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Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems

Author

Listed:
  • Fan Zhang

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Wenyu Zhang

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Xiwen Luo

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Zhigang Zhang

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Yueteng Lu

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

  • Ben Wang

    (Key Laboratory of the Ministry of Education of China for Key Technologies for Agricultural Machine and Equipment, South China Agricultural University, Guangzhou 510642, China
    College of Engineering, South China Agricultural University, Guangzhou 510642, China)

Abstract

Smart farming uses advanced tools and technologies such as intelligent agricultural machines, high-precision sensors, navigation systems, and sophisticated computer systems to increase the economic benefits of agriculture and reduce the associated human effort. With the increasing demands of individualized farming operations, the internet of things is a crucial technique for acquiring, monitoring, processing, and managing the agricultural resource data of precision agriculture and ecological monitoring domains. Here, an internet of things-based system scheme integrating the most recent technologies for designing a management platform for agricultural machines equipped with automatic navigation systems is proposed. Various agricultural machinery cyber-models and their corresponding sensor nodes were constructed in a pre-production phase. Three key enabling technologies—multi-optimization of agricultural machinery scheduling, development of physical architecture and software, and integration of the controller-area-network with a mobile network—were addressed to support the system scheme. A demonstrative prototype system was developed and a case study was used to validate the feasibility and effectiveness of the proposed approach.

Suggested Citation

  • Fan Zhang & Wenyu Zhang & Xiwen Luo & Zhigang Zhang & Yueteng Lu & Ben Wang, 2022. "Developing an IoT-Enabled Cloud Management Platform for Agricultural Machinery Equipped with Automatic Navigation Systems," Agriculture, MDPI, vol. 12(2), pages 1-19, February.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:2:p:310-:d:754773
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    References listed on IDEAS

    as
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    Cited by:

    1. Huang Huang & Xinwei Cuan & Zhuo Chen & Lina Zhang & Hao Chen, 2023. "A Multiregional Agricultural Machinery Scheduling Method Based on Hybrid Particle Swarm Optimization Algorithm," Agriculture, MDPI, vol. 13(5), pages 1-18, May.
    2. Mohammad Amiri-Zarandi & Mehdi Hazrati Fard & Samira Yousefinaghani & Mitra Kaviani & Rozita Dara, 2022. "A Platform Approach to Smart Farm Information Processing," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    3. Zhikai Ma & Kun Chong & Shiwei Ma & Weiqiang Fu & Yanxin Yin & Helong Yu & Chunjiang Zhao, 2022. "Control Strategy of Grain Truck Following Operation Considering Variable Loads and Control Delay," Agriculture, MDPI, vol. 12(10), pages 1-14, September.

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