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Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing

Author

Listed:
  • Yong Li

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    Henan Engineering Laboratory of Farmland Monitoring and Control, Zhengzhou 450046, China)

  • Hebing Liu

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    Henan Engineering Laboratory of Farmland Monitoring and Control, Zhengzhou 450046, China)

  • Jialing Wei

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    Henan Engineering Laboratory of Farmland Monitoring and Control, Zhengzhou 450046, China)

  • Xinming Ma

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Guang Zheng

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China)

  • Lei Xi

    (College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
    Henan Engineering Laboratory of Farmland Monitoring and Control, Zhengzhou 450046, China)

Abstract

The application of deep learning (DL) technology to the identification of crop growth processes will become the trend of smart agriculture. However, using DL to identify wheat growth stages on mobile devices requires high battery energy consumption, significantly reducing the device’s operating time. However, implementing a DL framework on a remote server may result in low-quality service and delays in the wireless network. Thus, the DL method should be suitable for detecting wheat growth stages and implementable on mobile devices. A lightweight DL-based wheat growth stage detection model with low computational complexity and a computing time delay is proposed; aiming at the shortcomings of high energy consumption and a long computing time, a wheat growth period recognition model and dynamic migration algorithm based on deep reinforcement learning is proposed. The experimental results show that the proposed dynamic migration algorithm has 128.4% lower energy consumption and 121.2% higher efficiency than the local implementation at a wireless network data transmission rate of 0–8 MB/s.

Suggested Citation

  • Yong Li & Hebing Liu & Jialing Wei & Xinming Ma & Guang Zheng & Lei Xi, 2023. "Research on Winter Wheat Growth Stages Recognition Based on Mobile Edge Computing," Agriculture, MDPI, vol. 13(3), pages 1-16, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:534-:d:1077568
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    References listed on IDEAS

    as
    1. Xinhui Ding & Wenjuan Zhang, 2021. "Computing Unloading Strategy of Massive Internet of Things Devices Based on Game Theory in Mobile Edge Computing," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, March.
    2. Petra Hellegers, 2022. "Food security vulnerability due to trade dependencies on Russia and Ukraine," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 14(6), pages 1503-1510, December.
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    Cited by:

    1. Xiuguo Zou & Zheng Liu & Xiaochen Zhu & Wentian Zhang & Yan Qian & Yuhua Li, 2023. "Application of Vision Technology and Artificial Intelligence in Smart Farming," Agriculture, MDPI, vol. 13(11), pages 1-4, November.

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