| Peer-Reviewed

Application of Multi-sensor Data Fusion in Greenhouse

Received: 7 April 2021     Accepted: 19 April 2021     Published: 23 April 2021
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Abstract

With the development of communication, computer and sensor technology, the application of Internet of things technology to agricultural monitoring is the trend of modern agricultural development. Real time and accurate acquisition of farmland environmental information is the basis of precision operation and intelligent management of agriculture, and it is also an important part of agricultural information construction. A farmland environment information monitoring system based on wireless sensor network is designed, crop growth environment parameters are collected by sensor nodes distributed in the field, using CC2530 to build ZigBee data transmission network, the information transmission between ZigBee network, GPRS network is realized by embedded gateway, and the remote monitoring of farmland environmental information is realized. Before data is transmitted, The negligent errors in the measurement data are excluded by Grubbs’ criterion, then the rest of the data are preprocessed based on the arithmetic mean and the batch estimates, lastly the data are fused using adaptive weighted fusion algo-rithm in the condition of minimal mean square error. The results show that the data by hybrid algorithm has perfect accuracy and minimal error. Using this hybrid data processing method, a large number of data can be fused into a data closest to the real situation, and more accurate environmental information can be obtained. The practical results show that, this solution enhances accuracy and reliability of the greenhouse environment detection. This system improves the information level of greenhouse planting, and applys to the management of greenhouse.

Published in Internet of Things and Cloud Computing (Volume 9, Issue 1)
DOI 10.11648/j.iotcc.20210901.12
Page(s) 10-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

ZigBee, Grubbs’ Criterion, Self-adaptive Weighting Fusion Algorithm, Error

References
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[3] Kong Guoli et al. Automatic Intelligent Management System of the Temperature and Humidity and Soil Moisture for the Vegetable Greenhouse [J]. Journal of Agricultural Mechanization Research, 2015, 37 (08): 184-188.
[4] Mao Min, Study on Greenhouse Monitoring Systems Based on Internet of Things Technology [J]. Electrical Automation, 2021, 43 (01): 34-36.
[5] Yang Jingwei et al. A Design of Dedicated Carbon Dioxide Monitor System in Facility Agriculture [J]. Journal of Agricultural Mechanization Research, 2014, 36 (06): 127-130+137.
[6] Wang maoli, et al. Research on digital farmland information monitoring system based on Internet of Things technology [J]. Journal of Chinese Agricultural Mechanization, 2019, 40 (9): 158~163, 180.
[7] ZHANG Wei-Cong, et al. Wireless Network Sensor Node Design Based on CC2530 and ZigBee Protocol Stack [J]. Computer Systems & Applications, 2011, 20 (07): 184-187+120.
[8] Liu Yuanyuan, et al. The System Design of Farmland Environmental Monitoring Based on GPRS and Wireless Sensor Network [J]. Journal of Agricultural Mechanization Research, 2013, 35 (7): 229-232.
[9] DUAN Jie, et al. Analysis of strawberry greenhouse information monitoring system based onagricultural internet of things [J]. Modern agricultural science and technology, 2018, 17 (18): 1288-1291.
[10] XIAO Linglu, et al. Design of Farmland Environment Monitoring System Based on Wireless Sensor Network [J]. Henan Science, 2017, 35 (10): 1574-1581.
[11] HUANG Haisong, et al. Study on datafusion of crop growth monitoring based on agricultural internet of things [J]. Jiangsu agricultural science, 2017, 20 (21): 1241-1243.
[12] FAN LeiSong, et al, Data fusion method based on BP neural network in wireless sensor networks [J]. Computer Engineering And Design, 2014, 35 (1): 62-66.
[13] TANG Ya-peng. Data Processing Based on Adaptive Weighted Data Fusion Algorithm [J]. Computer Technology And Development, 2015, 25 (04): 53-56.
[14] HUANG Jian, et al. Application of Multi-sensor Data Fusion in Underground Mines [J]. Coal Mine Machinery, 2015, 36 (7): 242~244.
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[17] Rout R R, Ghosh S K. Adaptive data aggregation and energy efficiency using network coding in a clustered wireless sensor network: an analytical approach [J]. Computer Communications, 2014, 40 (3): 65-75.
Cite This Article
  • APA Style

    Li Guangzhong. (2021). Application of Multi-sensor Data Fusion in Greenhouse. Internet of Things and Cloud Computing, 9(1), 10-15. https://doi.org/10.11648/j.iotcc.20210901.12

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    ACS Style

    Li Guangzhong. Application of Multi-sensor Data Fusion in Greenhouse. Internet Things Cloud Comput. 2021, 9(1), 10-15. doi: 10.11648/j.iotcc.20210901.12

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    AMA Style

    Li Guangzhong. Application of Multi-sensor Data Fusion in Greenhouse. Internet Things Cloud Comput. 2021;9(1):10-15. doi: 10.11648/j.iotcc.20210901.12

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  • @article{10.11648/j.iotcc.20210901.12,
      author = {Li Guangzhong},
      title = {Application of Multi-sensor Data Fusion in Greenhouse},
      journal = {Internet of Things and Cloud Computing},
      volume = {9},
      number = {1},
      pages = {10-15},
      doi = {10.11648/j.iotcc.20210901.12},
      url = {https://doi.org/10.11648/j.iotcc.20210901.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20210901.12},
      abstract = {With the development of communication, computer and sensor technology, the application of Internet of things technology to agricultural monitoring is the trend of modern agricultural development. Real time and accurate acquisition of farmland environmental information is the basis of precision operation and intelligent management of agriculture, and it is also an important part of agricultural information construction. A farmland environment information monitoring system based on wireless sensor network is designed, crop growth environment parameters are collected by sensor nodes distributed in the field, using CC2530 to build ZigBee data transmission network, the information transmission between ZigBee network, GPRS network is realized by embedded gateway, and the remote monitoring of farmland environmental information is realized. Before data is transmitted, The negligent errors in the measurement data are excluded by Grubbs’ criterion, then the rest of the data are preprocessed based on the arithmetic mean and the batch estimates, lastly the data are fused using adaptive weighted fusion algo-rithm in the condition of minimal mean square error. The results show that the data by hybrid algorithm has perfect accuracy and minimal error. Using this hybrid data processing method, a large number of data can be fused into a data closest to the real situation, and more accurate environmental information can be obtained. The practical results show that, this solution enhances accuracy and reliability of the greenhouse environment detection. This system improves the information level of greenhouse planting, and applys to the management of greenhouse.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Application of Multi-sensor Data Fusion in Greenhouse
    AU  - Li Guangzhong
    Y1  - 2021/04/23
    PY  - 2021
    N1  - https://doi.org/10.11648/j.iotcc.20210901.12
    DO  - 10.11648/j.iotcc.20210901.12
    T2  - Internet of Things and Cloud Computing
    JF  - Internet of Things and Cloud Computing
    JO  - Internet of Things and Cloud Computing
    SP  - 10
    EP  - 15
    PB  - Science Publishing Group
    SN  - 2376-7731
    UR  - https://doi.org/10.11648/j.iotcc.20210901.12
    AB  - With the development of communication, computer and sensor technology, the application of Internet of things technology to agricultural monitoring is the trend of modern agricultural development. Real time and accurate acquisition of farmland environmental information is the basis of precision operation and intelligent management of agriculture, and it is also an important part of agricultural information construction. A farmland environment information monitoring system based on wireless sensor network is designed, crop growth environment parameters are collected by sensor nodes distributed in the field, using CC2530 to build ZigBee data transmission network, the information transmission between ZigBee network, GPRS network is realized by embedded gateway, and the remote monitoring of farmland environmental information is realized. Before data is transmitted, The negligent errors in the measurement data are excluded by Grubbs’ criterion, then the rest of the data are preprocessed based on the arithmetic mean and the batch estimates, lastly the data are fused using adaptive weighted fusion algo-rithm in the condition of minimal mean square error. The results show that the data by hybrid algorithm has perfect accuracy and minimal error. Using this hybrid data processing method, a large number of data can be fused into a data closest to the real situation, and more accurate environmental information can be obtained. The practical results show that, this solution enhances accuracy and reliability of the greenhouse environment detection. This system improves the information level of greenhouse planting, and applys to the management of greenhouse.
    VL  - 9
    IS  - 1
    ER  - 

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Author Information
  • Department of Computer Science and Technology, Shandong Agricultural University, Taian City, China

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