Smart Agriculture with Advanced IoT Communication and Sensing Unit

  • David KrcmarikEmail author
  • Reza Moezzi
  • Michal Petru
  • Jan Koci
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


In this practical project, an advanced reconfigurable communication unit is studied which is well-matched to be used in harsh agriculture environment. The unit transfers all data through SQL database and back-end with a UF (user friendly) web interface. It collects many data online from different sources like accelerometer, tensometers, CAN, temp. sensors and analog or digital inputs. The communication is based on Global System for Mobile Communications (GSM) and It uses GPS data to obtain accurate real-time localization for knowing where the machine is. The unit is powered with three different power sources. An external motor generator, a solar battery or a standard 12 V tractor battery. To enable the device working even without any of the above-mentioned power sources, an internal battery is also provided as a secondary source. The heart and novelty of the unit is a multifunctional Linux based Printed Circuit Boards (PCB) which would be described in detail. Several simultaneously working units, can be simply addressed using private standard network. The back-end lets the operators to organize chosen activities. One can select desired data to be detected. It is conceivable to arrange the time span between two different packages and the data acquisition rate which would be sent to back-end. Information are accessible via web interface and can be copied in CSV format for post processing. The whole system is also designed to capture user-defined data constraints and if such a constraint is touched, a warning message would be sent to a pre-defined contact.


Internet of Things (IoT) Smart and precision agriculture Sensors GSM Big data 



The authors would like to thank Ministry of Education, Youth and Sports in Czechia and the European Union for financing this research study in the framework of the project “Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation”, Reg. No. CZ.02.1.01/0.0/0.0/16_025/0007293.


  1. 1.
    Jawad, H.M., Nordin, R., Gharghan, S.K., et al.: Energy-efficient wireless sensor networks for precision agriculture: a review. Sensors 17(8), 1781 (2017)CrossRefGoogle Scholar
  2. 2.
    Bhagwat, P., Raman, B., Sanghi, D.: Turning 802.11 inside-out. ACM SIGCOMM Comput. Commun. Rev. 34(1), 33–38 (2004)CrossRefGoogle Scholar
  3. 3.
    Chebrolu, K., Raman, B.: FRACTEL: a fresh perspective on (rural) mesh networks. In: Workshop on Networked systems for developing regions, pp. 8:1–8:6. ACM (2007)Google Scholar
  4. 4.
    Hussain, M.I., Ahmed, Z.I., Sarma, N., Saikia, D.: An efficient TDMA MAC protocol for multi-hop wifi-based long distance networks. Wirel. Pers. Commun. 86(4), 1971–1994 (2016)CrossRefGoogle Scholar
  5. 5.
    Jayaraman, P.P., Yavari, A., Georgakopoulos, D., et al.: Internet of things platform for smart farming: experiences and lessons learnt. Sensors 16(11), 1884 (2016)CrossRefGoogle Scholar
  6. 6.
    Talavera, J.M., Tobon, L.E., Gomez, J.A., et al.: Review of IoT applications in agro-industrial and environmental fields. Comput. Electron. Agric. 142, 283–297 (2017)CrossRefGoogle Scholar
  7. 7.
    Tzounis, A., Katsoulas, N., Bartzanas, T., et al.: Internet of things in agriculture, recent advances and future challenges. Biosys. Eng. 164, 31–48 (2017)CrossRefGoogle Scholar
  8. 8.
    Popovic, T., Latinovic, N., Pesic, A., et al.: Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: a case study. Comput. Electron. Agric. 140, 255–265 (2017)CrossRefGoogle Scholar
  9. 9.
    Ray, P.P.: Internet of things for smart agriculture: technologies, practices and future direction. J. Ambient Intell. Smart Environ. 9, 395–420 (2017)CrossRefGoogle Scholar
  10. 10.
    Elijah, O., Rahman, T.A., Orikumhi, I., et al.: An overview of internet of things (IoT) and data analytics in agriculture: benefits and challenges. IEEE Internet Things J. 5(5), 3758–3773 (2018)CrossRefGoogle Scholar
  11. 11.
    Rahimian Koloor, S.S., Karimzadeh, A., Yidris, N., Petrů, M., Ayatollahi, M.R., Tamin, M.N.: An energy-based concept for yielding of multidirectional FRP composite structures using a mesoscale lamina damage model. Polymers 12(1), 157 (2020)CrossRefGoogle Scholar
  12. 12.
    Cyrus, J., Krcmarik, D., Moezzi, R., Koci, J., Petru, M.: Hololens used for precise position tracking of the third party devices - autonomous vehicles. Commun. - Sci. Lett. Univ. Zilina 21(2), 18–23 (2019)Google Scholar
  13. 13.
    Minh, V.T., Moezzi, R., Owe, I.: Fuel economy regression analyses for hybrid electric vehicle. Eur. J. Electr. Eng. 20(3), 363–377 (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • David Krcmarik
    • 1
    Email author
  • Reza Moezzi
    • 1
    • 2
  • Michal Petru
    • 1
  • Jan Koci
    • 1
  1. 1.Institute for Nanomaterials, Advanced Technologies and InnovationTechnical University of LiberecLiberecCzech Republic
  2. 2.Faculty of Mechatronics, Informatics and Interdisciplinary StudiesTechnical University of LiberecLiberecCzech Republic

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