Individual Beef Cattle Identification Using Muzzle Images and Deep Learning Techniques

The ability to identify individual animals has gained great interest in beef feedlots to allow for animal tracking and all applications for precision management of individuals. This study assessed the feasibility and performance of a total of 59 deep learning models in identifying individual cattle with muzzle images. The best identification accuracy was 98.7%, and the fastest processing speed was 28.3 ms/image. A dataset containing 268 US feedlot cattle and 4923 muzzle images was published along with this article. This study demonstrates the great potential of using deep learning techniques to identify individual cattle using muzzle images and to support precision beef cattle management. Individual feedlot beef cattle identification represents a critical component in cattle traceability in the supply food chain. It also provides insights into tracking disease trajectories, ascertaining ownership, and managing cattle production and distribution. Animal biometric solutions, e.g., identifying cattle muzzle patterns (unique features comparable to human fingerprints), may offer noninvasive and unique methods for cattle identification and tracking, but need validation with advancement in machine learning modeling. The objectives of this research were to (1) collect and publish a high-quality dataset for beef cattle muzzle images, and (2) evaluate and benchmark the performance of recognizing individual beef cattle with a variety of deep learning models. A total of 4923 muzzle images for 268 US feedlot finishing cattle (\u3e12 images per animal on average) were taken with a mirrorless digital camera and processed to form the dataset. A total of 59 deep learning image classification models were comparatively evaluated for identifying individual cattle. The best accuracy for identifying the 268 cattle was 98.7%, and the fastest processing speed was 28.3 ms/image. Weighted cross-entropy loss function and data augmentation can increase the identification accuracy of individual cattle with fewer muzzle images for model development. In conclusion, this study demonstrates the great potential of deep learning applications for individual cattle identification and is favorable for precision livestock management. Scholars are encouraged to utilize the published dataset to develop better models tailored for the beef cattle industry

Cattle Identification Using Muzzle Images and Deep Learning Techniques

Traditional animal identification methods such as ear-tagging, ear notching, and branding have been effective but pose risks to the animal and have scalability issues. Electrical methods offer better tracking and monitoring but require specialized equipment and are susceptible to attacks. Biometric identification using time-immutable dermatoglyphic features such as muzzle prints and iris patterns is a promising solution. This project explores cattle identification using 4923 muzzle images collected from 268 beef cattle. Two deep learning classification models are implemented - wide ResNet50 and VGG16\_BN and image compression is done to lower the image quality and adapt the models to work for the African context. From the experiments run, a maximum accuracy of 99.5\% is achieved while using the wide ResNet50 model with a compression retaining 25\% of the original image. From the study, it is noted that the time required by the models to train and converge as well as recognition time are dependent on the machine used to run the model.Comment: 8 pages, 4 figures, 2 table

CORF3D contour maps with application to Holstein cattle recognition using RGB and thermal images

Livestock management involves the monitoring of farm animals by tracking certain physiological and phenotypical characteristics over time. In the dairy industry, for instance, cattle are typically equipped with RFID ear tags. The corresponding data (e.g. milk properties) can then be automatically assigned to the respective cow when they enter the milking station. In order to move towards a more scalable, affordable, and welfare-friendly approach, automatic non-invasive solutions are more desirable. Thus, a non-invasive approach is proposed in this paper for the automatic identification of individual Holstein cattle from the side view while exiting a milking station. It considers input images from a thermal-RGB camera. The thermal images are used to delineate the cow from the background. Subsequently, any occluding rods from the milking station are removed and inpainted with the fast marching algorithm. Then, it extracts the RGB map of the segmented cattle along with a novel CORF3D contour map. The latter contains three contour maps extracted by the Combination of Receptive Fields (CORF) model with different strengths of push-pull inhibition. This mechanism suppresses noise in the form of grain type texture. The effectiveness of the proposed approach is demonstrated by means of experiments using a 5-fold and a leave-one day-out cross-validation on a new data set of 3694 images of 383 cows collected from the Dairy Campus in Leeuwarden (the Netherlands) over 9 days. In particular, when combining RGB and CORF3D maps by late fusion, an average accuracy of was obtained for the 5-fold cross validation and for the leave–one day–out experiment. The two maps were combined by first learning two ConvNet classification models, one for each type of map. The feature vectors in the two FC layers obtained from training images were then concatenated and used to learn a linear SVM classification model. In principle, the proposed approach with the novel CORF3D contour maps is suitable for various image classification applications, especially where grain type texture is a confounding variable

Identification and recognition of animals from biometric markers using computer vision approaches: a review

Although classic methods (such as ear tagging, marking, etc.) are generally used for animal identification and recognition, biometric methods have gained popularity in recent years due to the advantages they offer. Systems utilizing biometric markers have been developed for various purposes in animal management, including more effective and accurate tracking of animals, vaccination, disease management, and prevention of theft and fraud. Animals" irises, retinas, faces, muzzle, and body patterns contain unique biometric markers. The use of these markers in computer vision approaches for animal identification and tracking systems has become a highly effective and promising research area in recent years. This review aims to provide a general overview of the latest developments in image processing approaches for animal identification and recognition applications. In this review, we examined in detail all relevant studies we could access from different electronic databases for each biometric method. Afterward, the opportunities and challenges of classical and biometric methods were compared. We anticipate that this study, which conducts a literature review on animal identification and recognition based on computer vision approaches, will shed light on future research towards developing automated systems with biometric methods

Assessing the predictive value of dairy facial biometrics for measures of productivity, health, and social dominance

Includes bibliographical references.2018 Fall.To view the abstract, please see the full text of the document

Oestrus and ovulation detection in pasture-based dairy herds: the role of new technologies

Automatic milking systems (AMS) are becoming increasingly popular due to the growing cost of labour and reduced labour availability. The voluntary cow traffic and resultant distribution of milkings throughout the day and night affects most aspects of herd and farm management in AMS. The literature review (Chapter 1) highlighted a need to evaluate the effects of milk yield and milking frequency during early lactation on reproductive performance. The analysis of a 5-year historic database from Australia’s first AMS research farm (Chapter 2) found no significant association of average milk yield and milking frequency during 100 days in milk with any of the reproductive measures. However, the interval from calving to first oestrus increased gradually within the study period and consequently influenced other reproductive outcomes. As a result, a series of studies were conducted with a multidisciplinary approach (both physiological and technological) to investigate the potential to improve oestrus detection on pasture-based AMS farms. A field study (Chapter 3) was conducted to allow for the development and application of an algorithm to assess the application accuracy of an infrared thermography (IRT) device when used to detect oestrus events or pending oestrus events by detecting the time of ovulation. Vulval and muzzle temperatures were measured by IRT in twenty synchronized cows (using a controlled internal drug release and prostaglandin F2α). Whilst the IRT showed some potential as an oestrus detection aid with higher sensitivity than visual observation (67%) and Estrotect activation (67%), the specificity and positive predictive value were lower with the IRT. The vulva and muzzle were the focus areas for the IRT application and some concern was generated with regard to the potential for the IRT data to impacted by faecal contamination, obscuring of the vulva by the tail and time since last drinking (affecting muzzle surface temperature). To address these concerns a further study (Chapter 5) was conducted to test the hypothesis that the specificity of IRT in detecting oestrus (or imminent oestrus) could be improved if other body parts were focused on. In that study (Chapter 5), an additional technology was incorporated to test the hypothesis that the combined activity and rumination data generated by an accelerometer (SCR heat and rumination long distance tags) would provide a more accurate indication of oestrus and/or ovulation than the activity and rumination data alone. Unfortunately the monitoring of eyes and/or ears did not provide the improvement in accuracy of IRT (as an oestrus detection aid) indicating that as an oestrus detection aid there was likely to be limited value in developing this as an automated stand-alone device. Alerts generated by accelerometer based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators and would still require the herd’s person to filter data to identify the false alerts to ensure that cows are not inseminated unnecessarily. Whilst the use of in-line milk monitoring has already been commercialized for the assessment of milk progesterone, there is potential for other biomarkers to provide further opportunities for the assessment of milk components. Biomarkers of oxidative stress were evaluated in plasma showing that plasma glutathione was lower in ovulated cows compared to those of an-ovulated cows (Chapter 4). Whilst baseline plasma data for oxidative stress biomarkers was a useful starting point, the real value of these biomarkers would be realised if their concentration in milk could be linked with oestrus (and or ovulation). Milk superoxide dismutase activity was shown to be higher in ovulated cows while lipoperoxides, glutathione peroxidase were lower in ovulated cows compared to those in an-ovulated cows (Chapter 6). Further work would be required to determine the accuracy with which these biomarkers could be used to identify oestrus cows but these results are promising and suggest that there may be some potential to develop in-line milk sampling technology to alert the herdsperson to cows that should be inseminated. In summary, this thesis provides very useful, scientifically based information on potential use of technologies for oestrus and ovulation detection in dairy cows, which should serve as a foundation to develop and upgrade automated on-farm technologies and biosensors for better reproductive management of cows in pasture-based AMS. However, it is noted that the most likely success with automated oestrus detection is to require a combination of different indicators that should be incorporated to truly increase the accuracy of detection beyond that which can be achieved by skilled and devoted herd’s people

Welfare of cattle during killing for purposes other than slaughter

Cattle of different ages may have to be killed on farm for purposes other than slaughter (the latter being defined as killing for human consumption) either individually or on a large scale, e.g. for economic reasons or for disease control. The purpose of this scientific opinion is to assess the risks associated with the on‐farm killing of cattle. The processes during on‐farm killing that were assessed included handling and moving, stunning and/or killing methods (including restraint). The killing methods were grouped into mechanical and electrical methods as well as lethal injection. In total, 21 hazards compromising animal welfare were identified and characterised, most of these related to stunning and/or killing. Staff was identified as an origin for all hazards, either due to lack of appropriate skills needed to perform tasks or due to fatigue. Possible preventive and corrective measures were assessed: measures to correct hazards were identified for 19 hazards, and the staff was shown to have a crucial role in prevention. Three welfare consequences of hazards to which cattle can be exposed during on‐farm killing were identified: impeded movement, pain and fear. The welfare consequences and relevant animal‐based measures related to these were described. Outcome tables linking hazards, welfare consequences, animal‐based measures, origins of the hazards, preventive and corrective measures were developed for each process. Mitigation measures to minimise the welfare consequences are proposed.info:eu-repo/semantics/publishedVersio

Temporal variation in body measurements in three Taurine cattle populations of Burkina Faso supports introgression of Zebu genes into West African Taurine cattle

A total of 769 adult females belonging to 3 taurine and one zebu cattle populations sampled in 3 provinces of Burkina Faso were assessed for 19 body measurements during two different years (2014 and 2018). The aim of this research was to identify temporal morphological variation in cattle bred in the humid southern zones to obtain empirical evidence supporting a possible introgression of zebu cattle genes into Gourounsi and Lobi taurine cattle breeds. Zebu cattle samples were used as out-group for both 2014 and 2018 subsets. Least square means of body measurements allowed to classify Burkina Faso taurine cattle into three subgroups according to body size (Gourounsi–Sanguié –GourS-, Gourounsi-Nahouri –GourN- and Lobi from the tallest to the smallest respectively). Principal Component Analysis suggested that in 2014, taurine populations were structured. Dispersion map constructed using the two first factors informed that the GourS population was well separated from both the Lobi and the GourN, which, in turn, overlapped. However, in 2018 a strong signal of homogenization was identified, with GourN partially overlapping the other two populations. Linear Discriminant Analysis suggested that about 20% of both GourS and GourN individuals were reciprocally misclassified. Clues for such increase have been pointed out by MANOVA analysis. Although on 2014, Lobi cattle was clearly smaller than Gourounsi and both GourS and GourN populations showed clear differences on body traits, on 2018 it could be assessed an increase in size in Lobi cattle and a strong homogenization signal within Gourounsi cattle. Zebu cattle gene flow southwards in Burkina Faso is likely to have caused these changes, suggesting a fast erosion of taurine cattle genetic background. Keywords: Body traits, quantitative traits, Gourounsi cattle, Lobi, Burkina Faso