AIoE-Powered Supply Chain Optimization for Smart Agriculture

Asghar Hemmati (), Mahdi Aliyari and Adel Pourghader Chobar ()
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Asghar Hemmati: Islamic Azad University
Mahdi Aliyari: Islamic Azad University
Adel Pourghader Chobar: Islamic Azad University

A chapter in Artificial Intelligence of Everything and Sustainable Development, 2025, pp 223-240 from Springer

Abstract: 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.

Keywords: AIoE; Blockchain; Smart agriculture; Supply chain optimization; Transparency (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-7202-8_13

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DOI: 10.1007/978-981-96-7202-8_13

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