Cattle identification based in biometric features of the muzzle

Cattle identification has been a serious issue for breeding association. The muzzle pattern that is correlated with human fingerprints has been considered as a biometric marker for cattle and could be used in identification of bovine animals. This work presents a robust and fast cattle identification scheme from muzzle images using Speed-up Robust Features matching. To eliminate miss-matched outliers a matching refinement technique based on the matching orientation information has been proposedinfo:eu-repo/semantics/publishedVersio

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

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.

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

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

Determining Cattle Identity Based on Planum Nasolabiale Imprints

S gledišta veterinarske medicine, identifikacija životinja jedan je od važnijih postupaka za struku, kako u postupku procjenjivanja kontrole kvalitete praćenja zdravstvenog stanja životinja, tako i tijekom praćenja prometa domaćih i drugih životinja i životinjskih proizvoda u cilju nadzora lanca ishrane, odnosno kontrole uvoza i izvoza, rezidua te dobrobiti životinja. Metoda izuzimanja otiska nosnog zrcala pomoću foto crnog sjajnog papira je metoda izbora identifikacije goveda na osnovi izuzetog otiska nosnog zrcala pri čijem izvođenju životinji nije prouzročena bol, patnja, tjeskoba ili trajno oštećenje u jednakoj ili većoj mjeri od uboda igle. Na identifikacijskom kartonu fiksiranog otiska nosnog zrcala goveda, izuzetog metodom pomoću foto crnog sjajnog papira te vizualiziranog sivim instant praškom, odabirom najmanje 3 od 6 osnovnih oblika utora i rasporeda sekrecijskih žlijezda nosnog zrcala u najmanje 12 identifikacijskih točaka moguća je identifikacija životinje sa sigurnošću od 86%.From the perspective of veterinary medicine, the identification of animals is one of the most important procedures in the field. Every step is important, from the procedure of assessing animal health condition control quality, to following domestic animal, other animal, and animal product traffic in order to monitor the food chain, or in other words control imports, exports, residue, and the well-being of animals. The method by which imprints of the planum nasolabiale are taken is using a black shiny paper. This method helps identify cattle and does in no way cause pain, suffering, anxiety, or permanent damage larger than a needle prick. The method is conducted by taking an imprint from the planum nasolabiale of a cattle using black shiny paper later making the imprint visible with gray instant powder, using at least three to six basic slot shapes and planum nasolabiale secretion gland schedules in at least 12 identifiable points. The fixed cattle planum nasolabiale imprint ID chart helps identify the animal with an 86% certainty

Identificação biométrica de gado bovino a partir de imagens do focinho

A identificação de gado bovino tem sido um problema grave para a associação de criadores. O presente trabalho tem como objetivo implementar um método para identificação de bovinos através de imagens biométricas do seu focinho. Para tal são avaliadas quatro metodologias de identificação, tendo em conta vários parâmetros de avaliação tais como: taxa de acerto, necessidade de pré-processamento e facilidade de utilização, bem como a velocidade de execução. A primeira metodologia intitulada por “Identificação através de pontos de Landmark” consiste na deteção destes pontos e através destes efetuar a correspondência entre as imagens. A segunda metodologia designada por “Correspondência espetral e Reweighted random walk matching (RRWM) ”, consiste na utilização de matrizes de afinidade e, a partir delas, encontrar correspondências entre imagens. A terceira metodologia denominada por “Identificação através de pontos SURF”, que se baseia em identificar as características e em efetuar a correspondência entre as imagens utilizando o método SURF (Speeded Up Robust Features). O último método designa-se por “Identificação utilizando o diagrama de Voronoi e a triangulação de Delaunay” este baseia-se na deteção dos centróides das glândulas e na identificação de características semelhantes através da triangulação de Delaunay. O método que produziu melhores resultados de avaliação foi a técnica de identificação baseada nas características obtidas pelo SURF. No final deste trabalho, e através dos resultados obtidos foi possível concluir-se que a metodologia adotada foi bem-sucedida obtendo uma taxa de acerto de 100 %, tornando-se assim numa alternativa válida de identificação.The identification of cattle has been a serious problem for the association of breeders. This work aims to implement a method for identifying cattle through yours biometric muzzle images. To this are evaluated four methodologies of identification, into account various evaluation parameters such as hit rate, the need for pre-processing and ease of use ad execution speed. The first methodology entitled by "Identification through Landmark points" consists in detecting these points and using that to make correspondence between the images. The second method called "Correspondence spectral and Reweighted Random Walk Matching (RRWM)" consists in the utilization of affinity matrices and from them, find correspondences between images. The third method referred to as "Identification through SURF points”, which is based on identifying characteristics and make correspondence between the images using the SURF method (Speeded Up Robust Features). The last method is referred to as "Identification using the Voronoi diagram and Delaunay triangulation". This is based initially for detection of centroids of the glands and identifying similar characteristics by Delaunay Triangulation. The method that produced the best results of evalution was the identification technique based on the characteristics obtained by SURF. At the end of this work, and through the obtained results we conclude that the methodology adopted was successful getting a 100% hit rate, thus becoming a valid alternative of the identification