Abstract
A framework combining the Internet of Things (IoT) and blockchain can help achieve system automation and credibility, and the corresponding technologies have been applied in many industries, especially in the area of agricultural product traceability. In particular, IoT devices (radio frequency identification (RFID), geographic information system (GIS), global positioning system (GPS), etc.) can automate the collection of information pertaining to the key aspects of traceability. The data are collected and input to the blockchain system for processing, storage, and query. A distributed, decentralized, and nontamperable blockchain can ensure the security of the data entering the system. However, IoT devices may generate abnormal data in the process of data collection. In this context, it is necessary to ensure the accuracy of the source data of the traceability system. Considering the whole-process traceability chain of agricultural products, this paper analyzes the whole-process information of a tea supply chain from planting to sales, constructs the system architecture and each function, and designs and implements a machine learning- (ML-) blockchain-IoT-based tea credible traceability system (MBITTS). Based on IoT technologies such as radio frequency identification (RFID) sensors, this article proposes a new method that combines blockchain and ML to enhance the accuracy of blockchain source data. In addition, system data storage and indexing methods and scanning and recovery mechanisms are proposed. Compared with the existing agricultural product (tea) traceability system based on blockchain, the introduction of the ML data verification mechanism can ensure the accuracy (up to 99%) of information on the chain. The proposed solution provides a basis to ensure the safety, reliability, and efficiency of agricultural traceability systems.
Highlights
As a valuable branch of agriculture, the tea industry has had a long history of development
To address the abovementioned challenges, we propose and design a machine learning- (ML-) blockchain-IoTbased tea credible traceability system (MBITTS)
Ethereum uses consensus mechanisms such as proof of work (POW), proof of stake (POS), and proof of authority (POA), and the encrypted hash value after the consensus is stored in the blockchain node of the ledger
Summary
As a valuable branch of agriculture, the tea industry has had a long history of development. The existing supply chain of tea has a complex structure, so it is necessary to establish an efficient and credible traceability system to realize the supervision and recall of tea. Such a system can help rapidly identify the relevant links and data to promptly solve the problem [3]. An ensemble learning algorithm (AdaBoost, XGBoost, gradient boosting decision tree (GBDT), or RF) is used to perform binary classification of the traceability data to filter the outliers.
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