Application of Multi-sensor Data Fusion in Greenhouse

Li Guangzhong

doi:doi:10.11648/j.iotcc.20210901.12

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Application of Multi-sensor Data Fusion in Greenhouse

Li Guangzhong

Published in Internet of Things and Cloud Computing (Volume 9, Issue 1)

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.
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[16]	Awang A, Agarwal S. Data aggregation using dynamic selection of aggregation points based on RSSI for wireless sensor networks [J]. Wireless Personal Communications, 2015, 80 (2): 611-633.
[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

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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  -

Copy | Download

Author Information

Li Guangzhong

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

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Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download