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Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields

Author

Listed:
  • Wenbo Wei

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Maohua Xiao

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Weiwei Duan

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Hui Wang

    (Weichai Lovol Intelligent Agricultural Technology Co., Ltd., Weifang 261206, China)

  • Yejun Zhu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Cheng Zhai

    (Weichai Lovol Intelligent Agricultural Technology Co., Ltd., Weifang 261206, China)

  • Guosheng Geng

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

Agriculture is a labor-intensive industry. However, with the demographic shift toward an aging population, agriculture is increasingly confronted with a labor shortage. The technology for autonomous operation of agricultural equipment in large fields can improve productivity and reduce labor intensity, which can help alleviate the impact of population aging on agriculture. Nevertheless, significant challenges persist in the practical application of this technology, particularly concerning adaptability, operational precision, and efficiency. This review seeks to systematically explore the advancements in unmanned agricultural operations, with a focus on onboard environmental sensing, full-coverage path planning, and autonomous operational control technologies. Additionally, this review discusses the challenges and future directions of key technologies for the autonomous operation of agricultural equipment in large fields. This review aspires to serve as a foundational reference for the development of autonomous operation technologies for large-scale agricultural equipment.

Suggested Citation

  • Wenbo Wei & Maohua Xiao & Weiwei Duan & Hui Wang & Yejun Zhu & Cheng Zhai & Guosheng Geng, 2024. "Research Progress on Autonomous Operation Technology for Agricultural Equipment in Large Fields," Agriculture, MDPI, vol. 14(9), pages 1-20, August.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:9:p:1473-:d:1466937
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    References listed on IDEAS

    as
    1. Haoling Ren & Jiangdong Wu & Tianliang Lin & Yu Yao & Chang Liu, 2023. "Research on an Intelligent Agricultural Machinery Unmanned Driving System," Agriculture, MDPI, vol. 13(10), pages 1-19, September.
    2. Zhenling Cui & Hongyan Zhang & Xinping Chen & Chaochun Zhang & Wenqi Ma & Chengdong Huang & Weifeng Zhang & Guohua Mi & Yuxin Miao & Xiaolin Li & Qiang Gao & Jianchang Yang & Zhaohui Wang & Youliang Y, 2018. "Pursuing sustainable productivity with millions of smallholder farmers," Nature, Nature, vol. 555(7696), pages 363-366, March.
    3. Chenchen Ren & Xinyue Zhou & Chen Wang & Yaolin Guo & Yu Diao & Sisi Shen & Stefan Reis & Wanyue Li & Jianming Xu & Baojing Gu, 2023. "Ageing threatens sustainability of smallholder farming in China," Nature, Nature, vol. 616(7955), pages 96-103, April.
    4. Lihong Xu & Jiawei You & Hongliang Yuan, 2023. "Real-Time Parametric Path Planning Algorithm for Agricultural Machinery Kinematics Model Based on Particle Swarm Optimization," Agriculture, MDPI, vol. 13(10), pages 1-17, October.
    5. Chuandong Liang & Kui Pan & Mi Zhao & Min Lu, 2023. "Multi-Node Path Planning of Electric Tractor Based on Improved Whale Optimization Algorithm and Ant Colony Algorithm," Agriculture, MDPI, vol. 13(3), pages 1-19, February.
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    Cited by:

    1. Shuya Zhang & Hui Liu & Xiangchen Cao & Zhijun Meng, 2024. "Agricultural Machinery Movement Trajectory Recognition Method Based on Two-Stage Joint Clustering," Agriculture, MDPI, vol. 14(12), pages 1-16, December.
    2. Jin Yuan & Zichen Huang, 2024. "Intelligent Agricultural Machinery and Robots: Embracing Technological Advancements for a Sustainable and Highly Efficient Agricultural Future," Agriculture, MDPI, vol. 14(12), pages 1-3, November.

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