Pleurotus ostreatus is valued for its nutritional, medicinal, economic, and ecological benefits and is widely used in the food, pharmaceutical, and environmental protection industries. Pleurotus ostreatus, as a highly perishable edible fungus, faces significant challenges in supply chain quality control and food safety due to its short shelf life. As consumer demand for food freshness and full traceability increases, there is an urgent need to establish a reliable traceability system that enables real-time monitoring, spoilage prevention, and quality assurance. This study focuses on the Pleurotus ostreatus supply chain and designs and implements a multi-role flexible traceability system that integrates blockchain and the Internet of Things. The system collects key production and storage environment parameters in real time through sensor networks and enhances data accuracy and robustness using an improved adaptive weighted fusion algorithm, enabling precise monitoring of the growth environment and quality risks. The system adopts a “link-chain” mapping mechanism for multi-chain storage and dynamic reorganization of business processes. It incorporates attribute-based encryption strategies and smart contracts to support tiered data access and secure sharing among multiple parties. Key information is stored on the blockchain to prevent tampering, while auxiliary data is stored in off-chain databases and the Interplanetary File System to ensure efficient and verifiable data queries. Deployed at Shandong Qihe Ecological Agriculture Co., Ltd., No. 517, Xilou Village, Kunlun Town, Zichuan District, 255000, Zibo City, Shandong Province, China, the system covers 12 cultivation units and 60 sensor nodes, recording over 50,000 traceable data points. Experimental results demonstrate that the system outperforms baseline methods in query latency, data consistency, and environmental monitoring accuracy. The improved fusion algorithm reduced the total variance of environmental data by 20%. In practical application, the system reduced the spoilage rate of Pleurotus ostreatus by approximately 12.3% and increased the quality inspection pass rate by approximately 15.4%, significantly enhancing the supply chain’s quality control and food safety capabilities. The results show that the framework is feasible and scalable in terms of information credibility and operational efficiency and significantly improves food quality and safety monitoring throughout the production, storage, and distribution of Pleurotus ostreatus. This study provides a viable technological path for spoilage prevention, quality tracking, and digital food safety supervision, offering valuable insights for both food science research and practical applications.

Hymenopellis raphanipes is a high-value edible fungus with a short shelf life and high perishability, which poses significant challenges for quality control and safety assurance throughout its supply chain. Ensuring effective traceability is essential for improving production management, strengthening consumer trust, and supporting brand development. This study proposes a comprehensive traceability system tailored to the full lifecycle of Hymenopellis raphanipes, addressing the operational needs of producers and regulators alike. Through detailed analysis of the entire supply chain, from raw material intake, cultivation, and processing to logistics and sales, the system defines standardized traceability granularity and a unique hierarchical coding scheme. A multi-layered system architecture is designed, comprising a data acquisition layer, network transmission layer, storage management layer, service orchestration layer, business logic layer, and user interaction layer, ensuring modularity, scalability, and maintainability. To address performance bottlenecks in traditional systems, a multi-chain collaborative traceability model is introduced, integrating a mainchain–sidechain storage mechanism with an on-chain/off-chain hybrid management strategy. This approach effectively mitigates storage overhead and enhances response efficiency. Furthermore, data integrity is verified through hash-based validation, supporting high-throughput queries and reliable traceability. Experimental results from its real-world deployment demonstrate that the proposed system significantly outperforms traditional single-chain models in terms of query latency and throughput. The solution enhances data transparency and regulatory efficiency, promotes sustainable practices in green agricultural production, and offers a scalable reference model for the traceability of other high-value agricultural products.