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

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  • 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
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    References listed on IDEAS

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    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.
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