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Application of big data technology in agricultural Internet of Things

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  • Chunling Zhang
  • Zunfeng Liu

Abstract

This article first analyzes big data technology. Then, the agricultural Internet of Things system was established, and the acquisition of agricultural data was achieved through the establishment of sensor modules, image acquisition modules, and meteorological acquisition modules. The data are transmitted to the server through GPRS communication technology and 3G network card to realize data transmission. The Web Service technology is used to connect the Internet of Things with the neural network model to achieve data interoperability. By comparing the prediction results and actual data of the model, it is found that the prediction error of the model designed in this article is less than 1%, and the high-precision prediction of agricultural data is realized, which provides an effective guidance for the improvement of agricultural product quality and yield.

Suggested Citation

  • Chunling Zhang & Zunfeng Liu, 2019. "Application of big data technology in agricultural Internet of Things," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:10:p:1550147719881610
    DOI: 10.1177/1550147719881610
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    References listed on IDEAS

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    1. Shin, Dong-Hee, 2016. "Demystifying big data: Anatomy of big data developmental process," Telecommunications Policy, Elsevier, vol. 40(9), pages 837-854.
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    Cited by:

    1. Xue-Bo Jin & Wei-Zhen Zheng & Jian-Lei Kong & Xiao-Yi Wang & Min Zuo & Qing-Chuan Zhang & Seng Lin, 2021. "Deep-Learning Temporal Predictor via Bidirectional Self-Attentive Encoder–Decoder Framework for IOT-Based Environmental Sensing in Intelligent Greenhouse," Agriculture, MDPI, vol. 11(8), pages 1-25, August.
    2. Nala Alahmari & Rashid Mehmood & Ahmed Alzahrani & Tan Yigitcanlar & Juan M. Corchado, 2023. "Autonomous and Sustainable Service Economies: Data-Driven Optimization of Design and Operations through Discovery of Multi-Perspective Parameters," Sustainability, MDPI, vol. 15(22), pages 1-44, November.
    3. Shiya Gao & Zeyu Wang & Shaoxiang Jiang & Wen Ding & Yuchen Wang & Xiufang Dong, 2022. "Optimization of Work Environment and Community Labor Health Based on Digital Model—Empirical Evidence from Developing Countries," IJERPH, MDPI, vol. 19(20), pages 1-17, October.

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