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Automated Field Monitoring by a Group of Light Aircraft-Type UAVs

  • Ekaterina Pantelej
  • Nikolay Gusev
  • George Voshchuk
  • Alexander Zhelonkin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)

Abstract

This paper provides an overview of some existing methods of Earth remote sensing (ERS) using for agricultural needs. Special emphasis is placed on sensing with the help of UAVs. The paper describes the developed software and hardware complex for an aircraft-type UAV group. The proposed solution significantly increases the operating time and automates the process of monitoring agricultural areas. In addition, legislative restrictions on the use of UAVs are considered.

Keywords

Agriculture monitoring Precision agriculture Unmanned aerial vehicle (UAV) Internet of Things (IoT) Image analysis 

Notes

Acknowledgements

The work was supported by the Ministry of Education and Science of the Russian Federation within the contract agreement #14.574.21.0183 - unique identification number RFMEFI57417X0183.

References

  1. 1.
    Collection: Agro-industrial complex of Russia in 2016. Rosinformagrotech, p. 704 (2017)Google Scholar
  2. 2.
    Uwizera, D., McSharry, P.: Forecasting and monitoring maize production using satellite imagery in Rwanda. In: Technological Innovations in ICT for Agriculture and Rural Development (TIAR), pp. 51–56. IEEE (2017)Google Scholar
  3. 3.
    Yang, C., Everitt, J.H., Du, Q., Luo, B., Chanussot, J.: Using high-resolution airborne and satellite imagery to assess crop growth and yield variability for precision agriculture. In: Proceedings of the IEEE, vol. 101, No. 3, pp. 582–592 (2013)CrossRefGoogle Scholar
  4. 4.
    Li, W., Yuan, H., Li, W., Song, L.: Prediction of wheat gains with imagery from four-rotor UAV. In: 2nd IEEE International Conference on Computer and Communications, pp. 662–665 (2016)Google Scholar
  5. 5.
    Perez-Ortiz, M., Gutierrez, P.A., Pena, J.M., Torres-Sanchez, J., Lopez-Granados, F., Hervas-Martınez, C.: Machine learning paradigms for weed mapping via unmanned aerial vehicles. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (2016)Google Scholar
  6. 6.
    Vehicl, J.N., Prado, J., Lino, M.: Low-cost multi-spectral vegetation classification using an unmanned aerial. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), pp. 336–342 (2017)Google Scholar
  7. 7.
    Hwang, J., Shin, C., Yoe, H.: Study on an agricultural environment monitoring server system using wireless sensor networks. Sensor 10, 11189–11211 (2010)CrossRefGoogle Scholar
  8. 8.
    Mekala, M.S., Viswanathan, P.: A survey smart agriculture IoT with cloud computing. In: 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS) (2017)Google Scholar
  9. 9.
    Yonghong, T., Bing, Z., Zeyu, L.: Agricultural greenhouse environment monitoring system based on internet of things. In: 3rd IEEE International Conference on Computer and Communications, pp. 2981–2985 (2017)Google Scholar
  10. 10.
    Skobelev, P., Budaev, D., Brankovsky, A., Voshuk, G.: Multi-agent tasks scheduling for coordinated actions of unmanned aerial vehicles acting in group. Int. J. Des. Nat. Ecodynamics 13(1), 39–45 (2018)CrossRefGoogle Scholar
  11. 11.
    The Air Code of the Russian Federation. http://docs.cntd.ru/document/9040995. Accessed 5 April 2018

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ekaterina Pantelej
    • 1
  • Nikolay Gusev
    • 2
  • George Voshchuk
    • 3
  • Alexander Zhelonkin
    • 3
  1. 1.Network-Centric Platforms, Ltd.SamaraRussian Federation
  2. 2.Samara State Technical UniversitySamaraRussian Federation
  3. 3.Aeropatrol, Ltd.SamaraRussian Federation

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