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Digital Twins in Plant Factory: A Five-Dimensional Modeling Method for Plant Factory Transplanter Digital Twins

Author

Listed:
  • Kaikang Chen

    (Department of Electrical and Mechanical Engineering, College of Engineering, China Agricultural University, Beijing 100089, China
    National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Bo Zhao

    (National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Haiyan Zhou

    (National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Liming Zhou

    (National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Kang Niu

    (National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Xin Jin

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Ruoshi Li

    (College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang 471003, China)

  • Yanwei Yuan

    (National Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China)

  • Yongjun Zheng

    (Department of Electrical and Mechanical Engineering, College of Engineering, China Agricultural University, Beijing 100089, China)

Abstract

To address challenges such as the complex correlations among multiple parameters during the modeling process of plant factory transplanters, the large differences between simulations and actual models, and the difficulties in data acquisition and processing, this paper proposes the concept of a Plant Factory Transplanter (PFT) digital twin five-dimensional model based on research of plant factory transplanters. The PFT digital twin five-dimensional model builds on traditional 3D modeling and includes physical entities, virtual models, services, twin data, and connecting interactions. This study delves deeply into the connotations and construction methods of the PFT five-dimensional model from the five aspects of PFT physical entity, virtual entity, services, twin data, and connections, and illustrates the implementation steps and effects of each link. Finally, practical examples of the application of the PFT digital twin five-dimensional model are presented in actual scenarios. The five-dimensional modeling approach for plant factory transplanters based on digital twins can monitor the working status of transplanters online and evaluate the effectiveness of transplantation. This method overcomes problems such as poor adaptability and difficulty in updating physical models, thus improving the efficiency of monitoring and optimizing configuration parameters. Moreover, the generated virtual entities are more intuitively reflected in the control interface, significantly reducing the reliance of equipment operators on relevant professional skills. In the future, the proposed digital twin five-dimensional model is expected to be further refined and optimized, with creation tools and application scenarios studied. Application research will also be conducted to meet different application requirements.

Suggested Citation

  • Kaikang Chen & Bo Zhao & Haiyan Zhou & Liming Zhou & Kang Niu & Xin Jin & Ruoshi Li & Yanwei Yuan & Yongjun Zheng, 2023. "Digital Twins in Plant Factory: A Five-Dimensional Modeling Method for Plant Factory Transplanter Digital Twins," Agriculture, MDPI, vol. 13(7), pages 1-18, June.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:7:p:1336-:d:1184119
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    References listed on IDEAS

    as
    1. Verdouw, Cor & Tekinerdogan, Bedir & Beulens, Adrie & Wolfert, Sjaak, 2021. "Digital twins in smart farming," Agricultural Systems, Elsevier, vol. 189(C).
    2. Mingyong Li & Liqiang Xiao & Xiqiang Ma & Fang Yang & Xin Jin & Jiangtao Ji, 2022. "Vision-Based a Seedling Selective Planting Control System for Vegetable Transplanter," Agriculture, MDPI, vol. 12(12), pages 1-14, December.
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