Cover
Vol. 16 No. 1 (2020)

Published: June 30, 2020

Pages: 28-38

Original Article

Internet of Things (IoT) for Smart Precision Agriculture

Abstract

The scarcity of clean water resources around the globe has generated a need for their optimum utilization. Internet of Things (IoT) solutions, based on the application-specific sensors’ data acquisition and intelligent processing, are bridging the gaps between the cyber and physical worlds. IoT based smart irrigation management systems can help in achieving optimum water- resource utilization in the precision farming landscape. This paper presents an open-source technology-based smart system to predict the irrigation requirements of a field using the sensing of ground parameters like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. The sensing nodes, involved in the ground and environmental sensing, consider soil moisture, air temperature, and relative humidity of the crop field. This mainly focused on wastage of water, which is a major concern of the modern era. It is also time-saving, allows a user to monitor environmental data for agriculture using a web browser and Email, cost-effectiveness, environmental protection, low maintenance and operating cost and efficient irrigation service. The proposed system is made up of two parts: hardware and software. The hardware consists of a Base Station Unit (BSU) and several Terminal Nodes (TNs). The software is made up of the programming of the Wi-Fi network and the system protocol. In this paper, an MQTT (Message Queue Telemetry Transportation) broker was built on the BSU and TU board.

References

  1. M. I. Hussain, Z. I. Ahmed, N. Sarma, and D. K. Saikia, “An efficient TDMA MAC protocol for multi-hop WiFi-based long distance networks,” Wireless Personal Communications, vol. 86, no. 4, pp. 1971–1994, 2016.
  2. T. Ojha, S. Misra, and N. S. Raghuwanshi, “Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges,” Computers and Electronics in Agriculture, vol. 118, pp. 66–84, Oct. 2015.
  3. Y. Kim, R. G. Evans, and W. M. Iversen, “Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 7. pp. 1379–1387, 2008.
  4. C. Wang, C. Chen, X. Zheng, and C. Chen, “An Agricultural-Cloud Based Greenhouse Monitoring System", Agricultural Water Management, vol. 5, no. June, pp. 24–27, 2013.
  5. S. P. Goyal and A. Bhise, “Zigbee Based Real -Time Monitoring System of Agricultural Environment,” International journal of engineering research and applications, vol. 4, no. 6, pp. 06–09, 2014.
  6. N. Agrawal and S. Singhal, “Smart drip irrigation system using raspberry pi and arduino,” IEEE, International Conference on Computing, Communication and Automation, Taiwan, pp. 928–932, 2015.
  7. K. K. Namala, K. K. P. A. V, A. Math, A. Kumari, and S. Kulkarni, “Smart Irrigation with Embedded System,” IEEE, International Conference on Bombay Section Symposium (IBSS), pp. 1–5, 2016.
  8. B. S. Faiçal, H. Freitas, P. H. Gomes, L. Y. Mano, G. Pessin, A. C. P. L. F. de Carvalho, B. Krishnamachari, and J. Ueyama, ``An adaptive approach for UAV-based pesticide spraying in dynamic environments,'' Computers and Electronics in Agriculture, vol. 138, pp. 210 223, Jun. 2017.
  9. C. Romero-Trigueros, P. A. Nortes, J. J. Alarcón, J. E. Hunink, M. Parra, S. Contreras, P. Droogers, and E. Nicolás, "Effects of saline reclaimed waters and defcit irrigation on Citrus physiology assessed by UAV remote sensing,'' Agricultural Water Management, vol. 183, pp. 60-69, Mar. 2017.
  10. H. Hoffmann, R. Jensen, A. Thomsen, H. Nieto, J. Rasmussen, and T. Friborg, "Crop water stress maps for an entire growing season from visible and thermal UAV imagery,'' Biogeosciences, vol. 13, no. 24, pp. 6545- 6563, 2016.
  11. S. Park, D. Ryu, S. Fuentes, H. Chung, E. Hernández- Montes, and M. O'Connell, ``Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV),'' Remote Sensing, vol. 9, no. 8, p. 828, 2017.
  12. Q. Yang and S.-J. Yoo, "Optimal UAV path planning: Sensing data acquisition over IoT sensor networks using multi-objective bio-inspired algorithms,'' IEEE Access, vol. 6, pp. 13671-13684, 2018.
  13. J. Dai, Y.Wang, C.Wang, J. Ying, and J. Zhai, "Research on hierarchical potential eld method of path planning for UAVs,'' IEEE 2nd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), China, May 2018, pp. 529-535.
  14. R. K. Kodali and K. S. Mahesh, “A low cost implementation of MQTT using ESP8266,” IEEE, 2017 2nd International Conference on Contemporary Computing and Informatics (IC3I), Japan, pp. 404–408, 2017.
  15. S. Wyche and C. Steinfeld, "Implementation of Environment Parameters Monitoring in a Manufacturing Industry using IOT,'' 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Egypt, pp. 320- 333, 2019.
  16. J. Qian, T. Yang, M. Wu, B. Xing, G. Wu, and M. Li, "Farm and environment information bidirectional acquisition system with individual tree identification using smartphones for orchard precision management,'' Computers and Electronics in Agriculture, vol. 116, pp. 101-108, Aug. 2017.
  17. A. Lavric, A. I. Petrariu, and V. Popa, "Long-range SigFox communication protocol scalability analysis under large-scale, high-density conditions,'' IEEE Access, vol. 7, pp. 35816-35825, 2018.
  18. A. Hadi, “Automatic Controlling System of Drip Irrigation Based on GSM,” Archives of Current Research International, vol. 7, no. 2, pp. 1–8, 2017.