657 research outputs found
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
Image Segmentation of Cattle Muzzle Using Region Merging Statistical Technic
Making an identification system that able to assist in obtaining, recording and organizing information is the first step in developing any kind of recording system. Nowadays, many recording systems were developed with artificial markers although it has been proved that it has many limitations. Biometrics use of animals provides a solution to these restrictions. On a cattle, biometric features contained in the cattle muzzle that can be used as a pattern recognition sample. Pattern recognition methods can be used for the development of cattle identification system utilizing biometric found on the cattle muzzle using digital image processing techniques. In this study, we proposed cattle muzzle identification method using segmentation Statistical Region Merging (SRM). This method aims to identify specific patterns found on the cattle muzzle by separating the object pattern (foreground) from unnecessary information (background) This method is able to identified individual cattle based on the pattern of it muzzle. Based on our evaluation, this method can provide good performance results. This method good performance can be seen from the precision and recall : 87% and the value of ROC : 0.976. Hopefully this research can be used to help identify cattle accurately on the recording process
Visual identification of individual Holstein-Friesian cattle via deep metric learning
Holstein-Friesian cattle exhibit individually-characteristic black and white
coat patterns visually akin to those arising from Turing's reaction-diffusion
systems. This work takes advantage of these natural markings in order to
automate visual detection and biometric identification of individual
Holstein-Friesians via convolutional neural networks and deep metric learning
techniques. Existing approaches rely on markings, tags or wearables with a
variety of maintenance requirements, whereas we present a totally hands-off
method for the automated detection, localisation, and identification of
individual animals from overhead imaging in an open herd setting, i.e. where
new additions to the herd are identified without re-training. We propose the
use of SoftMax-based reciprocal triplet loss to address the identification
problem and evaluate the techniques in detail against fixed herd paradigms. We
find that deep metric learning systems show strong performance even when many
cattle unseen during system training are to be identified and re-identified -
achieving 98.2% accuracy when trained on just half of the population. This work
paves the way for facilitating the non-intrusive monitoring of cattle
applicable to precision farming and surveillance for automated productivity,
health and welfare monitoring, and to veterinary research such as behavioural
analysis, disease outbreak tracing, and more. Key parts of the source code,
network weights and underpinning datasets are available publicly.Comment: 37 pages, 14 figures, 2 tables; Submitted to Computers and
Electronics in Agriculture; Source code and network weights available at
https://github.com/CWOA/MetricLearningIdentification; OpenCows2020 dataset
available at https://doi.org/10.5523/bris.10m32xl88x2b61zlkkgz3fml1
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
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