aiqbench analysis
This systematic review evaluates deep learning architectures like CNNs and YOLO for livestock identification, highlighting their superiority over classical methods in handling complex environmental variables such as animal mobility. The analysis underscores critical infrastructure challenges, including the scarcity of high-quality public datasets and the necessity for real-time processing capabilities to ensure scalable biosecurity solutions. While the application targets agricultural efficiency, the core technical contribution remains firmly rooted in advancing computer vision algorithms within machine learning frameworks.
Advanced Machine Learning and Deep Learning Techniques for Enhanced Cattle Identification and Detection: A Comprehensive Review
arXiv:2606.15655v1 Announce Type: new Abstract: The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management. This paper presents a systematic review of recent research in cattle identification using machine learning and deep learning techniques. The present systematic review measures the effectiveness of traditional and modern cattle identification techniques using studies from major academic databases, where articles were subjected to full-text review. Among these techniques, classical Machine Learning Techniques such as K-Nearest Neighbors and Support Vector Machines have demonstrated good results in cattle identification; however, Deep Learning Techniques, such as Convolutional Neural Networks, Residual Networks, and You Only Look Once, are better in cognition, detection, and identification tasks. Feature extraction relies on common techniques like Local Binary Pattern (LBP), Speeded-Up Robust Features (SURF), and Scale-Invariant Feature Transform (SIFT), while key features commonly used in these studies include muzzle prints and coat patterns. The review highlights key hurdles involving cattle identification, such as the limited number of publicly accessible datasets, issues with data quality susceptible to environmental changes and animal mobility, and high demand for real-time processing ability. The paper aims to inform researchers, policymakers, and stakeholders about implementing scalable, humane, and effective cattle identification systems to achieve sustainable livestock management.
Abstract:The need for effective cattle identification technology is now more acutely felt than ever in maintaining biosecurity, food safety, and supply chain efficacy in livestock management. This paper presents a systematic review of recent research in cattle identification using machine learning and deep learning techniques. The present systematic review measures the effectiveness of traditional and modern cattle identification techniques using studies from major academic databases, where articles were subjected to full-text review. Among these techniques, classical Machine Learning Techniques such as K-Nearest Neighbors and Support Vector Machines have demonstrated good results in cattle identification; however, Deep Learning Techniques, such as Convolutional Neural Networks, Residual Networks, and You Only Look Once, are better in cognition, detection, and identification tasks. Feature extraction relies on common techniques like Local Binary Pattern (LBP), Speeded-Up Robust Features (SURF), and Scale-Invariant Feature Transform (SIFT), while key features commonly used in these studies include muzzle prints and coat patterns. The review highlights key hurdles involving cattle identification, such as the limited number of publicly accessible datasets, issues with data quality susceptible to environmental changes and animal mobility, and high demand for real-time processing ability. The paper aims to inform researchers, policymakers, and stakeholders about implementing scalable, humane, and effective cattle identification systems to achieve sustainable livestock management.
Published in the journal of Annals of Emerging Technologies in Computing (AETiC), 34 pages, 5 Figures. The Article is available here: this http URL
Artificial Intelligence (cs.AI)
arXiv:2606.15655 [cs.AI]
(or arXiv:2606.15655v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.15655 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
Annals of Emerging Technologies in Computing (AETiC),Vol. 10, No. 2, 2026
https://doi.org/10.33166/AETiC.2026.02.001 Focus to learn more DOI(s) linking to related resources
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