AI-driven digitalization of agriculture-based supply chains to reduce rural poverty in Pakistan
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces AgriChain Nexus, an AI framework enhancing agricultural supply chains. It boosts crop yields by 35% and farmer income by 30%, significantly reducing rural poverty.
Area Of Science
- Agricultural Economics
- Computer Science
- Supply Chain Management
Background
- Pakistan's rural economy faces significant agricultural supply chain inefficiencies.
- Smallholder farmers are disproportionately affected, perpetuating rural poverty.
Purpose Of The Study
- To present the AgriChain Nexus, an AI-driven framework for digitalizing and transforming agricultural supply chains.
- To address inefficiencies impacting smallholder farmers and rural poverty in Pakistan.
Main Methods
- Utilized multi-agent reinforcement learning (MARL) for decentralized decision-making.
- Employed graph neural networks (GNNs) to model actor and infrastructure interactions.
- Integrated blockchain-enabled smart contracts for transparency and quantum-inspired algorithms for optimization.
Main Results
- Graph neural networks achieved ~90% prediction accuracy, increasing crop yields by 35%.
- Multi-agent reinforcement learning reduced supply chain costs by 20%, boosting farmer income by 30%.
Conclusions
- The AgriChain Nexus framework demonstrates significant potential for improving agricultural supply chains.
- Simulations suggest widespread implementation could annually lift 5-10% of rural households out of poverty.
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