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An IoT-Enabled Multi-Sensor System with Location Detection for Agricultural Applications

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Abstract

Real-time environmental data acquisition and monitoring is a significant aspect of IoT-enabled farming to overcome the constraints in present day’s farming that includes regular monitoring of agricultural fields and adjacent weather-related information. Real-time monitoring can be achieved by measuring various parameters such as humidity, pressure, temperature and location data using sensors. The humidity, pressure and temperature data help in environmental monitoring of the farming zone, and the latitude and longitude data enable specific location-based farming. The measured parameters are to be communicated to the primary users efficiently in real time. This work showcases the concept of IoT-enabled farming in line with agriculture 4.0 where a hardware module consisting of a Raspberry Pi, SenseHat and low-cost, compact GPS receiver is implemented for agricultural applications. This idea would be useful for cost-effective IoT research, application development and for data recording in harsh and constrained environmental conditions with advantages of compact size and low power consumptions. The module design has a dimension of 20 × 11cm2 and has a temperature accuracy of ± 2 °C, humidity in the 20–80% RH range with an accuracy ± 4.5%, pressure sensor with 260–1260 hPa absolute range with ± 0.1 hPa under normal conditions, and the GPS sensor has an accuracy of 2.5 m. The proposed system is made Wi-Fi enabled to acquire data in the server for the primary users.

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References

  1. S. Mahato, A. Santra, S. Dan, P. Verma, P. Banerjee and A. Bose, Visibility Anomaly of GNSS Satellite and Support from Regional System. Current Science, 119 (2020) 1774–1782.

    Article  Google Scholar 

  2. M.A. Oliver, Precision Agriculture and Geostatistics: How to Manage Agriculture More Exactly. Significance, 10 (2013) 17–22.

    Article  Google Scholar 

  3. H. P. W. Jayasuriya, S. Zekri, R. Zaier, & H. Al-Busaidi, Evaluation of a Sensor-Based Precision Irrigation System for Efficiency and to Monitor and Control Groundwater Over-Pumping in Oman, 12th International Conference on Precision Agriculture, Sacramento, California (USA) (2014) 21–23

  4. N. Tremblay, P. Vigneault, C. Bélec, E. Fallon, & M. Y. Bouroubi, A Comparison of Performance Between UAV and Satellite Imagery for N Status Assessment in Corn, Proceedings of 12th International Conference on Precision Agriculture, Sacramento, California, USA (2014) 19.

  5. W.S. Kim, W.S. Lee and Y.J. Kim, A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation. J. Biosyst. Eng., 45 (2020) 385–400.

    Article  Google Scholar 

  6. S. G. Nikhade, Wireless Sensor Network System Using Raspberry pi and Zigbee for Environmental Monitoring Applications, International Conference on smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, (2015) 376–381.

  7. How Internet of Things Is Transforming the Food Industry. Available: https://www.comparethecloud.net/articles/how-internet-of-things-transforming-food-industry/, accessed June (2021).

  8. J.O. Payero, A. Mirzakhani-Nafchi, A. Khalilian, X. Qiao and R. Davis, Development of a Low-Cost Internet-of-Things (IoT) System for Monitoring Soil Water Potential Using Watermark 200SS Sensors. Adv. Internet Things, 7 (2017) 71–86.

    Article  Google Scholar 

  9. M.S. Liao, S.F. Chen, C.Y. Chou, H.Y. Chen, S.H. Yeh, Y.C. Chang and J.A. Jiang, On Precisely Relating the Growth of Phalaenopsis Leaves to Greenhouse Environmental Factors by Using an IoT-Based Monitoring System. Comput. Electron. Agric., 136 (2017) 125–139.

    Article  Google Scholar 

  10. S. Tabassum and A. Hossain, Design and Development of Weather Monitoring and Controlling System for a Smart Agro (Farm). Intell. Control Autom., 9 (2018) 65.

    Article  Google Scholar 

  11. A. Bharadwaj, A. Sudhir, H. Shekhar, N. Khandelwal and I. Kishor, Raspberry Pi Based Weather Monitoring System. Int. J. Res. Eng., Sci. Manag., 4 (2021) 114–117.

    Google Scholar 

  12. R. Ikhankar, V. Kuthe, S. Ulabhaje, S. Balpande, and M. Dhadwe, Pibot: The Raspberry pi Controlled Multi-Environment Robot for Surveillance & Live Streaming, 2015 International Conference on Industrial Instrumentation and Control (ICIC), 1402–1405 (2015).

  13. J. Lowenberg-DeBoer and B. Erickson, “Setting the record straight on precision agriculture adoption. Agron. J., 111 (2019) 1552–1569.

    Article  Google Scholar 

  14. N. Thereza, I. P. A. Saputra, and A. Hamdadi, The Design of Monitoring System of Smart Farming Based on IoT Technology to Support Operational Management of Tea Plantation, Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019), (2020) 52–57.

  15. J. Das V, S. Sharma and A. Kaushik, Views of Irish farmers on smart farming technologies: an observational study. Agri Eng., 1 (2019) 164–187.

    Google Scholar 

  16. N.G. Rezk, E.E.D. Hemdan, A.F. Attia, A. El-Sayed and M.A. El-Rashidy, An Efficient IoT Based Smart Farming System Using Machine Learning Algorithms. Multimed. Tools Appl., 80 (2021) 773–797.

    Article  Google Scholar 

  17. Open source HTML5 Charts for your website, https://www.chartjs.org/ – Chart.js, accessed on 13 May (2021).

  18. D. Abbott, Linux for embedded and real-time applications, Elsevier, (2011).

  19. About SenseHat, https://www.raspberrypi.org/products/sense-hat/, accessed on 21 June (2021).

  20. Raspberry Pi Sense HAT Fact Sheet, https://www.farnell.com/datasheets/1958037.pdf, accessed on 11 December (2022).

  21. GPS Sensor specification, https://acoptex.com/project/258/basics-project-053b-neo-6m-gy-gps6mv2-gps-module-at-lex-c/, accessed on 20 November (2022).

  22. S. Mahato, P. Rakshit, A. Santra, S. Dan, N.C. Tiglao and A. Bose, A GNSS-Enabled Multi-Sensor for Agricultural Applications. J. Inform. Optim. Sci., 40 (2019) 1763–1772.

    Google Scholar 

  23. S. Mahato, G. Shaw, A. Santra, S. Dan, S. Kundu and A. Bose, Low Cost GNSS Receiver RTK Performance in Forest Environment, 2020 URSI Regional Conference on Radio Science (URSI-RCRS), Varanasi, 1–4 (2020)..

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Acknowledgements

The authors acknowledge Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Govt of India (Project Code: SERB-CRG/2020/005098), and National Institute of Technology, Sikkim, for financial support to carry out the research work.

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Correspondence to Atanu Santra.

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Kar, G.N., Verma, P., Mahato, S. et al. An IoT-Enabled Multi-Sensor System with Location Detection for Agricultural Applications. MAPAN 38, 375–382 (2023). https://doi.org/10.1007/s12647-022-00617-7

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