Improving traceability query in blockchain-based food supply chain systems using NLP and B+ tree indexing

Food supply chains have increasingly adopted blockchain technology due to its critical role in ensuring food safety and providing consumers with traceability and transparency of food products. Existing blockchain-based traceability systems often rely on complementary technologies such as QR codes, RFID, and barcodes to facilitate tracking and data retrieval. However, these systems face significant challenges in managing large-scale, complex traceability queries. Moreover, their query time performance tends to degrade as the volume of data and the size of the blockchain ledger grow, since the search must be performed sequentially across blocks especially in complex food supply chains. To address these limitations, we propose a novel blockchain-based traceability system that integrates a natural language processing (NLP) model and B+ tree indexing technique. This integration ensures accurate data responses and optimizes traceability queries within the blockchain ledger. The system was simulated using the hyperledger fabric framework, with various scenarios tested based on different data and block sizes, and the experimental results demonstrate a significant improvement in complex traceability range queries, achieving an average reduction of 68.32% compared to native blockchain-based methods with sequential search and scanning of all blocks in the ledger.