IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i5p651-d1380695.html
   My bibliography  Save this article

Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks

Author

Listed:
  • Zhiwei Tian

    (Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
    Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
    These authors contributed equally to this work.)

  • Ang Gao

    (College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271017, China
    These authors contributed equally to this work.)

  • Wei Ma

    (Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China)

  • Huanyu Jiang

    (Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China)

  • Dongping Cao

    (Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China)

  • Weizi Wang

    (Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
    Chengdu National Agricultural Science and Technology Center, Chengdu 610213, China)

  • Jianping Qian

    (Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China)

  • Lijia Xu

    (College of Mechanical and Electrical Engineering, Sichuan Agriculture University, Ya’an 625014, China)

Abstract

The mechanical properties of a plug seedling substrate determine whether it will crush during the transplantation, thereby affecting the integrity of the root system and the survival rate of transplanted seedlings. In this study, we measured eight morphological parameters of pepper seedlings using machine vision and physical methods, and the corresponding substrate mechanical parameters of the plug seedlings were tested using a texture analyzer. Based on the experimental data, a BPNN framework was constructed to predict the substrate mechanical properties of plug seedlings at different growth stages. The results indicate that the BPNN with a framework of [8, 15, 15, 1] exhibits higher R 2 and lower errors. The mean absolute error ( MAE ), mean squared error ( MSE ), and mean absolute percentage error ( MAPE ) values are 7.669, 88.842, and 9.076%, respectively, with an R 2 of 0.867. The average prediction accuracy of 20 test data set is 90.472%. Finally, predictions and experimental validations were conducted on the substrate mechanical properties of seedlings grown for 47 days. The results revealed that the BPNN achieved an average prediction accuracy of 93.282%. Additionally, it exhibited faster speed and lower computational costs. This study provides a reference for the non-intrusive estimation of substrate mechanical properties in plug seedlings and the design and optimization of transplanting an end-effector.

Suggested Citation

  • Zhiwei Tian & Ang Gao & Wei Ma & Huanyu Jiang & Dongping Cao & Weizi Wang & Jianping Qian & Lijia Xu, 2024. "Modeling the Mechanical Properties of Root–Substrate Interaction with a Transplanter Using Artificial Neural Networks," Agriculture, MDPI, vol. 14(5), pages 1-12, April.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:651-:d:1380695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/5/651/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/5/651/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2023. "Prediction of Pea ( Pisum sativum L.) Seeds Yield Using Artificial Neural Networks," Agriculture, MDPI, vol. 13(3), pages 1-19, March.
    2. Wei Liu & Shijie Tian & Qingyu Wang & Huanyu Jiang, 2023. "Key Technologies of Plug Tray Seedling Transplanters in Protected Agriculture: A Review," Agriculture, MDPI, vol. 13(8), pages 1-19, July.
    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. Jinbo Ren & Chongcheng Chen & Difa Bao & Xinhui Wu & Shuhe Zheng, 2024. "Simulation Analysis and Optimization Design of Paddy Field Mud Spreader Blades for Uniform Dispersion," Agriculture, MDPI, vol. 14(3), pages 1-20, February.
    2. Aliakbar Mohammadi Mirik & Mahdieh Parsaeian & Abbas Rohani & Shaneka Lawson, 2023. "Optimizing Linseed ( Linum usitatissimum L.) Seed Yield through Agronomic Parameter Modeling via Artificial Neural Networks," Agriculture, MDPI, vol. 14(1), pages 1-21, December.
    3. Gniewko Niedbała & Sebastian Kujawa, 2023. "Digital Innovations in Agriculture," Agriculture, MDPI, vol. 13(9), pages 1-10, August.
    4. Sheng Sun & Bin Hu & Xinming Wu & Xin Luo & Jian Wang, 2024. "Research on a Vibrationally Tuned Directional Seed Supply Method Based on ADAMS-EDEM Coupling and the Optimization of System Parameters," Agriculture, MDPI, vol. 14(3), pages 1-19, March.
    5. Jarosław Kurek & Gniewko Niedbała & Tomasz Wojciechowski & Bartosz Świderski & Izabella Antoniuk & Magdalena Piekutowska & Michał Kruk & Krzysztof Bobran, 2023. "Prediction of Potato ( Solanum tuberosum L.) Yield Based on Machine Learning Methods," Agriculture, MDPI, vol. 13(12), pages 1-25, December.

    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:14:y:2024:i:5:p:651-:d:1380695. 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.