IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i9p1460-d914358.html
   My bibliography  Save this article

Development and Application of a Remote Monitoring System for Agricultural Machinery Operation in Conservation Tillage

Author

Listed:
  • Changhai Luo

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Jingping Chen

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
    These authors contributed equally to this work.)

  • Shuxia Guo

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Xiaofei An

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Yanxin Yin

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Changkai Wen

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Huaiyu Liu

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Zhijun Meng

    (Intelligent Equipment Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China)

  • Chunjiang Zhao

    (National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China)

Abstract

There is an increasing demand for remote monitoring and management of agricultural machinery operation in conservation tillage. Considering the problems of large errors in detecting operation quality parameters, such as tillage depth and corn straw cover rate, in complex farmland environments, this paper proposes a tillage depth measurement method based on the dual attitude compound of a tractor body and three-point hitch mechanism with lower pull rod and an online measurement method based on K-means clustering of the corn straw cover rate on farmland surface. An operation monitoring terminal was developed for the remote collection of quality parameters of conservation tillage field operation. A remote monitoring system of agricultural machinery operation was constructed and applied over a large area. The field tests showed that the static mean error and root-mean-square error of this method were 0.16 and 0.67 cm for uphill and 0.36 and 0.57 cm for downhill, respectively. For the 28 and 33 cm tillage depth tests, the mean dynamic measurement errors of this method were 0.55 and 0.61 cm, and the root means square errors were 0.64 and 0.73 cm, respectively, and the coefficient of variation of tillage depth did not exceed 3%. The correlation coefficient between the corn straw cover rate detection algorithm based on K-means clustering and the manual image marking method reached 0.92, with an average error of 9.69%, and the accuracy filled the demand for straw cover rate detection. The detection accuracy of tillage depth and straw cover rate was high and thus provides an effective means of information technology support for the quality monitoring and production management of conservation tillage farming operations.

Suggested Citation

  • Changhai Luo & Jingping Chen & Shuxia Guo & Xiaofei An & Yanxin Yin & Changkai Wen & Huaiyu Liu & Zhijun Meng & Chunjiang Zhao, 2022. "Development and Application of a Remote Monitoring System for Agricultural Machinery Operation in Conservation Tillage," Agriculture, MDPI, vol. 12(9), pages 1-22, September.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1460-:d:914358
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/9/1460/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/12/9/1460/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marco Pittarello & Francesca Chiarini & Cristina Menta & Lorenzo Furlan & Paolo Carletti, 2022. "Changes in Soil Quality through Conservation Agriculture in North-Eastern Italy," Agriculture, MDPI, vol. 12(7), pages 1-12, July.
    2. Ping Xue & Xinru Han & Yongchun Wang & Xiudong Wang, 2022. "Can Agricultural Machinery Harvesting Services Reduce Cropland Abandonment? Evidence from Rural China," Agriculture, MDPI, vol. 12(7), pages 1-15, June.
    3. Yucui Ning & Xu Wang & Yanna Yang & Xu Cao & Yulong Wu & Detang Zou & Dongxing Zhou, 2022. "Studying the Effect of Straw Returning on the Interspecific Symbiosis of Soil Microbes Based on Carbon Source Utilization," Agriculture, MDPI, vol. 12(7), pages 1-16, July.
    4. Shuzhen Yang & Bocai Jia & Tao Yu & Jin Yuan, 2022. "Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot," Agriculture, MDPI, vol. 12(7), pages 1-24, 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. Jin Yuan & Wei Ji & Qingchun Feng, 2023. "Robots and Autonomous Machines for Sustainable Agriculture Production," Agriculture, MDPI, vol. 13(7), pages 1-4, July.
    2. Lei Niu & Lulu Yuan & Zhongmin Ding & Yifu Zhao, 2023. "How Do Support Pressure and Urban Housing Purchase Affect the Homecoming Decisions of Rural Migrant Workers? Evidence from Rural China," Agriculture, MDPI, vol. 13(8), pages 1-28, July.
    3. Rimantas Barauskas & Andrius Kriščiūnas & Dalia Čalnerytė & Paulius Pilipavičius & Tautvydas Fyleris & Vytautas Daniulaitis & Robertas Mikalauskis, 2022. "Approach of AI-Based Automatic Climate Control in White Button Mushroom Growing Hall," Agriculture, MDPI, vol. 12(11), pages 1-25, November.
    4. Yuan Hu & Ziyang Zhou & Li Zhou & Caiming Liu, 2024. "Self-Owned or Outsourced? The Impact of Farm Machinery Adoption Decisions on Chinese Farm Households’ Operating Income," Agriculture, MDPI, vol. 14(11), pages 1-26, October.
    5. Linyi Zheng & Liufang Su & Songqing Jin, 2023. "Reducing land fragmentation to curb cropland abandonment: Evidence from rural China," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 71(3-4), pages 355-373, September.
    6. Yangxiao Lu & Suhao Wei, 2024. "Outsourcing of Agricultural Machinery Operation Services and the Sustainability of Farmland Transfer Market: Promoting or Inhibiting?," Sustainability, MDPI, vol. 16(22), pages 1-19, November.
    7. Ruining Li & Yanli Yu, 2022. "Impacts of Green Production Behaviors on the Income Effect of Rice Farmers from the Perspective of Outsourcing Services: Evidence from the Rice Region in Northwest China," Agriculture, MDPI, vol. 12(10), pages 1-27, October.
    8. Sha Feng & Dandan Fu & Xinru Han & Xiudong Wang, 2022. "Impacts of the Extension of Cassava Soil Conservation and Efficient Technology on the Reduction of Chemical Fertilizer Input in China," Sustainability, MDPI, vol. 14(22), pages 1-13, November.

    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:12:y:2022:i:9:p:1460-:d:914358. 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.