In an information-driven society, food safety has become a significant concern in people's daily lives. Establishing an agricultural product traceability system plays a crucial role in ensuring food safety. However, traditional agricultural product traceability systems encounter numerous issues in practical application, such as information asymmetry, centralized system structures, and vulnerability to information tampering. Therefore, this research aims to leverage blockchain and Internet of Things (IoT) technologies to address these problems in the existing agricultural product traceability systems, enhancing their efficiency and reliability, while also employing machine learning for supply and demand prediction within the supply chain. The study found that the decentralized, tamper-proof, and transparent nature of blockchain technology, combined with the extensive data collection and processing capabilities of IoT, can effectively improve agricultural product traceability systems. Additionally, machine learning can accurately predict demand, thereby improving the efficiency of supply chain transportation to avoid shortages and overstocking.
Blockchain; Internet of Things; Traceability; Hyperledger structure; Agricultural products; Machine learning
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