A Refined Supervision Model of Rice Supply Chain Based on Multi-Blockchain

Area of Science:

• Agricultural Science
• Information Technology
• Supply Chain Management

Background:

• Growing demand for sustainable and precision agriculture in Agriculture 4.0.
• Rice quality and safety are critical for global health and social development.
• Need for robust supervision in the rice supply chain to prevent problematic products.

Purpose of the Study:

• To propose a multi-layer blockchain-based model for refined rice supply chain supervision.
• To enhance the security, transparency, and traceability of rice data.
• To address the challenges of data transmission, usage, and storage in the agricultural supply chain.

Main Methods:

• Analysis of information flow in the rice supply chain and construction of a key information classification table.
• Design of the Multi-blockchain Rice Refined Supervision Model (MBRRSM) framework.
• Implementation of multi-party hybrid encryption, secure multi-party computing, multi-mode storage, and the Supervision Proof of Peers (SPOP) consensus algorithm.

Main Results:

• MBRRSM enables fine-grained supervision of all data types within the rice supply chain.
• The model guarantees secure data transmission and usage for enterprise users with varying privacy needs.
• Security and performance analysis confirmed the model's capabilities, including the SPOP consensus algorithm.

Conclusions:

• Multi-blockchain technology offers a novel approach to refined supervision of the rice supply chain.
• The MBRRSM provides a secure and efficient solution for managing rice data, ensuring quality and safety.
• This research introduces new information field theories into agricultural research, supporting technological advancement in food security.