Advertisement

The Role of Geospatial Technology with IoT for Precision Agriculture

  • V. BhanumathiEmail author
  • K. Kalaivanan
Chapter
Part of the Studies in Big Data book series (SBD, volume 49)

Abstract

Precision agriculture is mainly used to make the farming as user-friendly to achieve the desired production of a crop. With the latest Geospatial technologies, the analysis related to any type of application using the Internet of Things (IoT) made each and everyone, to materialize the things whatever is imagined. The geographic information collected from various sources and with this, IoT establishes a communication to the entire world through an Internet. The information will be helpful in the maintenance of the farmland by applying the required amount of fertilizer at the right time in the right place. It is expected that in the future, this type of smart agriculture with the application of information and communication technologies including IoT will definitely bring a revolution in the global agricultural scenario to make it more resource-efficient and productive. The main goal in combining the Geospatial technology with IoT for precision is to monitor and predict the critical parameters such as water quality, soil condition, ambient temperature and moisture, irrigation, and fertilizer for improving the crop production. It can be expected that with the help of Geospatial and IoT in smart farming, the prediction of the amount of fertilizer, weeds, and irrigation will be accurate and it helps the farmers in making decisions related to all the requirements in terms of control and supply.

Keywords

Wireless sensor networks Precision agriculture Internet of Things Smart farming Crop production 

References

  1. 1.
    Barik, R.K., Dubey, H., Misra, C., Borthakur, D., Constant, N., Sasane, S. A., Mankodiya, K.: Fog assisted cloud computing in era of Big Data and Internet-of-Things: systems, architectures, and applications. In: Cloud Computing for Optimization: Foundations, Applications, and Challenges, pp. 367–394. Springer, Cham (2018)Google Scholar
  2. 2.
    Thorp, K.R., Hunsaker, D.J., French, A.N., Bautista, E., Bronson, K.F.: Integrating geospatial data and cropping system simulation within a geographic information system to analyze spatial seed cotton yield, water use, and irrigation requirements. Precis. Agric. 16(5), 532–557 (2015)CrossRefGoogle Scholar
  3. 3.
    Tzounis, A., Katsoulas, N., Bartzanas, T., Kittas, C.: Internet of Things in agriculture, recent advances and future challenges. Biosyst. Eng. 164, 31–48 (2017)CrossRefGoogle Scholar
  4. 4.
    Coates, R.W., Delwiche, M.J., Broad, A., Holler, M.: Wireless sensor network with irrigation valve control. Comput. Electron. Agric. 96, 13–22 (2013)CrossRefGoogle Scholar
  5. 5.
    Faial, B.S., Costa, F.G., Pessin, G., Ueyama, J., Freitas, H., Colombo, A., Fini, P.H., Villas, L., Osorio, F.S., Vargas, P.A., Braun, T.: The use of unmanned aerial vehicles and wireless sensor networks for spraying pesticides. J. Syst. Archit. 60, 393–404 (2014)CrossRefGoogle Scholar
  6. 6.
    Alahi, M.E.E., Nag, A., Mukhopadhyay, S.C., Burkitt, L.: A temperature-compensated graphene sensor for nitrate monitoring in real-time application. Sens. Actuators A Phys. 269, 79–90 (2018)CrossRefGoogle Scholar
  7. 7.
    Martnez, J.L., Claraco, J.L.B., Alonso, J.P., Ferre, A.J.C.: Distributed network for measuring climatic parameters in heterogeneous environments: application in a greenhouse. Comput. Electron. Agric. 145, 105–121 (2018)CrossRefGoogle Scholar
  8. 8.
    Pajares, G.: Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogramm. Eng. Remote Sens. 81, 281–329 (2015)Google Scholar
  9. 9.
    Polo, J., Hornero, G., Duijneveld, C., Garcia, A., Casas, O.: Design of a low-cost wireless sensor network with UAV mobile node for agricultural applications. Comput. Electron. Agric. 119, 19–32 (2015)CrossRefGoogle Scholar
  10. 10.
    Rawat, P., Singh, K.D., Chaouchi, H., Bonnin, J.M.: Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput. 68, 1–48 (2014)CrossRefGoogle Scholar
  11. 11.
    Sanchez, A.J.G., Sanchez, F.G., Haro, J.G.: Wireless sensor network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops. Comput. Electron. Agric. 75, 288–303 (2011)CrossRefGoogle Scholar
  12. 12.
    Afzal, B., Umair, M., Shah, G.A., Ahmed, E.: Enabling IoT platforms for social IoT applications: vision, feature mapping, and challenges. Future Gener. Comput. Syst. Available online 13 Dec 2017Google Scholar
  13. 13.
    Chen, M., Mao, S., Liu, Y.: Big Data: a survey. Mob. Netw. Appl. 19, 171–209 (2014)CrossRefGoogle Scholar
  14. 14.
    DeRen, L., JianJun, C., Yuan, Y.: Big data in smart cities. Sci. China Inf. Sci. 58 (2015)Google Scholar
  15. 15.
    Aazam, M., Zeadally, S., Harras, K.A.: Offloading in fog computing for IoT: review, enabling technologies, and research opportunities. Future Gener. Comput. Syst. 87, 278–289 (2018)CrossRefGoogle Scholar
  16. 16.
    Panigrahi, C.R., Sarkar, J.L., Pati, B., Das, H.: S2S: a novel approach for source to sink node communication in wireless sensor networks. In: International Conference on Mining Intelligence and Knowledge Exploration, pp. 406–414. Springer, Cham (2015)Google Scholar
  17. 17.
    Bhanumathi, V., Kalaivanan, K.: Application specific sensor-cloud: architectural model. In: Mishra, B., Dehuri, S., Panigrahi, B., Nayak, A., Mishra, B., Das, H. (eds.) Computational Intelligence in Sensor Networks. Studies in Computational Intelligence, vol. 776, pp. 277–305. Springer, Berlin, Heidelberg (2019)Google Scholar
  18. 18.
    Barkunan, S.R., Bhanumathi, V.: An efficient deployment of sensor nodes in wireless sensor networks for agricultural field. J. Inf. Sci. Eng. 34(4), 903–918 (2018)Google Scholar
  19. 19.
    Mulla, D.J.: Twenty five years of remote sensing in precision agriculture: key advances and remaining knowledge gaps. Biosyst. Eng. 114, 358–371 (2013)CrossRefGoogle Scholar
  20. 20.
    Bhardwaj, A., Sam, L., Bhardwaj, A., Torres, F.J.M.: LiDAR remote sensing of the cryosphere: present applications and future prospects. Remote Sens. Environ. 177, 125–143 (2016)CrossRefGoogle Scholar
  21. 21.
    Asher, J.B., Yosef, B.B., Volinsky, R.: Ground-based remote sensing system for irrigation scheduling. Biosyst. Eng. 114, 444–453 (2013)CrossRefGoogle Scholar
  22. 22.
    Kumar, S., Moore, K.B.: The evolution of global positioning system (GPS) technology. J. Sci. Educ. Technol. 11(1) (2002)Google Scholar
  23. 23.
    Barik, R.K., Lenka, R.K., Dubey, H., Mankodiya, K.: TCloud: cloud SDI model for tourism information infrastructure management. In: Chaudhuri, S., Ray, N. (eds.) GIS Applications in the Tourism and Hospitality Industry, pp. 116–144. IGI Global, Hershey PA, USA (2018)Google Scholar
  24. 24.
    Boyd, D.S., Foody, G.M.: An overview of recent remote sensing and GIS based research in ecological informatics. Ecolog. Inform. 6, 25–36 (2011)CrossRefGoogle Scholar
  25. 25.
    Ammar, M., Russello, G., Crispo, B.: Internet of Things: a survey on the security of IoT frameworks. J. Inf. Secur. Appl. 38, 8–27 (2018)Google Scholar
  26. 26.
    Sahani, R., Rout, C., Badajena, J.C., Jena, A.K., Das, H.: Classification of intrusion detection using data mining techniques. In: Progress in Computing, Analytics and Networking, pp. 753–764. Springer, Singapore (2018)Google Scholar
  27. 27.
    Pradhan, C., Das, H., Naik, B., Dey, N.: Handbook of Research on Information Security in Biomedical Signal Processing, pp. 1–414. IGI Global, Hershey, PA (2018)Google Scholar
  28. 28.
    Sarkar, J.L., Panigrahi, C.R., Pati, B., Das, H.: A novel approach for real-time data management in wireless sensor networks. In: Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, pp. 599–607. Springer, New Delhi (2016)Google Scholar
  29. 29.
    Hammoudi, S., Aliouat, Z., Harous, S.: Challenges and research directions for Internet of Things. Telecommun. Syst. 67(2), 367–385 (2018)CrossRefGoogle Scholar
  30. 30.
    Kalaivanan, K., Bhanumathi, V.: Reliable location aware and cluster-tap root based data collection protocol for large scale wireless sensor networks. J. Netw. Comput. Appl. 118, 83–101 (2018)CrossRefGoogle Scholar
  31. 31.
    Akkas, M.A., Sokullu, R.: An IoT-based greenhouse monitoring system with Micaz motes. Procedia Comput. Sci. 113, 603–608 (2017)CrossRefGoogle Scholar
  32. 32.
    Nagarajan, G., Minu, R.I.: Wireless soil monitoring sensor for sprinkler irrigation automation system. Wirel. Pers. Commun. 98(2), 1835–1851 (2018)CrossRefGoogle Scholar
  33. 33.
    Martinez, J.L., Claraco, J.L.B., Alonso, J.P., Ferre, A.J.C.: Distributed network for measuring climatic parameters in heterogeneous environments: application in a greenhouse. Comput. Electron. Agric. 145, 105–121 (2018)CrossRefGoogle Scholar
  34. 34.
    Foughali, K., Fathallah, K., Frihida, A.: Using cloud IOT for disease prevention in precision agriculture. Procedia Comput. Sci. 130, 575–582 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringAnna University Regional CampusCoimbatoreIndia

Personalised recommendations