Agricultural Products Traceability Research Articles

Empirical Research on Factors Influencing Chinese Farmers’ Adoption of Green Production Technologies

During a critical period of structural reform in China’s agricultural supply chain, accelerating the promotion and application of green production technologies emerges as a pivotal strategy to ensure the quality and safety of agricultural products while advancing agricultural modernization. This study empirically examines the factors influencing farmers’ adoption of green production technologies using an ordered logistic model based on survey data collected from 533 respondents in Shandong Province. The survey targeted regions where major economic crops such as corn and soybeans are cultivated, employing simple random sampling to ensure the data’s representativeness and reliability. The findings underscore several critical factors influencing farmers’ willingness to adopt green production technologies, including the presence of quality inspections, evaluations of restrictions on prohibited pesticide use, sales performance of green products, availability of government subsidies, and traceability of agricultural products. To foster greater adoption of green production technologies and propel the transformation of China’s agriculture, it is recommended to advocate and guide green agricultural practices, enhance green agricultural subsidy policies, and strengthen agricultural product market management systems. These measures are essential for ensuring sustainable agricultural development in China.

Salmon origin traceability based on hyperspectral imaging data fusion strategy and improved deep learning method

Salmon is subject to significant fraudulent activities due to its high economic value, making it crucial to accurately determine its geographical origin for trade, consumers, and food safety. This study aims to explore the spectral and textural features in Hyperspectral Imaging (HSI) data of salmons and investigate the potential of deep learning methods and information fusion strategy in salmon origin traceability. Additionally, an improved dung beetle optimization algorithm optimized Bidirectional Gated Recurrent Unit (BiGRU) deep learning model (CNN-BiGRU) was constructed to handle high-dimensional data accurately in discerning the origin of salmon. On the other hand, an ensemble learning model was built by stacking three base models (Random Forest, LightGBM, and Gradient Boosting Decision Tree), and a comprehensive evaluation of the predictive performance between the ensemble learning model and the base models was conducted. The results indicate that the classification performance of the ensemble learning model consistently outperforms the base models. However, compared with traditional machine learning methods, deep learning models demonstrate superior performance in handling high-dimensional data, particularly exhibiting robustness in handling fused data. Notably, the MSADBO–CNN–BiGRU model stands out among all algorithms, showing an improvement of 0.8%–7% in accuracy on the test set compared to the pre-optimized CNN model, with the best model achieving a test set accuracy of 99.5%. Overall, this study demonstrates that combining deep learning with information fusion strategy can rapidly and accurately identify the geographical origin of salmon, providing methods and insights for future traceability of agricultural products.

Traceability of Fruits and Vegetables Agricultural Products using Blockchain

This study explores the implementation of traceability systems utilizing blockchain technology in the agricultural sector, specifically focusing on fruits and vegetable products. Traceability is crucial for ensuring food safety, quality control, and supply chain transparency. Blockchain offers a decentralized and immutable ledger that enables the recording of every transaction or movement of agricultural products from farm to fork. By leveraging blockchain, stakeholders along the supply chain can access real-time, transparent, and tamper-proof information regarding the origin, cultivation, harvesting, processing, and distribution of fruits and vegetables. This enhances trust between consumers and producers, facilitates regulatory compliance, improves efficiency in recall management, and enables swift identification and resolution of food safety issues. Through case studies and analysis, this report evaluates the benefits, challenges, and future prospects of integrating blockchain-based traceability solutions into the agricultural industry, ultimately aiming to enhance food safety and consumer confidence.

Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality

The quality and safety of agricultural products involve a variety of risk factors, a large amount of risk information data, and multiple circulation and disposal processes, making it difficult to accurately trace the source of risks. To achieve precise traceability of agricultural product quality and safety, the monitoring and supervision level of agricultural product traceability systems needs to be improved. In this paper, the agricultural product quality- and safety-related detection data are taken as the research object, the agricultural product supply chain and business process are sorted out, the agricultural product risk factors under the influence of heavy metals are analyzed, and the agricultural product quality and safety risk evaluation model is established. The agricultural product risk traceability system was designed based on the six-layer architecture of the equipment layer, data layer, service layer, application layer, display layer, and user layer. Combined with the agricultural product quality and safety risk evaluation model, the system data were stored and modified by using the MySQL relational database, and the agricultural product risk traceability system was developed, which realized the accurate traceability of the risk link of the quality and safety of agricultural products. The research results of this paper provide an effective information means for the quality and safety supervision of agricultural products, and provide a technical reference for the development of a traceability system of risk factors related to agricultural products, which has good practical significance.

A Deep Vision Sensing-Based Smart Control Method for Apple-Picking Robots Under the Context of Agricultural E-Commerce

The growing development of the agricultural e-commerce industry is promoting the Digital transformation of agricultural production and logistics. Aiming at the problem of labor shortage in apple picking, this paper proposes a performance control method for robot apple picking based on a nonlinear wide convolutional neural network (NLWCNN) and multi-objective cooperative distance transformation algorithm (MOCDTA). This method utilizes deep learning algorithms and visual sensing technology to enable robots to accurately recognize apples and perform autonomous picking operations based on their maturity and position. We have designed a machine learning-based algorithm that enables robots to accurately recognize and distinguish apples of different maturity levels through training on a large amount of sample data. At the same time, we also combined the motion control algorithm of the robot to enable efficient picking operations based on the position and maturity information of the apples. The experimental results indicate that the NLWCNN method can significantly improve the efficiency and accuracy of apple picking, ensure the safety and traceability of agricultural products, increase consumer trust, and promote agricultural product transactions.

A Redactable Blockchain-Based Data Management Scheme for Agricultural Product Traceability.

With the development of agricultural information technology, the Internet of Things and blockchain have become important in the traceability of agricultural products. Sensors collect real-time data in agricultural production and a blockchain provides a secure and transparent storage medium for these data, which improves the transparency and credibility of agricultural product traceability. However, existing agricultural product traceability solutions are limited by the immutability of the blockchain, making it difficult to delete erroneous data and modify the scope of data sharing. This damages the credibility of traceability data and is not conducive to the exchange and sharing of information among enterprises. In this article, we propose an agricultural product traceability data management scheme based on a redactable blockchain. This scheme allows agricultural enterprises to encrypt data to protect privacy. In order to facilitate the maintenance and sharing of data, we introduce a chameleon hash function to provide data modification capabilities. Enterprises can fix erroneous data and update the access permissions of the data. To improve the efficiency of block editing, our scheme adopts a distributed block editing method. This method supports threshold editing operations, avoiding single-point-of-failure issues. We save records of data modifications on the blockchain and establish accountability mechanisms to identify malicious entities. Finally, in this paper we provide a security analysis of our proposed solution and verify its effectiveness through experiments. Compared with the existing scheme, the block generating speed is improved by 42% and the block editing speed is improved by 29.3% at 125 nodes.

Adoption decision of agricultural product traceability system in small and micro enterprises

PurposeDespite its crucial role in ensuring food safety, traceability remains underutilized by small and medium-sized enterprises (SMEs), a vital component of China’s agricultural supply chain, thereby compromising the integrity of the supply chain traceability system. Therefore, this study sets out to explore the factors influencing SMEs’ adoption of traceability systems and the impact of these factors on SMEs’ intent to adopt such systems. Furthermore, the study presents a model to deepen understanding of system adoption in SMEs and provides a simulation demonstrating the evolutionary trajectory of adoption behavior.Design/methodology/approachThis study considers the pivotal aspects of system adoption in SMEs, aiming to identify the influential factors through a grounded theory-based case study. Concurrently, it seeks to develop a mathematical model for SMEs’ adoption patterns and simulate the evolution of SMEs’ adoption behaviors using the Q-learning algorithm.FindingsThe adoption of traceability among SMEs is significantly influenced by factors such as system attributes, SMEs’ capability endowment, environmental factors and policy support and control. However, aspects of the SMEs’ capability endowment, specifically their learning rate and decay rate, have minimal impact on the adoption process. Furthermore, group pressure can expedite the attainment of an equilibrium state, wherein all SMEs adopt the system.Originality/valueThis study fills the existing knowledge gap about the adoption of traceability by SMEs in China’s agricultural supply chain. This study represents the pioneer study that identifies the factors influencing SMEs’ adoption and examines the effects of these factors on their traceability adoption, employing a multi-methodological approach that incorporates grounded theory, mathematical modeling and the Q-learning algorithm.

Research on Oracle Technology Based on Multi-Threshold Aggregate Signature Algorithm and Enhanced Trustworthy Oracle Reputation Mechanism.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle's historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

An improved PBFT consensus mechanism with trust value evaluation application in the agricultural product trusted traceability system

Blockchain has the advantages of tamper proof, high reliability and traceability, so it can better support the agricultural product traceability system. However, the agricultural product supply chain has the characteristics of long chain and decentralized production, which makes the application of the combination of blockchain technology and agricultural product traceability have many nodes, large system overhead and other problems. In order to solve the above problems, this paper proposes a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism based on trust value evaluation, and uses this optimization algorithm to propose an architecture model to meet the demand for trusted traceability of agricultural product supply chain, so as to realize the distributed storage and security protection of supply chain data. In order to ensure the effective operation of the blockchain system, this paper studies the consensus mechanism in the blockchain network, designs a trust value evaluation model to calculate the trust value of nodes, and selects trusted nodes to join the consensus group based on the trust value, which ensures the credibility of the consensus nodes. At the same time, the consistency protocol is optimized to reduce the communication overhead in the consensus process. The experimental results show that the consensus mechanism improved in this paper has certain advantages in communication overhead, transaction delay and throughput. Compared with the existing storage model, the model described in this paper has higher security and throughput efficiency, effectively ensures the reliable traceability of data, and can be better applied to the traceability scenario of agricultural product supply chain.

A peer-to-peer blockchain-based architecture for trusted and reliable agricultural product traceability

The lack of new and advanced technologies, such as the Internet of Things (IoT) and Fog computing, makes it challenging to trace the origin of agricultural products in supply chains. In addition, the system’s traditional centralized architecture cannot provide a trusted traceability service for farming goods in these supply chains. This paper proposes a peer-to-peer Blockchain-based architecture integrating self-sovereign identity (SSI) and a decentralized key management system (DKMS) for a trusted and reliable traceability service for agricultural food products in the supply chain networks. The public Blockchain network Fantom will provide faster and cheaper transactions. In contrast, SSI will ensure the identity of each entity by adopting DKMS technology on top of Blockchain to facilitate faster authenticity, integrity, and confidentiality service of the supply chain’s transaction and verification process. The system is deployed on the different public networks, i.e., Fantom and Ethereum, and shows that the proposed Fantom-based Blockchain network outperforms in terms of transaction and verification timing.

A Novel Blockchain-Based Model for Agricultural Product Traceability System

At present, the application of blockchain in the traceability system has become a mainstream technology to ensure the quality and safety of agricultural products. However, blockchain is designed to ensure open and transparent data and tamper-proof, and there is a deficiency in the privacy protection of data. In this study, a novel blockchain-based model for agricultural product traceability system is proposed, which has good generality and can protect the privacy of the agricultural product information in the traceability system. First, we designed a multi-channel data collection and uploading architecture, with one channel corresponding to the collection and uploading of information of one type of agricultural product, so as to meet the actual traceability needs of various types of agricultural products. Then, the information of agricultural products uploaded by each channel is encrypted by using the hierarchical encryption algorithm, and the encrypted information is stored in the blockchain to realize the secure sharing of agricultural product information. Finally, we evaluated the system model on the Hyperledger Fabric platform.

Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam.

Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, 1H NMR combined intelligent "rotation-invariant uniform local binary pattern" identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of 1H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products.

Identification of the Geographic Origin of Peanut Kernels by Raman Spectroscopy Fingerprinting with Chemometrics

This study aimed to investigate the feasibility of identifying the geographical origin of peanuts by combining Raman spectroscopy with chemometrics. A total of 161 peanut samples were collected from Jilin, Jiangsu, and Shandong provinces in China, and their Raman spectra were collected. One-way analysis of variance (ANOVA) was used to analyze the difference in characteristic Raman spectra of peanuts from these locations. Raman spectroscopy combined with principal component analysis (PCA), k-nearest neighbor (k-NN), stepwise linear discriminant analysis (SLDA), and support vector machines (SVM) were used to classify the peanuts by province and Jilin Province city. One-way ANOVA indicated that the peak intensities at 2900, 1660, 1440, 1077, and 848 cm−1 had significant differences. The peaks at 2900, 1660, 1440, 1300, and 1077 cm−1 had significant differences in the Jilin Province city. The correct identification rates were highest for k-NN. This study demonstrates the identification of the origin of peanuts by Raman spectroscopy with chemometrics and may provide technical support for the traceability of other agricultural products.

Construction of Jujube Quality and Safety Traceability System Based on Close Range Wireless Communication Technology

The quality and safety of jujube fruit is the key to ensuring the healthy development of the industry. Currently, there are problems such as unreasonable use of chemical fertilizers and pesticides in the traditional cultivation and management mode of the jujube industry. This article summarizes the construction of jujube quality and safety traceability system based on close range wireless communication technology. The system collects data on the use of pesticides, fertilizers, herbicides, and hormones during jujube production, processing, and circulation through NFC, and determines traceability information through the combination of hazard analysis of hazardous substances and key control points. It has developed a flexible mechanism and method for quality and safety traceability of tropical fruits and vegetables agricultural products, meeting the flexible management and configuration of traceability content, templates, and mechanisms, Realize the convenience of promoting the agricultural product traceability platform, and build an agricultural product quality and safety traceability management system according to the management needs of Hainan Agricultural Hi-Tech Zone, realizing functions such as enterprise settlement, product testing, and traceability information collection. Provide the application of the agricultural product quality and safety traceability system for enterprises settled in the agricultural Hi-Tech Zone, improving the settlement management level, and ensuring the reliability and traceability of agricultural product safety.

Research on the Impact of Digital Agriculture Development on Agricultural Green Total Factor Productivity

In this study, to discover how the growth of digital agriculture has impacted agricultural green total factor productivity (AGTFP), we take panel data from 2011 to 2019 for 30 Chinese provinces as the research object, measure the growth of AGTFP and digital agriculture development using the SBM-ML and entropy method, and use a fixed effect model to analyze the effects of digital agriculture development on AGTFP. The results demonstrate that (1) from the time-series characteristics, digital agriculture presented a steady growth state from 2011 to 2019; (2) during the study period, the technical efficiency index was slightly lower than the technological progress index in the AGTFP index, meaning that there is room for further development; (3) the relationship between the growth of digital agriculture and AGTFP presents an inverted U-shaped curve, with human capital playing a moderating role. Finally, corresponding countermeasures are proposed in four aspects: strengthening the construction of organizational mechanisms, building a standardized base for digital agriculture output, enhancing the traceability and certification of agricultural products, and improving social services in the agricultural industry.

Research on the application problems and countermeasures of blockchain in the traceability and circulation of agricultural products

With the improvement of Chinese residents' consumption level, people begin to put forward higher requirements for the safety, information transparency and query convenience of agricultural products, so it is particularly important to trace the source of agricultural products. This paper will comb and summarize the research and application of blockchain-based traceability system at home and abroad in recent years, and then study the current problems of blockchain traceability, and then explore the correct steps to build the traceability model of blockchain agricultural products. Finally, three suggestions are made to improve the application of blockchain agricultural products traceability in circulation, in order to provide reference for the research and establishment of blockchain agricultural products traceability system.

Design and Implementation of Traceability System Based on Blockchain

The fifth Plenary Session of the 19th CPC Central Committee included poverty alleviation and rural revitalization as the main goals of economic and social development during the 14th Five-Year Plan period. Moreover, it has created a new situation of digital sales of poverty alleviation and helps agricultural products such as "agriculture + e-commerce" and "agriculture + live streaming", and the traceability of agricultural products has attracted much attention. Com14bining the current social environment background and the investigation and understanding of the existing agricultural product sales mode of poverty alleviation, we found that the existing sales model chain and the traditional traceability system model have many shortcomings, such as unclear information sources and easy tampering. Based on this design, the idea of constructing a traceability system based on blockchain is proposed, and the required technical and economic feasibility is analyzed to confirm the realization of the idea. It is intended to meet the needs of agricultural product information management, poverty alleviation farmer information management, product traceability, etc. And through the web page and mobile terminal information interaction, the realization of agricultural products at any time, mobile, portable query. The final system test can ensure the normal operation of functions and the security, credibility, and transparency of information, provide new ideas and solutions for the field of targeted poverty alleviation, and help rural revitalization.

Edge Computing and Blockchain in Smart Agriculture Systems

The advancement of Internet-based technologies has made huge progress toward improving the accessibility of "smart agriculture." With the advent of unmanned and automatic management, smart agriculture is now able to accomplish monitoring, supervision, and real-time picture monitoring. It is not possible to know for sure that the data in a smart agriculture system is complete and secure from intrusion. This article investigates and assesses the potential of edge computing and blockchain for use in smart agriculture. We combine the advantages of blockchain technology and the edge computing framework to create a smart agriculture framework system that is based on a very straightforward analysis of the evolution of smart agriculture. The study proposes a thorough method for emphasizing the significance of agriculture and edge computing, as well as the advantages of incorporating blockchain technology in this context. This paper also proposes an intelligent agricultural product traceability system design: edge computing with blockchain for smart agriculture. The study concludes with a discussion of outstanding problems and difficulties that can arise during the creation of a blockchain-based edge computing system for smart agriculture systems.

Research on Blockchain-based Traceability System for Agricultural Products

To solve the problems of the existing agricultural products traceability system data storage security is not high, centralization is serious, data is easy to be tampered with and data trust, as well as to protect the quality and safety of agricultural products, safeguard the rights and interests of consumers and strengthen the supervision of agricultural products industry. In this paper, we propose a research on the safety traceability system of agricultural products based on blockchain on the traceability business process of the existing agricultural products traceability system. The decentralization of blockchain can ensure the immutability, transparency and accuracy of data traceability, solve the problems in traditional agricultural products traceability, improve the safety of agricultural products, and provide strong data support for the supervision of agricultural products industry. The advantages and challenges of implementing blockchain-based agricultural products traceability system are also elaborated, and the important links when applying blockchain technology to agricultural products traceability system are analyzed to provide reference for the research and establishment of blockchain agricultural products traceability system.

The Perception of the National Traceability Platform among Small-Scale Tea Farmers in Typical Agricultural Areas in Central China

As one of the key technologies to ensure the safety of agricultural products, the national traceability platform is being widely promoted in China. However, it has not yet been widely adopted among farmers, especially small-scale farmers. Farmers are both producers and direct participants in the traceability of agricultural products. Their perception directly affects the effectiveness of the promotion of the national traceability platform. This study explores the perception of the national traceability platform among small-scale tea farmers in typical agricultural areas in central China. This research employed Q methodology, an approach that integrates both qualitative and quantitative data allowing individuals' subjective understandings of a specific topic to be studied. The Q-sort procedure was performed in the field with 16 small-scale tea farmers. Next, Q-factor analyses were conducted using the Ken-Q analysis. The results show that small-scale tea farmers have different perceptions of the national traceability platform. Their main characteristics are active participation, resistant participation, risk aversion, and being driven by pressure. These four categories covered 52% of the perceived variance. Meanwhile, there is also a degree of internal consistency in the perception of small-scale tea farmers. Specifically, they are all concerned that participating in the national traceability platform may increase the cost and risk of cultivation and that it is difficult to obtain support from agricultural technicians. Therefore, understanding the perceptions of tea farmers of the national traceability platform is the premise for formulating effective promotion policies. Our research sheds light on the decision-making mechanisms for small-scale tea farmers to participate in national traceability platforms, further expanding the scope of current research on farmer behavior. This research has reference significance for promoting national traceability platforms in China and other countries around the world.