IOT Based Smart Irrigation and Soil Monitoring System for Precision Farming

Abstract

Efficient water management is critical due to increasing global water scarcity. This paper proposes an IoT-based smart irrigation and soil monitoring system that automates irrigation using real-time sensing and cloud connectivity. Soil moisture, soil temperature, air temperature, humidity, and UV radiation are measured, while weather forecast data supports predictive irrigation decisions. Sensor data is transmitted to a cloud platform, enabling remote monitoring and pump control through a mobile or web interface. The system reduces water usage and energy consumption by irrigating only when required, improving crop productivity and minimizing manual intervention. The integration of IoT and predictive analytics provides a scalable and sustainable solution for precision farming.

Introduction

Water scarcity is a growing global problem, intensified by population growth and inefficient agricultural irrigation, which consumes about 70% of global freshwater. Countries like India face severe pressure due to high agricultural water use despite limited freshwater resources. Conventional irrigation methods often waste water, degrade soil, and reduce crop yields. IoT-based precision farming offers an effective solution by enabling real-time monitoring and automated irrigation based on soil moisture and environmental conditions, leading to improved water efficiency, crop health, and reduced labor. Successful implementations in regions such as Andhra Pradesh and Israel demonstrate substantial water savings and yield improvements.

The literature review highlights the evolution of IoT-based smart irrigation systems using wireless sensor networks, microcontrollers, and cloud platforms to monitor soil and environmental parameters. Recent research integrates machine learning to enhance irrigation accuracy, achieving 30–50% water savings and higher yields. However, challenges remain in affordability, scalability, connectivity, energy dependence, and usability, particularly for small and medium-scale farmers.

To address these gaps, the study proposes a low-cost, scalable, and user-friendly IoT-based smart irrigation and soil monitoring system. The system monitors soil moisture, temperature, and humidity in real time, automates irrigation to minimize water waste, provides remote access through a dashboard or mobile application, and evaluates performance against traditional irrigation methods. The methodology employs a closed-loop system integrating sensors, a NodeMCU microcontroller, relay-controlled water pumps, and cloud platforms for data visualization and control.

Results show that the system effectively automates irrigation with rapid response, significantly reduces water and energy consumption, improves crop growth and yield quality, and lowers manual labor. The design is scalable and accessible to farmers, though it depends on reliable internet connectivity. Overall, the study demonstrates that IoT-enabled smart irrigation is a practical and sustainable approach to modern precision agriculture, promoting efficient water use and environmentally responsible farming.

Conclusion

This work introduces an Internet of Things (IoT) based Smart Irrigation and Soil Monitoring System that supports precision farming through automated irrigation and real-time soil and environmental condition monitoring. The system measures temperature, humidity, and soil moisture using inexpensive sensors and a NodeMCU/ESP8266 microcontroller, only turning on irrigation when necessary. When compared to manual irrigation, experimental results indicate a 30–40% decrease in water consumption. Additionally, cloud connectivity allows farmers to remotely monitor field conditions and control the pump, which reduces labor costs and increases efficiency. Data-driven decision-making is made possible by the Internet of Things, which enhances crop productivity and plant health. For small and medium-sized farming, the system is scalable and reasonably priced due to its low-cost and modular architecture. However, the system only monitors a few parameters at the moment, and its functionality is dependent on internet access and precise sensor calibration. Nutrient sensing, machine learning-based irrigation prediction, GSM/SMS alerts for internet-less areas, and solar-powered operation are possible future advancements. All things considered, the system provides a practical and long-lasting answer for contemporary precision farming.

References

[1] Alahi, M. E. E., et al. (2018). “Smart Sensing System for Soil Monitoring.” IEEE Sensors Journal. [2] Kumar, S., & Patel, R. (2020). “IoT-based Smart Irrigation using NodeMCU.” International Journal of Computer Applications. [3] Singh, A., & Sharma, D. (2019). “Low-Cost Soil Monitoring for Rural Farmers.” Journal of Agricultural Technology. [4] López, J., & Fernández, G. (2020). “Predictive Irrigation using Machine Learning.” Computers and Electronics in Agriculture. [5] Maheshwari, P., & Yadav, V. (2019). “Environmental Benefits of IoT in Agriculture.” Sustainability Reports. [6] M. Khan & R. Kumar, \"Precision Agriculture Using IoT-Based Sensor Networks,\" IEEE Sensors Journal, Vol. 15, No. 7, pp. 1256- 1265, 2021. [7] R. Patel, \"Cloud-Based Smart Irrigation Management System,\" IEEE International Conference on IoT and Smart Cities, 2022. [8] Prakasha M, Puneeth BR, Kumar C. Detection and tracking of moving objects using image processing. International Journal of Engineering Science Invention Development. 2017;4(1):28-39. [9] Puneeth BR, Nethravathi PS. A literature review of the detection and categorization of various arecanut diseases using image processing and machine learning approaches. International Journal of Applied Engineering and Management Letters. 2021;5(2):183-204. [10] Puneeth BR, Nethravathi PS. Bicycle industry in India and its challenges: A case study. International Journal of Case Studies in Business, IT and Education. 2021;5(2):62-74. [11] Gutiérrez, J., Villa-medina, J.F., Nieto-Garibay, A., Porta-gándara, M.Á., Gutierrez, J., Villa-medina, J.F., Nieto-Garibay, A., Porta-Gandara, M.A., 2014. Automated irriga tion system using a wireless sensor network and GPRS module. IEEE Trans. Instrum. Meas. 63, 166–176. https://doi.org/10.1109/TIM.2013.2276487 [12] K.Sekaran,M.N.Meqdad,P.Kumar,S.Rajan,andS.Kadry,“Smartagriculturemanagement systemusinginternetofthings,” Telkomnika(TelecommunicationComput.Electron.Control., vol. 18, no. 3, pp. 1275-1284, 2020, doi: https://doi.org/10.12928/TELKOMNIKA.v18i3. 14029. [13] ArchieBalingitSunga,JowieLumanogAdvincula,“The“Plantito/Plantita”Gardeningduring the Pandemic,” Community Psychology in Global Perspective, Vol 7, Issue 1, 88-105, 2021. [14] Y. Shekhar, E. Dagur, S. Mishra, R. J. Tom, M. Veeramanikandan, and S. Sankaranarayanan, “Intelligent IoT based automated irrigation system,” Int. J. Appl. Eng. Res., vol. 12, no. 18, pp. 7306-7320, 2017. [15] Sarika Tale, Sowmya P, “Intelligent Automatic Irrigation System,” Int. J. Computer Science and Information Technologies, vol. 7(1), pp. 141-143, 2016. [16] Mean Squared Error [WWW Document], 2018. Tutor vista (accessed 8.29.18). https:// math.tutorvista.com/statistics/mean-squared-error.html. [17] O’Shaughnessy, S.A., Evett, S.R., 2010. Canopy temperature-based system effectively schedules and controls center pivot irrigation of cotton. Agric. Water Manage. 97, 1310–1316. https://doi.org/10.1016/j.agwat.2010.03.012. [18] Goldstein, A., Fink, L., Meitin, A., Bohadana, S., Lutenberg, O., Ravid, G., 2017. Applying machine learning on sensor data for irrigation recommendations: revealing the agronomist’s tacit knowledge. Precis. Agric. 19, 421–444. https://doi.org/10.1007/ s11119-017-9527-4 [19] Vermesan, O. and Friess, P. (2013) “Internet of things: converging technologies for smart environments and integrated ecosystems”, Aalborg Denmark: River Publishers. ISBN 978-87-92982-96-4. [20] Open Ag Data Alliance (2016), [Online]. Available: http://openag.io/ [Accessed: 06 February 2017]. [21] Jazayeri, M., Liang, S. and Huang, C. (2015) “Implementation and Evaluation of Four Interoperable Open Standards for the Internet of Things”, Sensors, Vol. 15, No. 9, pp. 24343-24373. ISSN 1424-8220. DOI:10.3390/s150924343. [22] Davis, S.L., Dukes, M.D., Miller, G.L., 2009. Landscape irrigation by evapotranspiration-based irrigation controllers under dry conditions in Southwest Florida. Agric. Water Manage. 96, 1828–1836. https://doi.org/10.1016/j.agwat.2009.08.005. [23] Pospíšil, A. (2015) “T-Mobile post Avi sit pro internet v?cí”, Mobil Mania [Online] Available: http://www.mobilmania.cz/clanky/t-mobile-postavi-sit-pro-internet-veci/sc-3-a-1331933/default. aspx [Accessed: 04 February 2016]. [24] Gluhak, A., Krc, S., Nati, M., Pfisterer, D., Mitton, N. and Razafindralambo, T. (2011) “A survey on facilities for experimental internet of things research”, IEEE Communications Magazine, Vol. 49, No 11, pp. 58-67. ISSN 0163-6804. DOI: 10.1109/MCOM.2011.6069710. [25] Khan, R., Khan, U., Zaheer, R. and Khan, S. (2012) “Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges. In: 2012 ”10th International Conference on Frontiers of Information Technology. IEEE, pp. 257-260. ISBN 978-0-7695-4927-9. DOI: 10.1109/FIT.2012.53.

Copyright

Copyright © 2025 S. Singh, S. Surbhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.