Search Results (79)

Mastitis, an inflammatory disease caused by the invasion of various pathogenic microorganisms into mammary gland tissue, is a core health issue plaguing the global dairy industry. The consequences of this disease are manifold. In addition to directly compromising the health and welfare of dairy cows, it also precipitates a substantial decline in lactation function, a precipitous drop in raw milk production, and alterations in milk composition (e.g., increased somatic cell counts and imbalanced ratios of milk protein to fat). These changes result in a marked degradation of milk quality and safety, and in turn, engender significant economic losses for the livestock industry. Therefore, the establishment and implementation of a comprehensive prevention and control system is a key strategy to effectively curb the occurrence of mastitis, reduce its incidence rate, and minimise economic losses. This review systematically explores the complex etiological factors and pathogenic mechanisms of mastitis in dairy cows, and summarises various diagnostic methods, including milk apparent indicators monitoring, pathogen detection, physiological parameter monitoring, omics technologies, and emerging technologies. Furthermore, it undertakes an analysis of treatment protocols for mastitis in dairy cows, with a particular emphasis on the significance of rational antibiotic use and alternative therapies. Moreover, it delineates preventive measures encompassing both environmental and hygiene management, and dairy cow health management. The objective of this paper is to provide a comprehensive and scientific theoretical basis and practical guidance for dairy farming practices. This will help to improve the health of dairy cows, ensure a stable supply of high-quality dairy products, and promote the sustainable and healthy development of the dairy farming industry. Full article

The ‘head back’ posture is a pronounced and significant behavioral trait during bovine parturition, commonly interpreted as a natural response to the pain associated with parturition. Leveraging computer vision technology for real-time monitoring of parturition behaviors can provide timely assistance during calving and enhance animal welfare. This study initially evaluated the head back posture in cows of different types, finding that primiparous cows and those delivering calves weighing over 43 kg exhibited prolonged durations of both labor and head back posture. A model was developed using the YOLOv8 algorithm with 25,617 images to recognize and classify changes in head posture during parturition, including positions like lying with or without head back. The model demonstrated robust predictive performance with a precision (P) of 69.76%, recall (R) of 75.35%, average precision (AP) of 70.12%, and F₁ score of 0.71. Furthermore, the model’s capability to recognize postures from different camera angles and under varying environmental conditions was assessed. Notably, images captured from an abdominal angle achieved AP exceeding 90%, with consistent stability under varying lighting conditions, including sunny and overcast weather, during both daytime and nighttime. Behavioral analysis showed that the parturition duration and total duration of head back posture in primiparous cows were significantly higher than those in multiparous cows (p < 0.05), and the changing trends of motor performance between primiparous and multiparous cows were consistent across different parturition stages. Additionally, the correlation between calf birth weight and maternal behavior was stronger in primiparous cows than in multiparous cows, further indicating obvious differences in physiological and behavioral responses of cows during primiparous and multiparous parturition. This study underscores the potential of computer vision applications in enhancing real-time intervention and promoting welfare during bovine parturition. Full article

Timely, objective pain recognition in dairy cattle is essential for welfare assurance, productivity, and ethical husbandry yet remains elusive because evolutionary pressure renders bovine distress signals brief and inconspicuous. Without verbal self-reporting, cows suppress overt cues, so automated vision is indispensable for on-farm triage. Although earlier systems tracked whole-body posture or static grimace scales, frame-level detection of facial micro-expressions has not been explored fully in livestock. We translate micro-expression analytics from automotive driver monitoring to the barn, linking modern computer vision with veterinary ethology. Our two-stage pipeline first detects faces and 30 landmarks using a custom You Only Look Once (YOLO) version 8-Pose network, achieving a 96.9% mean average precision ($mAP$) at an Intersection over the Union (IoU) threshold of 0.50 for detection and 83.8% Object Keypoint Similarity (OKS) for keypoint placement. Cropped eye, ear, and muzzle patches are encoded using a pretrained MobileNetV2, generating 3840-dimensional descriptors that capture millisecond muscle twitches. Sequences of five consecutive frames are fed into a 128-unit Long Short-Term Memory (LSTM) classifier that outputs pain probabilities. On a held-out validation set of 1700 frames, the system records 99.65% accuracy and an F1-score of 0.997, with only three false positives and three false negatives. Tested on 14 unseen barn videos, it attains 64.3% clip-level accuracy (i.e., overall accuracy for the whole video clip) and 83% precision for the pain class, using a hybrid aggregation rule that combines a 30% mean probability threshold with micro-burst counting to temper false alarms. As an early exploration from our proof-of-concept study on a subset of our custom dairy farm datasets, these results show that micro-expression mining can deliver scalable, non-invasive pain surveillance across variations in illumination, camera angle, background, and individual morphology. Future work will explore attention-based temporal pooling, curriculum learning for variable window lengths, domain-adaptive fine-tuning, and multimodal fusion with accelerometry on the complete datasets to elevate the performance toward clinical deployment. Full article

With the development of precision livestock farming, in order to achieve the goal of fine management and improve the health and welfare of dairy cows, research on dairy cow motion monitoring has become particularly important. In this study, considering the problems surrounding a large amount of model parameters, the poor accuracy of multi-target tracking, and the nonlinear motion of dairy cows in dairy farming scenes, a lightweight detection model based on improved YOLO v11n was proposed and four tracking algorithms were compared. Firstly, the Ghost module was used to replace the standard convolutions in the YOLO v11n network and a more lightweight attention mechanism called ELA was replaced, which reduced the number of model parameters by 18.59%. Then, a loss function called SDIoU was used to solve the influence of different cow target sizes. With the above improvements, the improved model achieved an increase of 2.0 percentage points and 2.3 percentage points in mAP@75 and mAP@50-95, respectively. Secondly, the performance of four tracking algorithms, including ByteTrack, BoT-SORT, OC-SORT, and BoostTrack, was systematically compared. The results show that 97.02% MOTA and 89.81% HOTA could be achieved when combined with the OC-SORT tracking algorithm. Considering the demand of equipment in lightweight models, the improved object detection model in this paper reduces the number of model parameters while offering better performance. The OC-SORT tracking algorithm enables the tracking and localization of cows through video surveillance alone, creating the necessary conditions for the continuous monitoring of cows. Full article

Accurate body condition score (BCS) monitoring in dairy cows is essential for optimizing health, productivity, and welfare. Traditional manual scoring methods are labor-intensive and subjective, driving interest in automated imaging-based systems. This study evaluated the effectiveness of 2D imaging and deep learning for BCS classification using three camera perspectives—front, back, and top-down—to identify the most reliable viewpoint. The research involved 56 Norwegian Red milking cows at the Center for Livestock Experiments (SHF) of Norges Miljo-og Biovitenskaplige Universitet (NMBU) in Norway. Images were classified into BCS categories of 2.5, 3.0, and 3.5 using a YOLOv8 model. The back view achieved the highest classification precision (mAP@0.5 = 0.439), confirming that key morphological features for BCS assessment are best captured from this angle. Challenges included misclassification due to overlapping features, especially in Class 2.5 and background data. The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. Integration with precision farming tools enables continuous monitoring and early detection of health issues. This research highlights the potential of 2D imaging as a cost-effective alternative to 3D systems, particularly for small and medium-sized farms, supporting more effective herd management and improved animal welfare. Full article

Monitoring parturient cattle during calving is crucial for reducing cow and calf mortality, enhancing reproductive and production performance, and minimizing labor costs. Traditional monitoring methods include direct animal inspection or the use of specialized sensors. These methods can be effective, but impractical in large-scale ranching operations due to time, cost, and logistical constraints. To address this challenge, a network of low-power and long-range IoT sensors combining the Global Navigation Satellite System (GNSS) and tri-axial accelerometers was deployed to monitor in real-time 15 parturient Brangus cows on a 700-hectare pasture at the Chihuahuan Desert Rangeland Research Center (CDRRC). A two-stage machine learning approach was tested. In the first stage, a fully connected autoencoder with time encoding was used for unsupervised detection of anomalous behavior. In the second stage, a Random Forest classifier was applied to distinguish calving events from other detected anomalies. A 5-fold cross-validation, using 12 cows for training and 3 cows for testing, was applied at each iteration. While 100% of the calving events were successfully detected by the autoencoder, the Random Forest model failed to classify the calving events of two cows and misidentified the onset of calving for a third cow by 46 h. The proposed framework demonstrates the value of combining unsupervised and supervised machine learning techniques for detecting calving events in rangeland cattle under extensive management conditions. The real-time application of the proposed AI-driven monitoring system has the potential to enhance animal welfare and productivity, improve operational efficiency, and reduce labor demands in large-scale ranching. Future advancements in multi-sensor platforms and model refinements could further boost detection accuracy, making this approach increasingly adaptable across diverse management systems, herd structures, and environmental conditions. Full article

This review critically analyzes recent advancements in dairy cow behavior recognition, highlighting novel methodological contributions through the integration of advanced artificial intelligence (AI) techniques such as transformer models and multi-view tracking with social network analysis (SNA). Such integration offers transformative opportunities for improving dairy cattle welfare, but current applications remain limited. We describe the transition from manual, observer-based assessments to automated, scalable methods using convolutional neural networks (CNNs), spatio-temporal models, and attention mechanisms. Although object detection models, including You Only Look Once (YOLO), EfficientDet, and sequence models, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Long Short-Term Memory (convLSTM), have improved detection and classification, significant challenges remain, including occlusions, annotation bottlenecks, dataset diversity, and limited generalizability. Existing interaction inference methods rely heavily on distance-based approximations (i.e., assuming that proximity implies social interaction), lacking the semantic depth essential for comprehensive SNA. To address this, we propose innovative methodological intersections such as pose-aware SNA frameworks and multi-camera fusion techniques. Moreover, we explicitly discuss ethical challenges and data governance issues, emphasizing data transparency and animal welfare concerns within precision livestock contexts. We clarify how these methodological innovations directly impact practical farming by enhancing monitoring precision, herd management, and welfare outcomes. Ultimately, this synthesis advocates for strategic, empathetic, and ethically responsible precision dairy farming practices, significantly advancing both dairy cow welfare and operational effectiveness. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

Precision Livestock Farming (PLF) applies a complex of sensor technology, algorithms, and multiple tools for individual, real-time livestock monitoring. In intensive livestock systems, PLF is now quite widespread, allowing for the optimisation of management, thanks to the early recognition of diseases and the possibility of monitoring animals’ feeding and reproductive behaviour, with an overall improvement of their welfare. Similarly, PLF systems represent an opportunity to improve the profitability and sustainability of extensive farming systems, including those of small ruminants, rationalising the use of pastures by avoiding overgrazing and controlling animals. Despite the livestock distribution in several parts of the world, the low profit and the relatively high cost of the devices cause delays in implementing PLF systems in small ruminants compared to those in dairy cows. Applying these tools to animals in extensive systems requires customisation compared to their use in intensive systems. In many cases, the unit prices of sensors for small ruminants are higher than those developed for large animals due to miniaturisation and higher production costs associated with lower production numbers. Sheep and goat farms are often in mountainous and remote areas with poor technological infrastructure and ineffective electricity, telephone, and internet services. Moreover, small ruminant farming is usually associated with advanced age in farmers, contributing to poor local initiatives and delays in PLF implementation. A targeted literature analysis was carried out to identify technologies already applied or at an advanced stage of development for the management of grazing animals, particularly sheep and goats, and their effects on nutrition, production, and animal welfare. The current technological developments include wearable, non-wearable, and network technologies. The review of the technologies involved and the main fields of application can help identify the most suitable systems for managing grazing sheep and goats and contribute to selecting more sustainable and efficient solutions in line with current environmental and welfare concerns. Full article

Face recognition and identification is a method that is well established in traffic monitoring, security, human biodata analysis, etc. Regarding the current development and implementation of digitalization in all spheres of public life, new approaches are being sought to use the opportunities of high technology advancements in animal husbandry to enhance the sector’s sustainability. Using machine learning the present study aims to investigate the possibilities for the creation of a model for visual face recognition of farm animals (cows) that could be used in future applications to manage health, welfare, and productivity of the animals at the herd and individual levels in real-time. This study provides preliminary results from an ongoing research project, which employs attribution methods to identify which parts of a facial image contribute most to cow identification using a triplet loss network. A new dataset for identifying cows in farm environments was therefore created by taking digital images of cows at animal holdings with intensive breeding systems. After normalizing the images, they were subsequently segmented into cow and background regions. Several methods were then explored for analyzing attributions and examine whether the cow or background regions have a greater influence on the network’s performance and identifying the animal. Full article

Devices for assessing the quality of animal environments are important for maintaining production animals, thus improving animal well-being and mitigating pollutant emissions. Therefore, an IoT system was developed and preliminarily assessed across various livestock housing types, including those for pigs, dairy cows, and rabbits. This system measures and transmits key parameters, such as ambient temperature; relative humidity; light intensity; sound pressure; levels of carbon dioxide, ammonia, and hydrogen sulfide; and particulate matter and volatile organic compound concentrations. These data are sent from the sensors to a gateway and then displayed on a dashboard for monitoring. A preliminary evaluation of the system’s performance in controlled conditions revealed that the device’s accuracy and precision were within 2.7% and 3.3% of the measured values, respectively. The system was deployed in three case studies involving rabbit, pig, and dairy cow farms. The results demonstrated its effectiveness in assessing pollutant emissions and identifying critical situations where gas concentrations exceeded threshold levels, thus posing a risk to the animals. By systematically applying this technology on livestock farms to obtain a detailed understanding of the microclimatic and air quality conditions in which the animals live, animal welfare can be significantly improved. Full article

The implementation of new technologies and best practices on factory farms is crucial for reducing the environmental pollution burden. This study aimed to evaluate the use of natural sorbents in the cows’ diet to reduce gaseous pollutants released on the farm and the content of faecal biogenic compounds. To this end, a mixture of natural sorbents (65% beechwood biochar, 25% aluminosilicate, and 10% glycerine) was added to cows’ feed. Pollutants released from all groups of cows were continually monitored on the farm during the experiment. Blood samples were also collected from the cows for haematological and biochemical analysis to determine the sorbents’ impact on their health. The average level of gaseous pollutants in the air on the farm were highest in the control group and lowest in the experimental groups. The levels of NH₃, CH₄, and H₂S were statistically significant at p < 0.05. The results demonstrated that the added sorbents effectively reduced gaseous pollutants without adversely affecting the health of cows. Natural additives in the cows’ diet, including sorbents, bind harmful substances such as NH₃ and CH₄, which are common gaseous by-products of digestion. This leads to improvements in animal welfare and the natural environment. Full article

The aim of this research was to assess welfare indicators in different dairy cow management systems, determine the prevalence of parasitic infections, and examine the impact of these infections on welfare indicators. This study was conducted in 2024 on 45 Holstein-Friesian cows aged 2 to 6 years (first to third lactation) in Northern Serbia. Monitoring was carried out in tie stall, loose, and pasture-based systems, covering three production phases: late dry period, clinical puerperium, and peak lactation. Cow welfare was evaluated using the Welfare Quality^® protocol, and parasitological diagnostics from fecal samples. Identified welfare issues included a low body condition score (BCS), dirtiness of udders, flanks, and legs, integument alterations, nasal and ocular discharge, lameness, and diarrhea. Cows in the pasture-based system had significantly higher scores for dirtiness (p < 0.001), while those in tie stalls showed more integument alterations (p < 0.001). Loose-housed cows had higher nasal discharge scores (p < 0.001). Parasites identified included Eimeria spp., Buxtonella sulcata, gastrointestinal strongylids, Moniezia spp., Dicrocoelium dendriticum, Fasciola hepatica, and Paramphistomum spp. Significant correlations (p < 0.001) were found between certain welfare indicators and parasite infections, such as a low BCS with Eimeria oocysts and nasal discharge and hairless patches with Buxtonella sulcata and Dicrocoelium dendriticum. These data indicate needs for improving dairy cows’ welfare and the implementation of effective parasite control measures in all housing systems. Full article

The objective of the study was to examine the relationship between rumination time and various parameters related to eating and locomotion, including other chews, eating chews, eating time, drinking gulps, bolus counts, chews per minute, activity, and activity change, utilizing RumiWatch technology. The RumiWatch noseband sensor (RWS; ITIN + HOCH GmbH, Feeding Technology, Liestal, Switzerland) was utilized to record time and frequency related to rumination, eating, and movement behaviors. The RumiWatch system (RWS) was put into operation from 1 June 2023 to 30 June 2023. The first two weeks, from 1 June to 14 June 2023 at 7 a.m., served as a period for the cows to acclimate to the RWS, acting as an adjustment phase. Monitoring activities with the RWS commenced on 7 a.m. and lasted until the end of the month, 30 June 2023, with data being recorded daily on an hourly basis. Our findings indicate a significant negative correlation between rumination time and other activity time (r = −0.50), which represents the duration cows allocate to behaviors outside of eating, chewing cud, or distinct movement activities. Additionally, a significant negative correlation was observed between rumination time and eating time (r = −0.54). Furthermore, we observed strong positive correlations with rumination chews (r = 0.84) and bolus (r = 0.75). A weaker positive correlation was found with chews per minute (r = 0.29), while no significant correlation was detected with drinking gulps (r = 0.10). Based on our findings, we recommend the implementation of the RumiWatch System for monitoring rumination and feeding behaviors in lactating dairy cattle. This technology provides valuable insights into cow health and welfare, enabling early detection of potential health issues and improving herd management practices. Full article

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout early lactation (up to 100 days postpartum), utilising a 1-to-5 scale. Lameness was found to significantly impact both rumination and milk production. On the day of diagnosis, rumination time decreased by 26.64% compared to the pre-diagnosis period (p < 0.01) and by 26.06% compared to healthy cows, indicating the potential of rumination as an early health indicator. The milk yield on the day of diagnosis was 28.10% lower compared to pre-diagnosis levels (p < 0.01) and 40.46% lower than healthy cows (p < 0.05). These findings suggest that lameness manifests prior to clinical signs, affecting productivity and welfare. Milk composition was also influenced, with lame cows exhibiting altered fat (+0.68%, p < 0.05) and lactose (−2.15%, p < 0.05) content compared to healthy cows. Positive correlations were identified between rumination time and milk yield (r = 0.491, p < 0.001), while negative correlations were observed between milk yield and milk fat, protein, and the fat-to-protein ratio (p < 0.001). Additionally, lameness was associated with elevated somatic cell counts in the milk, although sample size limitations necessitate further validation. This study highlights the critical role of rumination and milk performance metrics in identifying subclinical lameness, emphasising the utility of automated systems in advancing dairy cow welfare and productivity. The findings underscore the importance of early detection and management strategies to mitigate the economic and welfare impacts of lameness in dairy farming. Full article