concerns of animal welfare in the commercial pig industry include aggression between pigs as it affects their health and growth. Early detection of aggressive behaviors is essential for optimizing their living environment. A major challenge for detection is that these behaviors are observed occasionally in normal conditions. Under this circumstance, a limited amount of aggressive behavior data will lead to class imbalance issue, making it difficult to develop an effective classification model for the detection of aggressive behaviors. In order to address this issue, this study has been designed with the aim of developing an anomaly detection model for identifying aggressive behaviors in pigs, enabling better management of the imbalanced class distribution and effective detection of infrequent aggressive episodes. The model consists of a convolutional neural network (CNN) and a variational long short-term memory (LSTM) autoencoder. Additionally, we adopted a training method similar to weakly supervised anomaly detection and included a few aggressive behavior data in the training set for prior learning. To effectively utilize the aggressive behavior data, we introduced Reconstruction Loss Inversion, a novel objective function, to train the autoencoder-based model, which increases the reconstruction error for aggressive behaviors by inverting the loss function. This approach has improved detection accuracy in both AUC-ROC and AUC-PR, demonstrating a significant enhancement in distinguishing aggressive episodes from normal behavior. As a result, it outperforms traditional classification-based methods, effectively identifying aggressive behaviors in a natural pig-farming environment. This method offers a robust solution for detecting aggressive animal behaviors and contributes to improving their welfare.
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Rymut, Haley E., Courtni R. Bolt, Alexandra K. Houser, et al. "PSVIII-19 PRRSV infection during gestation eliciting changes in piglet sickness behaviors following an immune challenge." Journal of Animal Science 98, Supplement_4 (2020): 260. http://dx.doi.org/10.1093/jas/skaa278.470.
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Abstract The pork industry faces financial losses associated with outbreaks in porcine reproductive and respiratory virus (PRRSV). One type of loss stems from the effect of PRRSV-elicited maternal immune activation during gestation on the response of pigs to a second immune challenge later in life. The objective of this study was to assess the effects of PRRSV infection during gestation, a second immune challenge, and sex on pig behavior. Camborough gilts were inoculated with PRRSV or saline (Controls) during the last third of gestation. The piglets from these gilts were weaned at day 21, and were injected with Poly(I:C) to elicit a second immune challenge, similarly to a viral infection, or with Saline on day 60. Post injection, behaviors were recorded in 5-minute intervals for one hour by a trained experimenter. Recorded behaviors included laying, standing, and panting, and totaled 624 observations per behavior on 51 pigs from 10 gilts. The logistic generalized mixed effect model used to describe behavior included the effects of gilt and pig treatments, sex, and interactions, and accounted for the random effect of gilt, and repeated measurements within pig. Panting showed a significant gilt challenge-by-sex-by-pig treatment interaction effect (P &lt; 0.031). The Poly(I:C) challenge had a significant and positive effect on panting among male pigs from Control gilts (P &lt; 0.001), while this effect was not observed in pigs from PRRSV-treated gilts. Among female pigs challenged with Poly(I:C), PRRSV treatment has a significant effect on lateral laying (P &lt; 0.015) and standing (P &lt; 0.042). Pigs from PRRSV-treated gilts were less active than from Control gilts. Our results highlight that the effect on behaviors of immune challenge later in life depend on the exposure of pigs to PRRSV during gestation. This study is supported by USDA NIFA AFRI, grant number 2018-67015-27413.
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Zeng, Yaqiong, Hao Wang, Bin Hu, et al. "The Effects of Space Allowance and Toy Provision on the Growth, Spatiotemporal Distribution of Behavior, and Pen Cleanliness of Finishing Pigs." Agriculture 13, no. 7 (2023): 1277. http://dx.doi.org/10.3390/agriculture13071277.
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Excretion and lying are key behavioral factors that cause pen fouling, thereby affecting pig welfare, pathogen fecal–oral transmission, and air quality in pig housing. This study investigated the effect of space allowance and toy provision on the spatiotemporal distribution of pigs’ excreting and lying behavior, as well as the score of floor cleanliness in finishing pig pens. A total of 144 Landrace × Yorkshire × Duroc hybrid fattening pigs were randomly assigned to 12 part-slatted pens at stocking densities of 0.75, 1.05, and 1.35 m2/pig with 12 pigs per pen, and 2 pens at each density level were provided with hanging chains and rubber stars as toys. The results showed that for the average daily gain (ADG) of the pigs, the main effect of space allowance was significant (p < 0.05). The ADG at the stocking density level of 1.35 m2/pig was significantly higher than 0.75 and 1.05 m2/pig (p < 0.05). The ADG of the pigs at a density of 0.75 m2/pig in the toys group was significantly higher than the no toys group (p < 0.05). When occupied space was limited, the provision of toys was beneficial to the growth performance of the pigs. Space allowance and toy provision did not affect the time-varying regularity of the pigs but had a certain impact on the areas where the two behaviors occurred. At a density of 1.35 m2/pig, the excreting rate in the corner areas of the slatted floor and the lying rate in the middle area of the solid floor were significantly higher than at a density of 0.75 and 1.05 m2/pig (p < 0.05). Under the conditions of this study, when the stocking density was 1.35 m2/pig and toys were provided, the average daily gain of the pigs was the highest, and the pigs excreted more in the defined excretion area, lay more in the lying area, and the cleanliness of the lying area was also higher. In the case of space constraints, the provision of toys can offset some of the adverse effects of space constraints on pig growth and pen cleanliness.
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Low, Beng Ern, Yesung Cho, Bumho Lee, and Mun Yong Yi. "Playing Behavior Classification of Group-Housed Pigs Using a Deep CNN-LSTM Network." Sustainability 14, no. 23 (2022): 16181. http://dx.doi.org/10.3390/su142316181.
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The swine industry is one of the industries that progressively incorporates smart livestock farming (SLF) to monitor the grouped-housed pigs’ welfare. In recent years, pigs’ positive welfare has gained much attention. One of the evident behavioral indicators of positive welfare is playing behaviors. However, playing behavior is spontaneous and temporary, which makes the detection of playing behaviors difficult. The most direct method to monitor the pigs’ behaviors is a video surveillance system, for which no comprehensive classification framework exists. In this work, we develop a comprehensive pig playing behavior classification framework and build a new video-based classification model of pig playing behaviors using deep learning. We base our deep learning framework on an end-to-end trainable CNN-LSTM network, with ResNet34 as the CNN backbone model. With its high classification accuracy of over 92% and superior performances over the existing models, our proposed model highlights the importance of applying the global maximum pooling method on the CNN final layer’s feature map and leveraging a temporal attention layer as an input to the fully connected layer for final prediction. Our work has direct implications on advancing the welfare assessment of group-housed pigs and the current practice of SLF.
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Suazo, Isaac, Lirong Xiang, Lingjuan wang-li, Anna K. Johnson, and Suzanne M. Leonard. "201 Evaluating the impact of thermal conditions on behavior in feeder pigs." Journal of Animal Science 103, Supplement_1 (2025): 37–38. https://doi.org/10.1093/jas/skaf102.041.
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Abstract The swine industry is one of the largest livestock production systems in the world. Therefore, understanding the impact of thermal stress on animal welfare, productivity, and behavior has become crucial to support sustainability in livestock production. Different behaviors can be associated with suboptimal thermal changes in an animal’s environment, which is why the objective of this study is to use precision livestock farming techniques and technologies to collect behavioral activity and physiological measures of pig outcomes under different thermal conditions. A total of 144 feeder pigs will be housed in two rooms. Each room has 12 pens with six pigs per pen. Pigs will be exposed to thermoneutral conditions, as well as moderate heat and cold stress every week. Each thermal condition will be applied for two consecutive observation days. Each room will have its own data acquisition system and sensors will be installed to continuously monitor environmental conditions, including temperature and relative humidity. Additionally, concentrations of gases such as carbon dioxide and ammonia will be measured. Water flow meters will be placed in every pen to measure pen-level water usage. Pig surface temperature will be monitored with a thermal camera. An RGB+IR camera mounted above each pen will collect images to be labeled for detecting lying lateral, lying sternal, sitting, and standing postures, maintenance behaviors, and activities. Changes in behavior budgets will be compared across pens, thermal conditions, and pig age. Data analysis will focus on evaluating the relationships between thermal conditions and both behavioral and physiological changes. Results are expected to provide insights for enhancing animal productivity and welfare in a controlled environment. A deeper understanding of animal behavior will guide the industry toward optimizing swine production and indoor environmental conditions.
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Colpoys, Jessica D., Anna K. Johnson, and Nicholas K. Gabler. "Daily feeding regimen impacts pig growth and behavior." Physiology & Behavior 159 (May 2016): 27–32. http://dx.doi.org/10.1016/j.physbeh.2016.03.003.
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Bramich, Narelle J. "Electrical behavior of guinea pig tracheal smooth muscle." American Journal of Physiology-Lung Cellular and Molecular Physiology 278, no. 2 (2000): L320—L328. http://dx.doi.org/10.1152/ajplung.2000.278.2.l320.
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Intracellular recordings were taken from the smooth muscle of the guinea pig trachea, and the effects of intrinsic nerve stimulation were examined. Approximately 50% of the cells had stable resting membrane potentials of −50 ± 1 mV. The remaining cells displayed spontaneous oscillations in membrane potential, which were abolished either by blocking voltage-dependent Ca2+channels with nifedipine or by depleting intracellular Ca2+stores with ryanodine. In quiescent cells, stimulation with a single impulse evoked an excitatory junction potential (EJP). In 30% of these cells, trains of stimuli evoked an EJP that was followed by oscillations in membrane potential. Transmural nerve stimulation caused an increase in the frequency of spontaneous oscillations. All responses were abolished by the muscarinic-receptor antagonist hyoscine (1 μM). In quiescent cells, nifedipine (1 μM) reduced EJPs by 30%, whereas ryanodine (10 μM) reduced EJPs by 93%. These results suggest that both the release of Ca2+ from intracellular stores and the influx of Ca2+ through voltage-dependent Ca2+channels are important determinants of spontaneous and nerve-evoked electrical activity of guinea pig tracheal smooth muscle.
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Pissarenko, Andrei, Wen Yang, Haocheng Quan, et al. "Tensile behavior and structural characterization of pig dermis." Acta Biomaterialia 86 (March 2019): 77–95. http://dx.doi.org/10.1016/j.actbio.2019.01.023.
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Yang, Qiumei, and Deqin Xiao. "A review of video-based pig behavior recognition." Applied Animal Behaviour Science 233 (December 2020): 105146. http://dx.doi.org/10.1016/j.applanim.2020.105146.
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Zhang, Kaining, Yu Zhang, and Hongli Xu. "Lightweight Domestic Pig Behavior Detection Based on YOLOv8." Applied Sciences 15, no. 11 (2025): 6340. https://doi.org/10.3390/app15116340.
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The prevalence and magnitude of extensive domestic pig breeding in China are rising, and behavioral assessment of these pigs is essential for enhancing production efficiency. The existing behavior identification technique for domestic pigs is computationally intensive, making it challenging to use on edge devices. This study introduces a lightweight method for identifying domestic pig behavior, YOLOv8-PigLite, derived from YOLOv8. Initially, a novel two-branch bottleneck module is developed within the C2f module, incorporating average pooling and deep convolution (DWConv) in one branch, while the other branch utilizes maximum pooling and DWConv to augment multi-scale feature representation. Subsequently, a Grouped Convolution module is integrated into the convolution framework, followed by incorporating the SE module to diminish the recognition error rate further. Ultimately, we implement BiFPN in the neck network to replace the original FPN, which streamlines the neck network and enhances its feature-processing capabilities. The test findings indicated that, in comparison to the original YOLOv8n model, the precision, recall, and mean average precision at 50% remain constant, while the parameters and floating-point computations are diminished by 59.80% and 39.50%, respectively. Additionally, the FPS has increased by 32.61%, and the model’s generalizability has been validated on public datasets.
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Giang, Nguyen Thi Huong, Tran Cong Chinh, Tran Duc Vien, Nguyen Tat Thang, and Ngo The An. "Applying the Theory of Planned Behavior to Determine the Influencing Factors of Recycling Pig Wastewater for Crop Cultivation in Hanoi City." Vietnam Journal of Agricultural Sciences 6, no. 2 (2023): 1810–20. http://dx.doi.org/10.31817/vjas.2023.6.2.06.
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The study was conducted to analyze the factors affecting the use of pig wastewater for crop cultivation in Hanoi city. Through the application of the Theory of Planned Behavior to develop the theoretical framework and structural equation modeling, the study showed that the behavior intention influenced the recycling behavior. Attitude toward the behavior (AT), social norms (SN), and perceived behavioral control (PBC) were important influencing factors on the intention to perform this behavior with standardized regression coefficients of ß = 0.96 (P <0.001), ß = -0.826 (P <0.001), and ß = -0.34 (P <0.001), respectively. These research results serve as the initial reference for policies and studies related to the use of pig wastewater.
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Hamant, Kumar Hamant. "pigeta-CLOSED SETS IN BITOPOLOGICAL SPACES." EPRA International Journal of Research and Development (IJRD) 7, no. 6 (2022): 313–20. https://doi.org/10.5281/zenodo.14880980.
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In this paper, a new class of sets namely pigη-closed, pigη-closure of a set and pigη-neighbourhood in bitopological spaces are introduced and some of their basic properties are discussed. The relationships among closed, alpha-closed, s-closed, ηclosed, gη-closed and other generalized closed sets are investigated. Several examples are provided to illustrate the behavior of these new class of sets.
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Liu, Xingqiao, Jun Xuan, Fida Hussain, Chen Chong, and Pengyu Li. "ARM-based Behavior Tracking and Identification System for Grouphoused Pigs." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 12, no. 6 (2019): 554–65. http://dx.doi.org/10.2174/2352096512666190329230400.
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Background: A smart monitoring system is essential to improve the quality of pig farming. A real-time monitoring system provides growth, health and food information of pigs while the manual monitoring method is inefficient and produces stress on pigs, and the direct contact between human and pig body increases diseases. Methods: In this paper, an ARM-based embedded platform and image recognition algorithms are proposed to monitor the abnormality of pigs. The proposed approach provides complete information on in-house pigs throughout the day such as eating, drinking, and excretion behaviors. The system records in detail each pig's time to eat and drink, and the amount of food and water intake. Results: The experimental results show that the accuracy of the proposed method is about 85%, and the effect of the technique has a significant advantage over traditional behavior detection methods. Conclusion: Therefore, the ARM-based behavior recognition algorithm has certain reference significance for the fine group aquaculture industry. The proposed approach can be used for a central monitoring system.
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Yolanda, Natasha Ayalus Yoan Yola, and Barli Bram. "Positive Politeness Strategies Used by Parents in Peppa Pig Official YouTube Channel." ELS Journal on Interdisciplinary Studies in Humanities 5, no. 4 (2022): 625–32. http://dx.doi.org/10.34050/elsjish.v5i4.24140.
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Parents are the first ones responsible for their children’s speech. Bad and good children’s speech behavior in society would be considered the results of their parents’ teachings. This research was conducted to analyze this phenomenon by exploring the positive politeness used by parents while communicating in front of their children. It showed how parents construct their speech to exemplify good speech behavior to their children. This research used qualitative research, specified to content analysis method to analyze the data. The data were collected from the video entitled “Peppa Pig Official Channel, Peppa Pig Adds Pineapple in Her Pizza” from Peppa Pig Official YouTube Channel. The results showed that the most strategy used was the ‘include both speakers and hearers in the activity’ which appeared in 36 out of 319 utterances. It showed Daddy Pig’s and Mommy Pig’s efforts to promote the politeness of appreciating others’ existence through their speech to their children. Furthermore, all the positive politeness strategies were used by Daddy Pig and Mommy Pig as the parents in Peppa Pig Video. Therefore, this video was recommended for parents to see how they can construct their language politely while speaking with their children. It will help children to get used to speaking politely and appropriately.
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Wei, Jiacheng, Xi Tang, Jinxiu Liu, and Zhiyan Zhang. "Detection of Pig Movement and Aggression Using Deep Learning Approaches." Animals 13, no. 19 (2023): 3074. http://dx.doi.org/10.3390/ani13193074.
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Motion and aggressive behaviors in pigs provide important information for the study of social hierarchies in pigs and can be used as a selection indicator for pig health and aggression parameters. However, relying only on visual observation or surveillance video to record the number of aggressive acts is time-consuming, labor-intensive, and lasts for only a short period of time. Manual observation is too short compared to the growth cycle of pigs, and complete recording is impractical in large farms. In addition, due to the complex process of assessing the intensity of pig aggression, manual recording is highly influenced by human subjective vision. In order to efficiently record pig motion and aggressive behaviors as parameters for breeding selection and behavioral studies, the videos and pictures were collected from typical commercial farms, with each unit including 8~20 pigs in 7~25 m2 space; they were bred in stable social groups and a video was set up to record the whole day’s activities. We proposed a deep learning-based recognition method for detecting and recognizing the movement and aggressive behaviors of pigs by recording and annotating head-to-head tapping, head-to-body tapping, neck biting, body biting, and ear biting during fighting. The method uses an improved EMA-YOLOv8 model and a target tracking algorithm to assign a unique digital identity code to each pig, while efficiently recognizing and recording pig motion and aggressive behaviors and tracking them, thus providing statistics on the speed and duration of pig motion. On the test dataset, the average precision of the model was 96.4%, indicating that the model has high accuracy in detecting a pig’s identity and its fighting behaviors. The model detection results were highly correlated with the manual recording results (R2 of 0.9804 and 0.9856, respectively), indicating that the method has high accuracy and effectiveness. In summary, the method realized the detection and identification of motion duration and aggressive behavior of pigs under natural conditions, and provided reliable data and technical support for the study of the social hierarchy of pigs and the selection of pig health and aggression phenotypes.
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Titahena, Brian S., George S. J. Tomatala, and Pieter M. Ririmase. "Analisis Perilaku Peternak Dalam Pengelolaan Ternak Babi Pada Peternakan Rakyat Di Desa Tawiri." Jurnal Agrosilvopasture-Tech 3, no. 1 (2024): 56–65. http://dx.doi.org/10.30598/j.agrosilvopasture-tech.2024.3.1.56.
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This research aims to analyze the behavior of farmers in managing pigs among smallholder farmers in Tawiri village. The research variables are farmer characteristics (age, education, main job, number of family dependents), and business characteristics (livestock ownership, length of business). The main variables in this research are as follows: knowledge, attitudes, and actions towards managing pig farms. The research results show that the behavior of pig farmers at the level of knowledge in managing the business is in a good category, namely 83.33%, where pig farmers in Tawiri village have good knowledge in managing the livestock business. Meanwhile, at the attitude level, breeders in Tawiri village are in a good category with a percentage of 70%, this figure shows the positive attitude of breeders in running their business. while the level of farmer action is in the sufficient category with a percentage of 66.67%, meaning that the farmer's action is to implement various technical actions in managing the pig farming business.
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De Zee, M., F. Bojsen-Møller, and M. Voigt. "Dynamic viscoelastic behavior of lower extremity tendons during simulated running." Journal of Applied Physiology 89, no. 4 (2000): 1352–59. http://dx.doi.org/10.1152/jappl.2000.89.4.1352.
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The aim of this project was to see whether the tendon would show creep during long-term dynamic loading (here referred to as dynamic creep). Pig tendons were loaded by a material-testing machine with a human Achilles tendon force profile (1.37 Hz, 3% strain, 1,600 cycles), which was obtained in an earlier in vivo experiment during running. All the pig tendons showed some dynamic creep during cyclic loading (between 0.23 ± 0.15 and 0.42 ± 0.21%, means ± SD). The pig tendon data were used as an input of a model to predict dynamic creep in the human Achilles tendon during running of a marathon and to evaluate whether there might consequently be an influence on group Ia afferent-mediated length and velocity feedback from muscle spindles. The predicted dynamic creep in the Achilles tendon was considered to be too small to have a significant influence on the length and velocity feedback from soleus during running. In spite of the characteristic nonlinear viscoelastic behavior of tendons, our results demonstrate that these properties have a minor effect on the ability of tendons to act as predictable, stable, and elastic force transmitters during long-term cyclic loading.