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AIoE-Powered Supply Chain Optimization for Smart Agriculture

In: Artificial Intelligence of Everything and Sustainable Development

Author

Listed:
  • Asghar Hemmati

    (Islamic Azad University)

  • Mahdi Aliyari

    (Islamic Azad University)

  • Adel Pourghader Chobar

    (Islamic Azad University)

Abstract

Integrating AIoE and blockchain revolutionizes agricultural supply chains, improving efficiency, transparency, and sustainability. Traditional agrarian systems face communication gaps, inefficient inventory management, logistical challenges, and limited traceability. This research explores how AIoE-driven intelligence and blockchain technology address these issues by enabling real-time data analytics, predictive modeling, and automated smart contracts. AIoE optimizes demand forecasting, logistics, and decision-making, ensuring efficient resource allocation. Blockchain technology provides secure, tamper-proof transactions, enhancing trust and traceability throughout the supply chain. The study presents an innovative framework that combines predictive intelligence, blockchain-based transparency, and financial automation to develop self-optimizing and resilient agricultural systems. Key advancements include AI-powered financial solutions, automated logistics, and climate-resilient farming strategies, offering a future-focused approach to intelligent agriculture. Emerging technologies such as quantum computing, 5G, and AI-driven digital twins further enhance autonomous and adaptive supply chain networks. These innovations contribute to global food security, economic stability, and sustainable agriculture, positioning AIoE and blockchain as essential drivers of the digital transformation of agricultural supply chains.

Suggested Citation

  • Asghar Hemmati & Mahdi Aliyari & Adel Pourghader Chobar, 2025. "AIoE-Powered Supply Chain Optimization for Smart Agriculture," Springer Books, in: Hamed Nozari (ed.), Artificial Intelligence of Everything and Sustainable Development, pages 223-240, Springer.
    • Handle: RePEc:spr:sprchp:978-981-96-7202-8_13
    DOI: 10.1007/978-981-96-7202-8_13
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