Food traceability has been one of the emerging blockchain applications in recent years, for improving the areas of anti-counterfeiting and quality assurance. Existing food traceability systems do not guarantee a high level of system reliability, scalability, and information accuracy. Moreover, the traceability process is time-consuming and complicated in modern supply chain networks. To alleviate these concerns, blockchain technology is promising to create a new ontology for supply chain traceability. However, most consensus mechanisms and data flow in blockchain are developed for cryptocurrency, not for supply chain traceability; hence, simply applying blockchain technology to food traceability is impractical. In this paper, a blockchain-IoT-based food traceability system (BIFTS) is proposed to integrate the novel deployment of blockchain, IoT technology, and fuzzy logic into a total traceability shelf life management system for managing perishable food. To address the needs for food traceability, lightweight and vaporized characteristics are deployed in the blockchain, while an integrated consensus mechanism that considers shipment transit time, stakeholder assessment, and shipment volume is developed. The data flow of blockchain is then aligned to the deployment of IoT technologies according to the level of traceable resource units. Subsequently, the decision support can be established in the food supply chain by using reliable and accurate data for shelf life adjustment, and by using fuzzy logic for quality decay evaluation.

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Blockchain-Driven IoT for Food Traceability With an Integrated Consensus Mechanism

Smart logistics is an effective way to meet the challenges of fast-changing customer expectations, take opportunities brought by new technologies, and facilitate new business models. This review pa...

Smart logistics based on the internet of things technology: an overview

Traditional traceability system has problems of centralized management, opaque information, untrustworthy data, and easy generation of information islands. To solve the above problems, this paper designs a traceability system based on blockchain technology for storage and query of product information in supply chain of agricultural products. Leveraging the characteristics of decentralization, tamper-proof and traceability of blockchain technology, the transparency and credibility of traceability information increased. A dual storage structure of “database + blockchain” on-chain and off-chain traceability information is constructed to reduce load pressure of the chain and realize efficient information query. Blockchain technology combined with cryptography is proposed to realize the safe sharing of private information in the blockchain network. In addition, we design a reputation-based smart contract to incentivize network nodes to upload traceability data. Furthermore, we provide performance analysis and practical application, the results show that our system improves the query efficiency and the security of private information, guarantees the authenticity and reliability of data in supply chain management, and meets actual application requirements.

/pdf/a-trusted-blockchain-based-traceability-system-for-fruit-and-5fjyga2x9u.pdf

A Trusted Blockchain-Based Traceability System for Fruit and Vegetable Agricultural Products

Background Digital twins have advanced fast in various industries, but are just emerging in postharvest supply chains. A digital twin is a virtual representation of a certain product, such as fresh horticultural produce. This twin is linked to the real-world product by sensors supplying data of the environmental conditions near the target fruit or vegetable. Statistical and data-driven twins quantify how quality loss of fresh horticultural produce occurs by grasping patterns in the data. Physics-based twins provide an augmented insight into the underlying physical, biochemical, microbiological and physiological processes, enabling to explain also why this quality loss occurs. Scope and approach We identify what the key advantages are of digital twins and how the supply chain of fresh horticultural produce can benefit from them in the future. Key findings and conclusions A digital twin has a huge potential to help horticultural produce to tell its history as it drifts along throughout its postharvest life. The reason is that each shipment is subject to a unique and unpredictable set of temperature and gas atmosphere conditions from farm to consumer. Digital twins help to identify the resulting, largely uncharted, postharvest evolution of food quality. The benefit of digital twins particularly comes forward for perishable species and at low airflow rates. Digital twins provide actionable data for exporters, retailers, and consumers, such as the remaining shelf life for each shipment, on which logistics decisions and marketing strategies can be based. The twins also help diagnose and predict potential problems in supply chains that will reduce food quality and induce food loss. Twins can even suggest preventive shipment-tailored measures to reduce retail and household food losses.

Digital twins are coming: Will we need them in supply chains of fresh horticultural produce?

An intelligent IoT-based control and traceability system to forecast and maintain water quality in freshwater fish farms

Development and evaluation of an intelligent traceability system for waterless live fish transportation

Survival prediction system for waterless live Chinese Sturgeon transportation based on temperature related glucose changes

Accurate prediction of forthcoming oxygen concentration during waterless live fish transportation plays a key role in reducing the abnormal occurrence, increasing the survival rate in delivery operations, and optimizing manufacturing costs. The most effective ambient monitoring techniques that are based on the analysis of historical process data when performing forecasting operations do not fully consider current ambient influence. This is likely lead to a greater deviation in on-line oxygen level forecasting in real situations. Therefore, it is not advisable for the system to perform early warning and on-line air adjustment in delivery. In this paper, we propose a hybrid method and its implementation system that combines a gray model (GM (1, 1)) with least squares support vector machines (LSSVM) that can be used effectively as a forecasting model to perform early warning effectively according to the dynamic changes of oxygen in a closed system. For accurately forecasting of the oxygen level, the fuzzy C-means clustering (FCM) algorithm was utilized for classification according to the flatfish’s physical features—i.e., length and weight—for more pertinent training. The performance of the gray model-particle swarm optimization-least squares support vector machines (GM-PSO-LSSVM) model was compared with the traditional modeling approaches of GM (1, 1) and LSSVM by applying it to predict on-line oxygen level, and the results showed that its predictions were more accurate than those of the LSSVM and grey model. Therefore, it is a suitable and effective method for abnormal condition forecasting and timely control in the waterless live transportation of flatfish.

Liu Yan

Papers

Development and evaluation of an intelligent traceability system for waterless live fish transportation

Survival prediction system for waterless live Chinese Sturgeon transportation based on temperature related glucose changes

An On-Line Oxygen Forecasting System for Waterless Live Transportation of Flatfish Based on Feature Clustering