Cattle Face Research Articles

Prevalence of Foot and Mouth Disease in Cattle of District Buner, Pakistan

Background: Foot and Mouth disease (FMD) is infectious disease in cattle caused by a virus of the family Picornaviridae, also known as Rhino virus. The disease is characterized by large fever, appears lesions in the buccal cavity due to lesions in buccal mucosa the cattle face the difficulties in feeding. Affected animals have a high temperature, which is followed by the development of blisters chiefly in the mouth and on the feet. This disease cause loss of appetite, loss of weight, reduce milk production, miscarriage also occurs in this disease and sometime it may lead to death mostly in young ones. Objective: This study aimed to assess the Prevalence of Foot and mouth disease in district Buner. Methods: The present study was conducted on the Prevalence of Foot and Mouth disease in district Buner, Pakistan during August 2023 to August 2024. During the outbreak we were visited to the local community, DVM doctors. Livestock office of district Buner, veterinary assistance, Doctors etc. In this outbreak large number of economic loses were reported of FMD among the farmers community. Results: During the study a total of (n=3565) cases of animals (cattle) of Foot and mouth disease were reported, in which (n=439) (12.3%) death cases were reported. The total recovered cases of Foot and Mouth disease were (n=2817). During the outbreak there are around (36612) cattle were vaccinated in district Buner. The study area was divided into six different tehsils. Conclusion: The most affected tehsil during the outbreak was Mandanr. Tehsil Gadezi are highly effected in which (n=1105) cases were reported with mortality rate of (n=9.5%). The most rear effected tehsil was Gagra in which only (n=286) cases of FMD were reported with mortality rate (n=17.4%).

Deep Learning based Individual Cattle Face Recognition using Data Augmentation and Transfer Learning

Monitoring animal welfare, disease prevention, vaccination administration, production supply, and ownership management all depend on accurate cattle identification. Ear tag-based cattle identification techniques are frequently used in livestock farm management. These are not used to identify specific cattle on large-scale farms. However, ear tags can come off, which makes it challenging to identify a particular individual. Ear tags are susceptible to fraud, can be copied, and run the risk of being damaged. Long-term animal identification is impossible with lost tags. For this purpose, a data set was created by taking images of cattle in their natural environment. The dataset, which contains 15,000 records from 30 different cattle, was divided into three sections: training, validation, and testing. Deep learning algorithms InceptionResNetV2, MobilenetV2, DenseNet201, Xception, and NasNetLarge were used in this study to identify specific cattle faces. The DenseNet201 algorithm achieved the highest test accuracy of 99.53% with a validation accuracy of 99.83%.

Boosting cattle face recognition under uncontrolled scenes by embedding enhancement and optimization

Accurate individual cattle identification is crucial for modern precision cattle farming. However, practical applications encounter challenges due to factors such as shooting distance and angles, cattle movements, weather conditions, and cattle face posture. Existing models struggle with low recognition accuracy for low-recognizable images and poor robustness to facial pose variations, presenting urgent problems that need to be addressed. In this study, a novel cattle face recognition method was proposed, aiming to solve the aforementioned issues by analyzing and optimizing the embedding distribution of cattle faces. Firstly, MobileFaceNet was employed as the feature extraction network to sufficiently extract discriminative representations. Secondly, due to the tendency of low-recognizable samples to cluster in specific areas of the embedding space, an Embedding Enhancement Module (EEM) was proposed. This module drives embedding features away from ineffective embedding spaces, thereby enhancing the model's ability to extract identity features from less recognizable cattle faces. Finally, an Embedding Optimization Module (EOM) was proposed, which utilized a Sub-Center method to alleviate the model's learning difficulty during early training stages and achieved "pose-irrelevant" clusters by merging Sub-Centers, enhancing robustness to facial pose variations. Experimental results confirmed the effectiveness of the proposed method, with it outperforming the baseline by up to 2.12 % in accuracy, achieving a recognition accuracy of 98.69 %. Furthermore, the application of the proposed method in cattle farms further demonstrated the significant potential value of this work.

Analysis of the Management of Cereal Crop Residues on Farms to Optimise Cattle Feed in the Western Cotton Growing Zone of Burkina Faso

Crop residues are insufficiently valorised by farmers in western Burkina Faso, while cattle face a shortage of forage. The aim of this study was to analyse the management of cereal crop residues in cotton-cereal farming to optimise their use as feed for draught cattle. A sample of 72 cotton farmers was selected from nine villages in the study area by the stratification method and interviewed using a semi-structured questionnaire. The quantities of cereal crop residue biomass collected by farmers were assessed directly in the field. The results showed that maize and sorghum stalks were predominantly used in cattle feed (86.1 and 90.3%), composting (50 and 44.4%), and mulching (26.9 and 25%), respectively. The average quantities of cereal crop residues stored were 0.83, 0.16, and 1.19 tonnes of dry matter (tDM) for crop farmers, livestock breeders, and agro-pastoralists, respectively, with respective fodder balances of -3.99, -29.05, and -16.99 Tropical Cattle Units (TCU) for the use of cattle, or -0.88, -0.38, and -1.81 TCU for the use of draught cattle during the lean season. Concentrated feed use was 0.20, 0.05, and 0.21 kg/cattle/day; respectively. The main factors influencing the quantities of cereal residues stored on the farms were the size of the cultivated areas and the farmers’ capacity for transportation. Provision of transport equipment and storage infrastructure would therefore appear to be a solution that could improve the level of fodder residue storage, particularly for draught oxen.

Cattle face detection method based on channel pruning YOLOv5 network and mobile deployment

The detection and identification of individual cattle plays an integral role in precision feeding and insurance claims, among others. Most current research is based on high-performance computing devices, which limits the application of deep learning techniques to mobile terminals. To this end, in this paper, we propose a channel-pruned YOLOv5 network-based method for cattle face detection on mobile terminals, referred to as NS-YOLO. First, the original model is sparsely trained and a sparse regularization penalty term is applied to the BN layers, then the corresponding mask values are labeled according to different weight thresholds, and the channels are pruned with global thresholds. Second, the detection precision is recovered by fine-tuning the model. Finally, the NCNN forward inference framework is used to quantize the model and an Android-based cattle face detection application is developed. Experimental results show that the model size, number of parameters and FLOPs are reduced by 86.10%, 88.19% and 63.25%, respectively, and the inference time is reduced by 35.53% compared to the original model, while mAP0.5 is reduced by only 1.6%. In particular, the 16-bit quantized model reduces the model size by 93.97% and the inference time by 34.97% compared to the original model on the mobile side. The results show that the proposed method can be easily deployed in resource-constrained mobile devices and has great advantages in practical engineering applications.

Research on 3D reconstruction method of cattle face based on image

As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features.

Comparing thermal conditions inside and outside lactating dairy cattle barns in Canada

The health, longevity, and performance of dairy cattle can be adversely affected by heat stress. This study evaluated the in-barn condition [i.e., temperature, relative humidity, and resulting temperature-humidity index (THI)] at 9 dairy barns with various climates and farm design-management combinations. Hourly and daily indoor and outdoor conditions were compared at each farm, including both mechanically and naturally ventilated barns. On-site conditions were compared with on-farm outdoor conditions, meteorological stations up to 125 km away, and NASA Power data. Canadian dairy cattle face periods of extreme cold and periods of high THI, dependent on the regional climate and season. The northernmost location (53°N) experienced about 75% fewer hours of THI >68 compared with the southernmost location (42°N). Milking parlors had higher THI than the rest of the barn during milking times. The THI conditions inside dairy barns were well correlated with THI conditions measured outside the barns. Naturally ventilated barns with metal roofs and without sprinklers fit a linear relationship (hourly and daily means) with a slope <1, indicating that in-barn THI exceeded outdoor THI more at lower THI and reached equality at higher THI. Mechanically ventilated barns fit nonlinear relationships, which showed the in-barn THI exceeded outdoor THI more at lower THI (e.g., 55-65) and approached equality at higher THI. In-barn THI exceedance was greater in the evening and overnight due to factors such as decreased wind speed and latent heat retention. Eight regression equations were developed (4 hourly, 4 daily) to predict in-barn conditions based on outdoor conditions, considering different barn designs and management systems. Correlations between in-barn and outdoor THI were best when using the on-site weather data from the study, but publicly available weather data from stations within 50 km provided reasonable estimates. Climate stations 75 to 125 km away and NASA Power ensemble data gave poorer fit statistics. For studies involving many dairy barns, the use of NASA Power data with equations for estimating average in-barn conditions in a population is likely appropriate especially when public stations have incomplete data. Results from this study show the importance of adapting recommendation on heat stress to the barn design and guide the selection of appropriate weather data depending on the aim of the study.

Immunotolerance of dairy heifers in response to repeated exposure to bacterial lipopolysaccharide endotoxin

Dairy cattle face a variety of stressful events on a daily basis. More specifically, climate change has resulted in more frequent heat stress events that increase the incidence of chronic bacterial infections by inducing conditions like leaky gut syndrome, whereby the integrity of the intestinal epithelium is compromised allowing for luminal bacterial lipopolysaccharide (LPS) endotoxin to infiltrate the host’s bloodstream resulting in acute or chronic systemic stimulation of the innate immune system. Repeated exposure to LPS over a short period of time is reported to induce immunotolerance within the host. This LPS tolerance is an essential immunohomeostatic response that can protect against over activation of the inflammatory response during subsequent exposure to LPS. In the present study, Holstein calves (n = 20) were initially stress challenged with either saline, or 100, 200 or 400 ng/kg of LPS administered intramuscular, and again re-challenged with 200 ng/kg of LPS 2-weeks later. Serum was collected every 2 hr for 6 hr to profile changes in circulatory stress biomarkers after the repeated LPS exposures. Heifers that were initially challenged with 100, 200 and 400 ng/kg of LPS demonstrated significantly attenuated cortisol responses in the second challenge (p < 0.01, 0.01 and 0.05, respectively), whereas control animals who previously received saline demonstrated a strong cortisol response at 2 hr after receiving 200 ng/kg of LPS (p < 0.05). The cytokine/chemokine (IL-6, CCL2, CCL3 and CCL4) responses were also attenuated during the LPS rechallenge (p < 0.05). Finally, microRNA expression profiles were determined to assess the epigenetic response to repeated LPS exposure. Interestingly, miR-31 and miR-223 were downregulated in response to the second LPS challenge. The present study demonstrates the dynamic nature of the stress response in dairy cattle as it relates to the development of LPS tolerance. Understanding the roles of various stress biomarkers in the context of innate immune cell tolerance is essential for evaluating their impact on immune system homeostasis.

Facial Recognition of Cattle Based on SK-ResNet

With the gradual increase of the scale of the breeding industry in recent years, the intelligence level of livestock breeding is also improving. Intelligent breeding is of great significance to the identification of livestock individuals. In this paper, the cattle face images are obtained from different angles to generate the cow face dataset, and a cow face recognition model based on SK_ResNet is proposed. Based on ResNet-50 and using a different number of sk_Bottleneck, this model integrates multiple receptive fields of information to extract facial features at multiple scales. The shortcut connection part connects to the maximum pooling layer to reduce information loss; the ELU activation function is used to reduce the vanishing gradient, prevent overfitting, accelerate the convergence speed, and improve the generalization ability of the model. The constructed bovine face dataset was used to train the SK-ResNet-based bovine face recognition model, and the accuracy rate was 98.42%. The method was tested on the public dataset and the self-built dataset. The accuracy rate of the model was 98.57% on the self-built pig face dataset and the public sheep face dataset. The accuracy rate was 97.02%. The experimental results verify the superiority of this method in practical application, which is helpful for the practical application of animal facial recognition technology in livestock.

A novel low-cost visual ear tag based identification system for precision beef cattle livestock farming

The precision livestock farming (PLF) has the objective to maximize each animal's performance while reducing the environmental impact and maintaining the quality and safety of meat production. Among the PLF techniques, the personalised management of each individual animal based on sensors systems, represents a viable option. It is worth noting that the implementation of an effective PLF approach can be still expensive, especially for small and medium-sized farms; for this reason, to guarantee the sustainability of a customized livestock management system and encourage its use, plug and play and cost-effective systems are needed. Within this context, we present a novel low-cost method for identifying beef cattle and recognizing their basic activities by a single surveillance camera. By leveraging the current state-of-the-art methods for real-time object detection, (i.e., YOLOv3) cattle's face areas, we propose a novel mechanism able to detect the ear tag as well as the water ingestion state when the cattle is close to the drinker. The cow IDs are read by an Optical Character Recognition (OCR) algorithm for which, an ad hoc error correction algorithm is here presented to avoid numbers misreading and correctly match the IDs to only actually present IDs. Thanks to the detection of the tag position, the OCR algorithm can be applied only to a specific region of interest reducing the computational cost and the time needed. Activity times for the areas are outputted as cattle activity recognition results. Evaluation results demonstrate the effectiveness of our proposed method, showing a mAP@0.50 of 89%.

Cattle face recognition under partial occlusion

The occlusion in the real feedlot environment is ubiquitous, and the current research based on the cattle face recognition under occlusion conditions is almost non-existent. Thus, an attention mechanism module with high accuracy and low model complexity is designed to incorporate into MobileNet so that the cattle face under occlusion can be identify accurately, which is the RGB images captured in the ranch environment. In this paper, we also construct a Simmental cattle face image dataset for data modeling and method evaluation, which contains 10,239 images of 103 cattle. The experimental results show that when the occluder is in the upper left and lower right corner, if the occlusion rate is less than 30%, the value of Top_1 reaches more than 90%; if it is less than 50%, the value of Top_1 is more than 80%. Even if the middle part occludes lots of important information, the occlusion rate of 40% has an accuracy of more than 80%. Furthermore, comparing the proposal model with MobileNet, the parameter and model size are equal, and the amount of calculation as a cost increase a little. Therefore, the proposal model is suitable to transplant to the embedded system in the future.

Cattle face recognition based on a Two-Branch convolutional neural network

• Proposed a cattle face recognition model based on a two-branch CNN. • Constructed face image datasets of dairy cows and beef cows. • Reduced the influence of cow face posture on recognition accuracy. • The network model is small and the recognition accuracy is high. Due to changes in cattle posture and different shooting angles, some features of collected cattle face images are missing, which leads to a decline in the accuracy of cattle face recognition. This paper proposes a cattle face recognition model based on a two-branch convolutional neural network (TB-CNN). The collected two cattle face images from different angles are input to the convolutional neural network of different channels for feature extraction, the features of the two channels are feature fused, and the global average pooling layer is combined with the classifier to identify the individual cattle. The squeeze-and-excitation block (SE) is embedded in the feature extraction network in the network model to improve the network feature extraction capability. The global average pooling layer is used to replace the fully connected layer, which improves the network classification capability and reduces the number of network parameters. The experimental results show that the recognition rate of the cattle face recognition model based on the TB-CNN is 99.85% on the Simmental beef cattle face image dataset, 99.81% on the Holstein cow face image dataset, and 99.71% on the beef cattle and cow mixed dataset. The cattle face recognition model proposed in this paper has good robustness and generalization ability, which can effectively reduce the influence of cattle face angle changes on the cattle face recognition rate and improve the accuracy of cattle face recognition.

Holstein Cattle Face Re-Identification Unifying Global and Part Feature Deep Network with Attention Mechanism.

Simple SummaryAutomated and non-intrusive recognition of individual livestock such as a cow or a pig is a vital requirement for obtaining individual information, something which has played a significant role in the intelligent management and welfare of livestock. However, individual cow identification remains a demanding task mainly due to the quite yet subtle inter-class differences in an open herd setting. The objective of this study is to develop a novel unified global and part feature deep network model (GPN) to learn more discriminative and robust features that can facilitate cow face representation for re-identification and verification. The results of various contrast experiments on collecting the large-scale cow face dataset show that the proposed GPN model outperforms the existing representative re-identification methods, and the further improved GPN-ST model has a higher accuracy rate (up by 2.8% and 2.2% respectively) in Rank-1 and mAP, compared with the GPN model. In conclusion, using the proposed framework can effectively ameliorate the performance of cow face re-identification.In precision dairy farming, computer vision-based approaches have been widely employed to monitor the cattle conditions (e.g., the physical, physiology, health and welfare). To this end, the accurate and effective identification of individual cow is a prerequisite. In this paper, a deep learning re-identification network model, Global and Part Network (GPN), is proposed to identify individual cow face. The GPN model, with ResNet50 as backbone network to generate a pooling of feature maps, builds three branch modules (Middle branch, Global branch and Part branch) to learn more discriminative and robust feature representation from the maps. Specifically, the Middle branch and the Global branch separately extract the global features of middle dimension and high dimension from the maps, and the Part branch extracts the local features in the unified block, all of which are integrated to act as the feature representation for cow face re-identification. By performing such strategies, the GPN model not only extracts the discriminative global and local features, but also learns the subtle differences among different cow faces. To further improve the performance of the proposed framework, a Global and Part Network with Spatial Transform (GPN-ST) model is also developed to incorporate an attention mechanism module in the Part branch. Additionally, to test the efficiency of the proposed approach, a large-scale cow face dataset is constructed, which contains 130,000 images with 3000 cows under different conditions (e.g., occlusion, change of viewpoints and illumination, blur, and background clutters). The results of various contrast experiments show that the GPN outperforms the representative re-identification methods, and the improved GPN-ST model has a higher accuracy rate (up by 2.8% and 2.2% respectively) in Rank-1 and mAP, compared with the GPN model. In conclusion, using the Global and Part feature deep network with attention mechanism can effectively ameliorate the efficiency of cow face re-identification.

A lightweight deep learning model for cattle face recognition

At present, there have been some researches on deep neural networks used in the field of biometrics to solve the problem of cattle identity authentication, particularly facial recognition under non-invasive methods. However, due to the large scale of deep neural network models, it is difficult to implement directly in resource-constrained embedded systems. Therefore, a lightweight neural network is designed in this paper that can be deployed in embedded systems that requires a small amount of weight representations and low-cost operators. The lightweight neural network designed in this paper contains six convolutional layers, which the convolution with a step size of two is used to achieve pooling, the Batch Normalization algorithm is used to normalize the neural network input, the dropout layer is used after the activation function Relu, and global average pooling is used to replace the fully connected layer after the sixth convolutional layer. The final parameter is 0.17 M, the model size is 2.01 MB, and the MFLOPs is 9.17. We have collected 10,239 cattle face images (103 subjects) from actual cattle farms to construct a dataset. The experimental results show that the accuracy of our proposed model is 98.37%, the macro average is 98%, and the Grad-CAM algorithm is used to verify the adequacy of features extracted by this network and avoid extracting noise features. The model we proposed is transplanted to the Raspberry Pi after training on the PC to test the running time, the model we proposed achieves a minimum detection time of 300 ms per picture. Our experiment results show that the lightweight neural network we proposed can achieve high recognition accuracy for cattle face recognition and significantly reduce the computational cost.

CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss

Cattle identification is crucial to be registered for breeding association, food quality tracing, disease prevention and control and fake insurance claims. Traditional non-biometrics methods for cattle identification is not really satisfactory in providing reliability due to theft, fraud, and duplication. In this study, a computer vision technique was proposed to facilitate precision animal management and improve livestock welfare. This paper presents a novel face identification framework by integrating light-weight RetinaFace-mobilenet with Additive Angular Margin Loss (ArcFace), namely CattleFaceNet. RetinaFace-mobilenet is designed for face detection and location, and ArcFace is adopted to strengthen the within-class compactness and also between-class discrepancy during training. Experiments on real-word scenarios dataset prove that RetinaFace-mobilenet achieves superior detection performance and significantly accelerates the computation time against RetinaNet. Three loss functions utilized in human face recognition combined with RetinaFace-mobilenet are compared and results indict that the proposed CattleFaceNet outperforms others with identification accuracy of 91.3% and processing time of 24 frames per second (FPS). This research work demonstrates the potential candidate of CattleFaceNet for livestock identification in real time in practical production scenarios.

Automated Muzzle Detection and Biometric Identification via Few-Shot Deep Transfer Learning of Mixed Breed Cattle

Livestock welfare and management could be greatly enhanced by the replacement of branding or ear tagging with less invasive visual biometric identification methods. Biometric identification of cattle from muzzle patterns has previously indicated promising results. Significant barriers exist in the translation of these initial findings into a practical precision livestock monitoring system, which can be deployed at scale for large herds. The objective of this study was to investigate and address key limitations to the autonomous biometric identification of cattle. The contributions of this work are fourfold: (1) provision of a large publicly-available dataset of cattle face images (300 individual cattle) to facilitate further research in this field, (2) development of a two-stage YOLOv3-ResNet50 algorithm that first detects and extracts the cattle muzzle region in images and then applies deep transfer learning for biometric identification, (3) evaluation of model performance across a range of cattle breeds, and (4) utilizing few-shot learning (five images per individual) to greatly reduce both the data collection requirements and duration of model training. Results indicated excellent model performance. Muzzle detection accuracy was 99.13% (1024 × 1024 image resolution) and biometric identification achieved 99.11% testing accuracy. Overall, the two-stage YOLOv3-ResNet50 algorithm proposed has substantial potential to form the foundation of a highly accurate automated cattle biometric identification system, which is applicable in livestock farming systems. The obtained results indicate that utilizing livestock biometric monitoring in an advanced manner for resource management at multiple scales of production is possible for future agriculture decision support systems, including providing useful information to forecast acceptable stocking rates of pastures.

Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

Overview of Folic Acid Supplementation Alone or in Combination with Vitamin B12 in Dairy Cattle during Periparturient Period.

The periparturient period is the period from three weeks before calving to three weeks post-calving. This period is important in terms of health, productivity and profitability, and is fundamental to successful lactation. During this period, the animal experiences stress because of hormonal changes due to pregnancy and the significant rise in milk production. In addition, a negative energy balance usually occurs, because the demand for nutrients to sustain milk production increases by more than the nutrient supply during the periparturient period. The immunity of dairy cattle is suppressed around parturition, which increases their susceptibility to infections. Special care regarding nutrition can reduce the risks of metabolism and immunity depression, which dairy cattle face during the periparturient span. Folic acid is relevant in this regard because of its critical role in the metabolism to maintain lactational performance and to improve health. Being a donor of one-carbon units, folic acid has a vital role in DNA and RNA biosynthesis. Generally, the folic acid requirements of dairy cattle can be met by the microbial synthesis in the rumen; however, in special cases, such as during the periparturient period, the requirement for this vitamin strictly increases. Vitamin B12 also has a critical role in the metabolism as a coenzyme of the enzyme methionine synthase for the transfer of a methyl group from folic acid to homocysteine for the regeneration of methionine. In the current review, we highlight the issues facing periparturient dairy cattle, and relevant knowledge and practices, and point out future research directions for utilization of the associated vitamins in ruminants, especially during the periparturient period.

Communal cattle farmer’s knowledge, attitudes and practices on ticks (Acari: Ixodidae), tick control and acaricide resistance

The aim of this study was to reveal communal farmer's knowledge, attitudes and practice on ticks and tick-borne diseases affecting cattle, tick control methods used and their knowledge on acaricide resistance. A total of 160 structured questionnaires are prepared and randomly administered to cattle farmers in Sinqu and Elundini communities in the Eastern Cape Province, South Africa. Associations between the area, gender, age, employment, source of income, level of education and farming experience were statistically tested using Chi-square test. Gall sickness (63%) was the most prevailing tick-borne disease in cattle during the summer season (61%). About 59% of the farmers had no knowledge of the effect of tick-borne diseases on cattle production, and 78% of respondents reported that ticks are the major challenge to cattle farming. Pour-on (61%) was the most commonly used acaricidal treatment system with every fortnight (40%) treatment frequency during the summer season and (31%) during the winter season. Pyrethroids (73%) were the most used acaricide compounds to control ticks, and about 65% of respondents perceived to have no knowledge of the use of ethnoveterinary medicines used to control ticks. Inefficacy of acaricide (44%) and undipped animals (42%) were regarded as the major contributing factors to the increased tick population and acaricide resistance. About 85% of respondents perceived that they do not practice acaricide rotation and 88% of the respondents had no knowledge of the acaricide resistance. Within the context of this study, ticks and tick-associated diseases are perceived by these farmers as the most important disease problem their cattle face. Thus, knowledge regarding the epidemiology of ticks and tick-borne diseases is key to control strategies, and farmer training should be encouraged to achieve better control of ticks and the diseases they transmit.

Biometric for Cattle Identification using Muzzle Patterns

Similar to human biometrics such as faces and fingerprints, animals also have biometrics for individual identifiers. This research paper works on biometrics of cattle using images of muzzle patterns. The proposed approach begins with a training process to construct a cattle face localization model using a Haar feature-based cascade classifier. Then, the watershed technique is applied to segment a region of interest (RoI) of a muzzle area in the detected region of the cattle face. This muzzle ROI is further enhanced to make ridge lines more outstanding. The next step, using two approaches, is to extract a main feature descriptor based on a bag of histograms of oriented gradients (BoHoG) and a histogram of local binary patterns (LBP). Then, the support vector machine (SVM) is applied with the histogram intersection kernel for a final cattle identifier. The proposed method is evaluated using five different datasets including one existing cattle dataset used in previous research works, one newly collected dataset of swamp buffalo captured in a controlled environment, and three newly collected datasets of swamp buffalo captured in an outdoor field environment. This outdoor field environment includes challenges of freely moving cattle and differences in daylight. It could achieve a promising accuracy of 95% for a large dataset of 431 subjects.