Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Intelligent Information and Database Systems

ACIIDS 2019: Intelligent Information and Database Systems: Recent Developments pp 31–42Cite as

Decision Making Based on IoT Data Collection for Precision Agriculture

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 830))

Abstract

Internet of Things (IoT) is a shared network of things which can associate with each other through the internet connection. IoT plays a vital role in the agriculture industry which can feed 9.0 billion people by 2050. Precision Agriculture provides a novel solution using a systems approach for today’s agricultural issues such as the need to balance productivity with environmental concerns. This paper proposes IoT for local information data collection on Precision Agriculture and uses some case study for examples. In this paper, we aim that the internet of things can be used to collect local information data on precision agriculture. The farmer could get the real time data for monitoring his field. Moreover, by using this technology, farmers can effectively use the information to achieve higher yields and therefore earn higher profits.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Srinivasulu, P., Venkat, R., Babu, M., Rajesh, K.: Cloud Service Oriented Architecture (CSoA) for agriculture through the Internet of Things (IoT) and big data. In: 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), IEEE, Karur, India (2017)

    Google Scholar 

  2. Khattab, A., Abdelgawad, A., Yelmarthi, K.: 2016 28th International Conference on Microelectronics (ICM), IEEE, Giza, Egypt, pp. 201–204 (2016)

    Google Scholar 

  3. Amandeep, Bhattacharjee, A., Das, P., Basu, D., Roy, S., Ghosh, S., Saha, S., Pain, S., Dey, S.: 2017 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE, Vancouver, BC, Canada, pp. 278–280 (2017)

    Google Scholar 

  4. Prasad, K., Kumaresan, A., Nageshwaran, B., Kumaresan, A., Kotteshwaran, M.: IoT based smart agricultural solutions to farmer enhanced with WIFI technology. Int. J. Adv. Res. Basic Eng. Sci. Technol. (IJARBEST) 3, 131–136 (2017)

    Google Scholar 

  5. Ferrández-Pastor, F., García-Chamizo, J., Nieto-Hidalgo, M., Mora-Martínez, J.: Precision agriculture design method using a distributed computing architecture on internet of things context. Sensors (2018)

    Google Scholar 

  6. Hedi, I., Špeh, I., Šarabok, A.: IoT network protocols comparison for IoT constrained networks. In: Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, pp. 501–505 (2017)

    Google Scholar 

  7. Tech-Target: IoT as a Solution for Precision Farming (2017). Available online: http://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/IoT-as-a-solution-for-precision-farming. Accessed on 4 Oct 2018

  8. Koch, B., Khosla, R.: The role of precision agriculture in cropping systems. J. Crop Prod. 9(1–2), 361–381 (2003)

    Article  Google Scholar 

  9. Pavithra, G.: Intelligent monitoring device for agricultural greenhouse using IOT. J. Agric. Sci. Food Res. 9, 220 (2018)

    Google Scholar 

  10. Gaikwad, S.V., Galande, S.G.: Measurement of NPK, temperature, moisture, humidity using WSN. Int. J. Eng. Res. Appl. 5(8), (Part-3), 84–89 (2015)

    Google Scholar 

  11. Krishna Jha, R., Kumar, S., Joshi, K., Pandey, R.: Field monitoring using IoT in agriculture. In: 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), IEEE, Kannur, India (2017)

    Google Scholar 

  12. Chavan, C.H., Karande, P.V.: Wireless monitoring of soil moisture, temperature & humidity using Zigbee in agriculture. Int. J. Eng. Trends Technol. (IJETT) 11(10) (2014)

    Google Scholar 

  13. Rajalakshmi, P., Mahalakshmi, S.D.: IOT based crop-field monitoring and irrigation automation. In: 10th International conference on intelligent systems and control (ISCO), IEEE, Coimbatore, India (2016)

    Google Scholar 

  14. Ananthi, N., Divya, J., Divya, M., Janani, V.: IoT based smart soil monitoring system for agricultural production. In: IEEE technological innovations in ICT for agriculture and rural development (TIAR), IEEE, Chennai, India (2017)

    Google Scholar 

  15. Nooriman, W.H., Abdullah, A.H., Abdul Rahim, N., Kamarudin, K.: Development of wireless sensor network for Harumanis Mango Orchard’s temperature, humidity, and soil moisture monitoring. In: IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), IEEE, Penang, Malaysia, pp. 264–268 (2018)

    Google Scholar 

  16. Bodić, M., Vuković, P., Rajs, V., Vasiljević-Toskić, M., Bajić, J.: Station for soil humidity, temperature and air humidity measurement with SMS forwarding of measured data. In: 41st International Spring Seminar on Electronics Technology (ISSE), IEEE, Zlatibor, Serbia (2018)

    Google Scholar 

  17. Nirmal Kumar, K., Ranjith, P., Prabakaran, R.: Real time paddy crop field monitoring using Zigbee network. In: 2011 International Conference on Emerging Trends in Electrical and Computer Technology (ICETECT), Nagercoil, India, pp. 1136–1140 (2011)

    Google Scholar 

  18. Veenadhari, S., Mishra, B., Singh, C.D.: Soybean productivity modelling using decision tree algorithms. Int. J. Comput. Appl. 27(7), 11–15 (2011)

    Google Scholar 

  19. Shakoor, T., Rahman, K., Nasrin Rayta, S., Chakrabarty, A.: Agricultural production output prediction using supervised machine learning techniques. In: 2017 1st International Conference on Next Generation Computing Applications (NextComp), IEEE, Mauritius (2017)

    Google Scholar 

  20. Khan, R., Ali, I., Zakarya, M., Ahmad, M., Imran, M., Shoaib, M.: Technology-assisted decision support system for efficient water utilization: a real-time testbed for irrigation using wireless sensor networks. IEEE 6, 25686–25697 (2018)

    Google Scholar 

  21. Viani, F., Bertolli, M., Salucci, M., Polo, A.: Low-cost wireless monitoring and decision support for water saving in agriculture. IEEE Sens. J. 17(13), 4299–4309 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by Ministry of Science and Technology, Taiwan. The Nos are MOST-107-2221-E-324-018-MY2 and MOST-106-2218-E-324-002, Taiwan.

Author information

Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan, Republic of China

    Christine Dewi & Rung-Ching Chen

Authors
  1. Christine Dewi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rung-Ching Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rung-Ching Chen .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Maciej Huk

  2. Department of Information Systems, Wrocław University of Science and Technology, Wrocław, Poland

    Marcin Maleszka

  3. Department of Management, Gdańsk University of Technology, Gdańsk, Poland

    Edward Szczerbicki

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Dewi, C., Chen, RC. (2020). Decision Making Based on IoT Data Collection for Precision Agriculture. In: Huk, M., Maleszka, M., Szczerbicki, E. (eds) Intelligent Information and Database Systems: Recent Developments. ACIIDS 2019. Studies in Computational Intelligence, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-030-14132-5_3

Download citation

Publish with us

Policies and ethics

Search

Navigation