Skip to main content
Top

2019 | OriginalPaper | Chapter

Intelligent Fusion Technology of Crop Growth Monitoring Data Under Wireless Sensor Networks

Authors : Yongheng Zhang, Xiaoyan Ai

Published in: Advanced Hybrid Information Processing

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Using adaptive weighted data fusion technology, the relative error of crop growth monitoring data is relatively large, accuracy is not high, and its fusion is not effective. In view of the above problems, the intelligent fusion technology of crop growth monitoring data under wireless sensor network is proposed. The technology consists of three parts: Using LEACH (Low Energy Adaptive Clustering Hierarchy) protocol to realize rapid processing and transmission of monitoring data. Accurate fusion of monitoring data through BP (Back Propagation) neural network; the two models are combined to construct the data fusion algorithm BPDFA (Back-Propagation Data Fusion Algorithm) model, so as to achieve intelligent fusion of crop growth monitoring data. By using the unique information processing characteristics of BP neural network, multi-information processing and transmission at the same time, the efficiency of processing is improved, and the fusion of crop growth information is realized. The results show that the intelligent fusion technology and adaptive weighted data fusion technology proposed in this study, the relative error is reduced by 3.72 °C, the accuracy is higher, and the fusion effect is better.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference Qiu, L.D., Liu, T.J., Fu, P.: Data aggregation in wireless sensor networks based on deep learning. Appl. Res. Comput. 3(1), 185–188 (2016) Qiu, L.D., Liu, T.J., Fu, P.: Data aggregation in wireless sensor networks based on deep learning. Appl. Res. Comput. 3(1), 185–188 (2016)
2.
go back to reference Li, L.: Ant colony optimized wireless sensor networks data aggregation algorithm. Microelectron. Comput. 33(6), 68–72 (2016)CrossRef Li, L.: Ant colony optimized wireless sensor networks data aggregation algorithm. Microelectron. Comput. 33(6), 68–72 (2016)CrossRef
3.
go back to reference Jin, C., Cai, G.Q., Chen, G., et al.: Design of fire alarm system based on wireless sensor networks and sensor fusion technology. Instrum. Tech. Sens. 4(6), 66–68 (2016) Jin, C., Cai, G.Q., Chen, G., et al.: Design of fire alarm system based on wireless sensor networks and sensor fusion technology. Instrum. Tech. Sens. 4(6), 66–68 (2016)
4.
go back to reference Yu, X.W., Fan, F.S., Zhou, L.X., et al.: Adaptive forecast weighting data fusion algorithm for wireless sensor network. J. Chin. Sens. Actuators 30(5), 772–776 (2017) Yu, X.W., Fan, F.S., Zhou, L.X., et al.: Adaptive forecast weighting data fusion algorithm for wireless sensor network. J. Chin. Sens. Actuators 30(5), 772–776 (2017)
5.
go back to reference Fei, H., Xiao, F., Li, G.H., et al.: An anomaly detection method of wireless sensor network based on multi-modals data stream. Chin. J. Comput. 40(8), 1829–1842 (2017) Fei, H., Xiao, F., Li, G.H., et al.: An anomaly detection method of wireless sensor network based on multi-modals data stream. Chin. J. Comput. 40(8), 1829–1842 (2017)
6.
go back to reference Chen, C.L., Cui, L., Xu, T.Y., et al.: Wireless-sensor and multi-data fusion technological research of sunlight greenhouse. J. Shenyang Agric. Univ. 47(1), 86–91 (2016) Chen, C.L., Cui, L., Xu, T.Y., et al.: Wireless-sensor and multi-data fusion technological research of sunlight greenhouse. J. Shenyang Agric. Univ. 47(1), 86–91 (2016)
7.
go back to reference Huang, T.H., Kai, K., Wang, Y.L., et al.: Firefly algorithm optimized neural network data fusion in wireless sensor network. Instrum. Tech. Sens. 5(7), 103–107 (2016) Huang, T.H., Kai, K., Wang, Y.L., et al.: Firefly algorithm optimized neural network data fusion in wireless sensor network. Instrum. Tech. Sens. 5(7), 103–107 (2016)
8.
go back to reference Xu, H.Y., Yang, Y.: Scheme of data aggregation based on data flow density for wireless sensor networks. Control Eng. Chin. 25(1), 165–169 (2018) Xu, H.Y., Yang, Y.: Scheme of data aggregation based on data flow density for wireless sensor networks. Control Eng. Chin. 25(1), 165–169 (2018)
9.
go back to reference Xiang, L.F.: Research on automatic monitoring of mobile data in sensor networks. Comput. Simul. 34(11), 447–450 (2017) Xiang, L.F.: Research on automatic monitoring of mobile data in sensor networks. Comput. Simul. 34(11), 447–450 (2017)
Metadata
Title
Intelligent Fusion Technology of Crop Growth Monitoring Data Under Wireless Sensor Networks
Authors
Yongheng Zhang
Xiaoyan Ai
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-19086-6_37

Premium Partner