• An adaptive method for fish growth prediction with empirical knowledge extraction

      Li, Hui; Chen, Yingyi; Li, Wensheng; Wang, Qingbin; Duan, Yanqing; Chen, Tao; ; University of Surrey; China Agricultural University; Laizhou Mingbo Aquatic Products Co., Ltd; et al. (Elsevier, 2021-11-25)
      Fish growth prediction provides important information for optimising production in aquaculture. Fish usually exhibit different growth characteristics due to the variations in the environment, the equipment used in different fish workshops and inconsistent application by operators of empirical rules varying from one pond to another. To address this challenge, the aim of this study is to develop an adaptive fish growth prediction method in response to feeding decision. Firstly, the practical operational experience in historical feeding decisions for different fish weights is extracted to establish the feeding decision model. Then, a fish weight prediction model is established by regression analysis methods based on historical fish production data analysis. The feeding decision model is integrated as the input information of the fish weight prediction model to obtain fish weight prediction. Furthermore, an adaptive fish growth prediction strategy is proposed by continuously updating model parameters using new measurements to adapt to specific characteristics. The proposed adaptive fish growth prediction method with empirical knowledge extraction is evaluated by the collected production data of spotted knifejaw (Oplegnathus punctatus). The results show that established models can achieve a good balance between goodness-of-fit and model complexity, and the adaptive prediction method can adapt to specific fish pond’s characteristics and provide a more effective way to increase fish weight prediction accuracy. The proposed method provides an important contribution to achieving adaptive fish growth prediction in a real time from the view of aquaculture practice for spotted knifejaw.
    • Application of graphene-based materials for detection of nitrate and nitrite in water—a review

      Li, Daoliang; Wang, Tan; Li, Zhen; Xu, Xianbao; Wang, Cong; Duan, Yanqing; China Agricultural University; University of Bedfordshire (MDPI AG, 2019-12-20)
      Nitrite and nitrate are widely found in various water environments but the potential toxicity of nitrite and nitrate poses a great threat to human health. Recently, many methods have been developed to detect nitrate and nitrite in water. One of them is to use graphene-based materials. Graphene is a two-dimensional carbon nano-material with sp2 hybrid orbital, which has a large surface area and excellent conductivity and electron transfer ability. It is widely used for modifying electrodes for electrochemical sensors. Graphene based electrochemical sensors have the advantages of being low cost, effective and efficient for nitrite and nitrate detection. This paper reviews the application of graphene-based nanomaterials for electrochemical detection of nitrate and nitrite in water. The properties and advantages of the electrodes were modified by graphene, graphene oxide and reduced graphene oxide nanocomposite in the development of nitrite sensors are discussed in detail. Based on the review, the paper summarizes the working conditions and performance of different sensors, including working potential, pH, detection range, detection limit, sensitivity, reproducibility, repeatability and long-term stability. Furthermore, the challenges and suggestions for future research on the application of graphene-based nanocomposite electrochemical sensors for nitrite detection are also highlighted.
    • Applying blockchain technology to improve agri-food traceability: a review of development methods, benefits and challenges

      Feng, Huanhuan; Wang, Xiang; Duan, Yanqing; Zhang, Jian; Zhang, Xiaoshuan; University of Bedfordshire; China Agricultural University; Beijing Information Science and Technology University (Elsevier, 2020-03-11)
      Traceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability.
    • Automatic recognition methods of fish feeding behavior in aquaculture: a review

      Li, Daoliang; Wang, Zhenhu; Wu, Suyuan; Miao, Zheng; Du, Ling; Duan, Yanqing; ; China Agricultural University; Renmin University of China; University of Bedfordshire (Elsevier, 2020-05-23)
      Feeding is a major factor that determines the production costs and water quality of aquaculture. Analysis of fish feeding behavior forms an important part of the feeding optimization. Fish feeding has generally been performed with automatic feeding machines which can lead to excessive or insufficient feeding. Recognition of fish feeding behavior can provide valuable input for optimizing feeding quantity. Due to the complexity of the environment and the uncertainty of fish behavior, the correlation and accuracy of behavior recognition are generally low. The accurate identification of fish feeding behavior till faces substantial challenges. This paper reviews the technical methods that have been used to identify fish feeding behavior in aquaculture over the past 30 years. The advantages and disadvantages of each method under different experimental conditions and applications are analyzed. Many methods are effective at evaluating and quantifying fish feeding intensity, but the recognition accuracy still needs further improvement. It is proposed by this paper that technologies such as data fusion and deep learning has great potential for improving the recognition of fish feeding behavior.
    • Developing a real-time monitoring traceability system for cold chain of Tricholoma matsutake

      Li, Xinwu; Yang, Lin; Duan, Yanqing; Wu, Zhigang; Zhang, Xiaoshuan; China Agricultural University; Tibet Agricultural and Animal Husbandry College; University of Bedfordshire (MDPI, 2019-04-11)
    • Development and evaluation of a brine mining equipment monitoring and control system using wireless sensor network and fuzzy logic

      He, Liu; Cui, Yan; Duan, Yanqing; Stankovski, Stevan; Zhang, Xiaoshuan; Zhang, Jian; China Agricultural University; University of Bedfordshire; University of Novi sad; Beijing Information Science & Technology University (SAGE, 2017-03-29)
      The brine mining equipment failure can seriously affect the productivity of the salt lake chemical industry. Traditional monitoring and controlling method mainly depends on manned patrol that is offline and ineffective. With the rapid advancement of information and communication technologies, it is possible to develop more efficient online systems that can automatically monitor and control the mining equipment and to prevent equipment damage from mechanical failure and unexpected interruptions with severe consequences. This paper describes a Wireless Monitoring and feedback fuzzy logic-based Control System (WMCS) for monitoring and controlling the brine well mining equipment. Based on the field investigations and requirement analysis, the WMCS is designed as a Wireless Sensors Network module, a feedback fuzzy logic controller, and a remote communication module together with database platform. The system was deployed in existing brine wells at demonstration area without any physical modification. The system test and evaluation results show that WMCS enables to track equipment performance and collect real-time data from the spot, provides decision support to help workers overhaul the equipment and follows the deployment of fuzzy control in conjunction with remote data logging. It proved that WMCS acts as a tool to improve management efficiency for mining equipment and underground brine resources.
    • Factors affecting consumers’ purchase intention of eco-friendly food in China: the evidence from respondents in Beijing

      He, Qile; Duan, Yanqing; Wang, Ruowei; Fu, Zetian; Coventry University; University of Bedfordshire; China Agricultural University (Wiley, 2019-05-07)
      The purpose aims to examine the key factors influencing Chinese consumer’s purchasing behaviour of eco-friendly food in China giving its context as an emerging economy and its rapidly rising importance in the world eco-friendly food market. This paper adopts and extends the Responsible Environmental Behaviour (REB) theory by empirically testing key psycho-social factors influencing the purchase intention of eco-friendly food and the moderating effects of consumers’ demographic characteristics on the relationship between the key psycho-social factors and the purchase intention.  A number of hypotheses are proposed. A questionnaire was designed and distributed via online survey in Beijing, China.  A total of 239 valid responses were received. The empirical data was used to test the research hypotheses using the hierarchical multiple regression analysis. The research finds that the personality factors in the REB model (i.e., pro-environmental attitudes, the internal locus of control and personal responsibly) have significant positive effects on the consumers’ eco-friendly food purchase intention. Such effect is stable across consumers with different income levels. On the other hand, the knowledge-skill factors in the REB model do not have significant effect on the purchase intention of consumers. This study contributes to a better understanding of factors affecting eco-friendly food consumption intention in China and the behavioural characteristics of consumers in developing countries. Moreover, the findings also shed light on the applicability of the REB theory in emerging economies and a specific industrial context.
    • A framework for the successful implementation of food traceability systems in China

      Duan, Yanqing; Mao, Meiyin; Wang, Ruimei; Fu, Zetian; Xu, Mark; University of Bedfordshire; China Agricultural University; University of Portsmouth (Taylor & Francis, 2017-06-02)
      Implementation of food traceability systems in China faces many challenges due to the scale, diversity and complexity of China’s food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a pre-study helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in eighty-three food companies. This study identifies six dimensions for critical success factors: laws, regulations and standards; government support; consumer knowledge and support; effective management and communication; top management and vendor support; and information and system quality.
    • Recent advances in sensor fault diagnosis: a review

      Li, Daoliang; Wang, Ying; Wang, Jinxing; Wang, Cong; Duan, Yanqing; China Agricultural University; Shandong Agricultural University; University of Bedfordshire (Elsevier, 2020-05-11)
      As an essential component of data acquisition systems, sensors have been widely used, especially in industrial and agricultural sectors. However, sensors are also prone to faults due to their harsh working environment. Therefore, the early identification of sensor faults is critical for making corrective actions to mitigate the impact. This paper provides a comprehensive review on the contemporary fault diagnosis techniques and helps researchers and practitioners to understand the current state of the art development in this emerging field. The paper introduces the common fault types and causes in sensors, and different types’ methods for fault diagnosis used in industry and agriculture sectors. It discusses the advantages and disadvantages of these methods, highlights the current challenges, and offers recommendations for future research directions.