AI-Driven Optimization and Blockchain-Based Traceability for Green Food Supply Chain Safety and Transparency

Wen LiuDexian Li^*

• College of Public Administration and Law, Hunan Agricultural University, Changsha, China

Introduction: Ensuring the quality and safety of green food throughout the supply chain has become increasingly critical due to growing consumer concerns and the complexity of food systems. Traditional approaches often suffer from information asymmetry and poor traceability. This study addresses these challenges by integrating artificial intelligence (AI) and blockchain technology (BCT) to enhance transparency, traceability, and early warning capabilities in green food supply chains. Methods: A two-part technical framework is developed. First, a production anomaly warning model is constructed using a Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm, allowing real-time detection of deviations in production parameters. Second, a blockchain-based traceability system is implemented using Hyperledger Fabric, recording critical events across procurement, processing, logistics, and distribution nodes. Data from IoT sensors are collected and transmitted through a closed-loop “sensing-analysis-certification” mechanism to ensure data reliability and integrity. Results: The ST-DBSCAN-based warning model outperforms traditional machine learning methods, achieving an accuracy of 0.93, precision of 0.91, recall of 0.90, and F1-score of 0.90. The BCT-based traceability model demonstrates superior performance with an average query time of 2.3 seconds, throughput of 400 requests/second, and latency of 1.15 seconds, significantly surpassing traditional and cloud database systems in response efficiency and stability. Discussion: The proposed AI-BCT integrated model enhances risk identification, real-time monitoring, and traceability across the green food supply chain. Moreover, the study introduces green finance and supply chain financing mechanisms to support small and medium-sized enterprises in adopting intelligent supervision technologies. This research contributes a practical, secure, and scalable approach to advancing digital governance in food safety and building resilient, trustworthy food supply chains.