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<p>Pakistan’s rural economy, which heavily depends on agriculture, continues to struggle with significant inefficiencies in its supply chains. These issues primarily affect smallholder farmers and contribute to the persistence of rural poverty. This study presents the AgriChain Nexus, an innovative framework driven by artificial intelligence designed to transform traditional agricultural supply chains through strategic digitalization. The framework encompasses four advanced computational components: multi-agent reinforcement learning (MARL) to facilitate decentralized and adaptive decision-making; graph neural networks (GNNs) to model the dynamic interactions among agricultural actors and infrastructure; blockchain-enabled smart contracts to ensure transactional transparency and traceability; and quantum-inspired optimization algorithms to address complex resource allocation and routing challenges under real-world constraints. Our study reveals that the GNN aspect achieves a prediction accuracy of roughly 90%, resulting in a 35% increase in crop yields compared to conventional approaches. In comparison, the MARL component successfully lowers supply chain expenses by 20%, resulting in a 30% increase in farmer income. These improvements imply that, based on our simulations, widespread implementation of such a system <i>could potentially</i> lift 5–10% of rural households above the poverty line each year.</p>

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AI-driven digitalization of agriculture-based supply chains to reduce rural poverty in Pakistan

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月14日

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  • Furqan Sikandar,
  • Hanggao Lu,
  • Muhammad Zada,
  • Sujian Jiang

摘要

Pakistan’s rural economy, which heavily depends on agriculture, continues to struggle with significant inefficiencies in its supply chains. These issues primarily affect smallholder farmers and contribute to the persistence of rural poverty. This study presents the AgriChain Nexus, an innovative framework driven by artificial intelligence designed to transform traditional agricultural supply chains through strategic digitalization. The framework encompasses four advanced computational components: multi-agent reinforcement learning (MARL) to facilitate decentralized and adaptive decision-making; graph neural networks (GNNs) to model the dynamic interactions among agricultural actors and infrastructure; blockchain-enabled smart contracts to ensure transactional transparency and traceability; and quantum-inspired optimization algorithms to address complex resource allocation and routing challenges under real-world constraints. Our study reveals that the GNN aspect achieves a prediction accuracy of roughly 90%, resulting in a 35% increase in crop yields compared to conventional approaches. In comparison, the MARL component successfully lowers supply chain expenses by 20%, resulting in a 30% increase in farmer income. These improvements imply that, based on our simulations, widespread implementation of such a system could potentially lift 5–10% of rural households above the poverty line each year.