IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i6p1165-d1160057.html
   My bibliography  Save this article

Digital Twins and Data-Driven in Plant Factory: An Online Monitoring Method for Vibration Evaluation and Transplanting Quality Analysis

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)

  • Yanwei Yuan

    (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)

  • 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)

  • Shengbo Gao

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

  • Ruoshi Li

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

  • Hao Guo

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

  • Yongjun Zheng

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

Abstract

The plant factory transplanter is a key component of the plant factory system. Its operation status directly affects the quality and survival rate of planted seedlings, which in turn affects the overall yield and economic efficiency. To monitor the operation status and transplanting quality of a transplanting machine in a timely manner, the primary task is to use a computerized and easy-to-use method to monitor the transplanting units. Inspired by the latest developments in augmented reality and robotics, a digital twin model-based and data-driven online monitoring method for plant factory transplanting equipment is proposed. First, a data-driven and virtual model approach is combined to construct a multi-domain digital twin of the transplanting equipment. Then, taking the vibration frequency domain signal above the transplanting manipulator and the image features of the transplanting seedling tray as input variables, the evaluation method and configuration method of the plant factory transplanter digital twin system are proposed. Finally, the effect of the transplanter is evaluated, and the cycle can be repeated to optimize the transplanter to achieve optimal operation parameters. The results show that the digital twin model can effectively use the sensor data to identify the mechanical vibration characteristics and avoid affecting transplanting quality due to mechanical resonance. At a transplanting rate of 3000 plants/h, the transplanting efficiency can be maintained at a high level and the vibration signal of the X, Y, and Z-axis above the transplanting manipulator is relatively calm. In this case, Combined the optimal threshold method with the traditional Wiener algorithm, the identification rate of healthy potted seedlings can reach 94.3%. Through comprehensively using the optimal threshold method and 3D block matching filtering algorithm for image threshold segmentation and denoising, the recognition rate of healthy seedlings has reached over 96.10%. In addition, the developed digital twin can predict the operational efficiency and optimal timing of the detected transplanter, even if the environmental and sensor data are not included in the training. The proposed digital twin model can be used for damage detection and operational effectiveness assessment of other plant factory equipment structures.

Suggested Citation

  • Kaikang Chen & Yanwei Yuan & Bo Zhao & Liming Zhou & Kang Niu & Xin Jin & Shengbo Gao & Ruoshi Li & Hao Guo & Yongjun Zheng, 2023. "Digital Twins and Data-Driven in Plant Factory: An Online Monitoring Method for Vibration Evaluation and Transplanting Quality Analysis," Agriculture, MDPI, vol. 13(6), pages 1-18, May.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1165-:d:1160057
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/6/1165/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/6/1165/
    Download Restriction: no
    ---><---

    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tsega Y. Melesse & Chiara Franciosi & Valentina Di Pasquale & Stefano Riemma, 2023. "Analyzing the Implementation of Digital Twins in the Agri-Food Supply Chain," Logistics, MDPI, vol. 7(2), pages 1-17, June.
    2. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Agri-Tech Economics Papers 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    3. Uztürk, Deniz & Büyüközkan, Gülçin, 2022. "Smart Agriculture Technology Evaluation: A Linguistic-based MCDM Methodology," Land, Farm & Agribusiness Management Department 337128, Harper Adams University, Land, Farm & Agribusiness Management Department.
    4. Metta, Matteo & Ciliberti, Stefano & Obi, Chinedu & Bartolini, Fabio & Klerkx, Laurens & Brunori, Gianluca, 2022. "An integrated socio-cyber-physical system framework to assess responsible digitalisation in agriculture: A first application with Living Labs in Europe," Agricultural Systems, Elsevier, vol. 203(C).
    5. Gackstetter, David & von Bloh, Malte & Hannus, Veronika & Meyer, Sebastian T. & Weisser, Wolfgang & Luksch, Claudia & Asseng, Senthold, 2023. "Autonomous field management – An enabler of sustainable future in agriculture," Agricultural Systems, Elsevier, vol. 206(C).
    6. Ahmad Ali Hakam Dani & Suhono Harso Supangkat & Fetty Fitriyanti Lubis & I Gusti Bagus Baskara Nugraha & Rezky Kinanda & Irma Rizkia, 2023. "Development of a Smart City Platform Based on Digital Twin Technology for Monitoring and Supporting Decision-Making," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    7. Asif, Muhammad & Searcy, Cory & Castka, Pavel, 2023. "ESG and Industry 5.0: The role of technologies in enhancing ESG disclosure," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    8. Konstantina Ragazou & Alexandros Garefalakis & Eleni Zafeiriou & Ioannis Passas, 2022. "Agriculture 5.0: A New Strategic Management Mode for a Cut Cost and an Energy Efficient Agriculture Sector," Energies, MDPI, vol. 15(9), pages 1-17, April.
    9. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Land, Farm & Agribusiness Management Department 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    10. Büyüközkan, Gülçin & Uztürk, Deniz, 2022. "A Methodology to Investigate Challenges for Digital Twin Technology in Smart Agriculture," Agri-Tech Economics Papers 337119, Harper Adams University, Land, Farm & Agribusiness Management Department.
    11. Görkem Giray & Cagatay Catal, 2021. "Design of a Data Management Reference Architecture for Sustainable Agriculture," Sustainability, MDPI, vol. 13(13), pages 1-17, June.
    12. Mezzour Ghita & Benhadou Siham & Medromi Hicham & Mounaam Amine, 2022. "HT-TPP: A Hybrid Twin Architecture for Thermal Power Plant Collaborative Condition Monitoring," Energies, MDPI, vol. 15(15), pages 1-38, July.
    13. Maurizio Cutini & Carlo Bisaglia & Massimo Brambilla & Andrea Bragaglio & Federico Pallottino & Alberto Assirelli & Elio Romano & Alessandro Montaghi & Elisabetta Leo & Marco Pezzola & Claudio Maroni , 2023. "A Co-Simulation Virtual Reality Machinery Simulator for Advanced Precision Agriculture Applications," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
    14. Shuyao Li & Wenfu Wu & Yujia Wang & Na Zhang & Fanhui Sun & Feng Jiang & Xiaoshuai Wei, 2023. "Production Data Management of Smart Farming Based on Shili Theory," Agriculture, MDPI, vol. 13(4), pages 1-26, March.
    15. Xuehao Bi & Bo Wen & Wei Zou, 2022. "The Role of Internet Development in China’s Grain Production: Specific Path and Dialectical Perspective," Agriculture, MDPI, vol. 12(3), pages 1-14, March.
    16. Yogeswaranathan Kalyani & Liam Vorster & Rebecca Whetton & Rem Collier, 2024. "Application Scenarios of Digital Twins for Smart Crop Farming through Cloud–Fog–Edge Infrastructure," Future Internet, MDPI, vol. 16(3), pages 1-16, March.
    17. Rijswijk, Kelly & de Vries, Jasper R. & Klerkx, Laurens & Turner, James A., 2023. "The enabling and constraining connections between trust and digitalisation in incumbent value chains," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    18. Zhang, Chen & Di, Liping & Lin, Li & Li, Hui & Guo, Liying & Yang, Zhengwei & Yu, Eugene G. & Di, Yahui & Yang, Anna, 2022. "Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data," Agricultural Systems, Elsevier, vol. 201(C).
    19. Sergejs Kodors & Jelena Lonska & Imants Zarembo & Anda Zvaigzne & Ilmars Apeinans & Juta Deksne, 2024. "Knowledge-Based Recommendation System for Plate Waste Reduction in Latvian Schools," Sustainability, MDPI, vol. 16(19), pages 1-34, September.
    20. Ronit Purian, 2023. "Goal oriented indicators for food systems based on FAIR data," Papers 2302.09916, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:6:p:1165-:d:1160057. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.