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 techniqu
The increasing sophistication of computer vision and machine learning models, coupled with growing biosecurity and supply chain concerns, is driving urgent demand for advanced livestock management solutions.
Improved cattle identification directly impacts food security, prevents disease spread, and enhances economic efficiency in a critical agricultural sector that is vulnerable to disruption.
The adoption of sophisticated AI for livestock identification reduces manual labor, improves accuracy, and provides real-time data for better decision-making in large-scale farming operations.
- · Agricultural technology companies
- · Livestock producers
- · Food safety regulators
- · AI/ML developers
- · Traditional manual cattle identification methods
- · Regions without access to advanced tech
- · Low-tech cattle management service providers
Widespread adoption of AI-driven systems for individual animal tracking and health monitoring in agriculture.
Increased efficiency and reduced disease transmission in global livestock supply chains, impacting protein production and trade.
The application of similar biometric AI identification technologies to other animal husbandry and potentially wild animal conservation efforts.
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