muhammad.rabbi@amanex.ca
Global Food Trusts : Blockchain Food Traceability System
Global Food Trusts : Blockchain Food Traceability System

Global Food Trusts : Blockchain Food Traceability System

The Global Food Traceability System is an advanced blockchain, artificial intelligence (AI), and machine learning (ML) integrated platform designed to enhance food safety, supply chain transparency, and consumer trust. By leveraging Distributed Ledger Technology (DLT), AI-driven analytics, and predictive modeling, the system ensures end-to-end visibility from farm to fork, reducing food fraud, contamination risks, and inefficiencies in the supply chain. By combining blockchain for transparency, AI for smart monitoring, and ML for predictive analytics, the Global Food Traceability System builds a trustworthy, secure, and data-driven food ecosystem, ensuring that consumers receive safe, high-quality, and ethically sourced food products worldwide.

How the Global Food Traceability System Stands Out

AI-Powered Anomaly Detection – Uses machine learning algorithms to detect irregularities, predict food spoilage, and flag potential contamination in real time.
Smart Contract Automation – Ensures secure, tamper-proof transactions between farmers, suppliers, and retailers, reducing delays and enhancing food safety compliance.
Real-Time Consumer Access – Customers can scan QR codes to instantly access verified data on product origin, quality certifications, and sustainability practices.
Predictive Analytics for Supply Chain Efficiency – AI-driven insights optimize inventory management, prevent food waste, and enhance supply-demand forecasting.

Blockchain technology

Blockchain technology can trace the journey of products from farms to store shelves in just 2.2 seconds instead of the traditional 7-day process, ensuring authenticity, safety, and regulatory compliance. (Ref Walmart IBM Food Trusts)

Global Food Trust

A blockchain-based food traceability network adopted by major retailers helping track food origins, processing, and distribution in real time.