Integrated Smart Agricultural Field Protection and Automatic Irrigation System

Authors

  • M. Nirmala Devi, C. Nishanthi, A. Savitha, P. Vinitha Gnanamani College of Technology, Namakkal, Tamil Nadu, India Author

DOI:

https://doi.org/10.15662/IJEETR.2026.0802150

Keywords:

Smart Agriculture, IoT, Automatic Irrigation System, Field Protection, Animal Detection, ESP32 Camera, Raspberry Pi, Soil Moisture Sensor, GSM Module, Sound Deterrent System, Dynamic Sound Variation

Abstract

This paper presents an integrated smart agricultural field protection and automatic irrigation system using advanced Internet of Things (IoT) technology. The proposed system is designed to monitor environmental conditions such as soil moisture, temperature, and humidity in real-time. The collected data is processed using a NodeMCU microcontroller and transmitted to a Raspberry Pi for intelligent decision-making. Based on soil moisture levels, the system automatically controls irrigation using a relay-driven water pump, thereby reducing water wastage and improving efficiency. 

In addition to irrigation, the system incorporates a field protection mechanism using an ESP32 camera module, which continuously monitors the agricultural field for any intrusion or unwanted activities. A GSM module is integrated to send real-time alerts to farmers, ensuring quick response and improved crop safety. Furthermore, the system includes an intelligent animal detection mechanism. When an animal enters the field, the system analyses the type of animal and generates a specific irritating sound to repel it. 

To prevent animals from getting habituated to a particular sound, the system is programmed to automatically change the sound at regular intervals. For each type of animal, multiple irritating sounds are stored, and the system cycles through these sounds every five days. This dynamic sound variation ensures continuous effectiveness of the deterrent mechanism and enhances field protection. 

The proposed system minimizes manual labor, optimizes water usage, and improves overall agricultural productivity. It is cost-effective, reliable, and suitable for modern smart farming applications

References

1. S. Md. Jakheer, G. Ramesh, V. S. Kumar, et al., “Smart Agriculture System for Animal Detection and Automated Irrigation Control Using IR Camera, Raspberry Pi and Sensor Integration,” Int. J. Sci. Res. Eng. Manag., Apr. 2024. [Online]. Available: https://www.researchgate.net/publication/379991186⁠�

2. H. Narula and A. Pathak, “IoT Based Smart Agriculture and Animal Detection System,” Int. J. Res. Anal. Rev., 2022. [Online]. Available: https://www.ijraset.com/research-paper/iot-based-smart-agriculture-and-animal-detection-system⁠

3. J. Miao, D. R. Rajasekhar, S. Mishra, et al., “A Fog-based Smart Agriculture System to Detect Animal Intrusion,” IEEE ICPADS, 2023. [Online]. Available: https://www.mdpi.com/1999-5903/16/8/296⁠�

4. D. Vallejo‑Gómez, M. Osorio, and C. A. Hincapié, “Smart Irrigation Systems in Agriculture: A Systematic Review,” Agronomy, vol. 13, no. 2, 2023. [Online]. Available: https://www.mdpi.com/2073-4395/13/2/342⁠�

5. X. Liu, Z. Zhao, and A. Rezaeipanah, “Intelligent and automatic irrigation system based on IoT using fuzzy control technology,” Scientific Reports, vol. 15, Art. no. 14577, 2025. [Online]. Available: https://www.nature.com/articles/s41598-025-98137-2⁠

6. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746

7. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2

8. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9

9. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025

10. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428

11. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w

12. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.

13. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.

14. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.

15. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007

16. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721

17. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022

18. Vimal, V. R., John Justin Thangaraj, S., Narayanan, L. K., Alagu Thangam, S., Loganayagi, S., & Balakrishnan, S. (2025, April). Enhanced Phishing Detection and Classification Using an Ensemble Machine Learning Approach for URL Analysis. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 229-239). Singapore: Springer Nature Singapore.

19. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. ResearchGate.

20. Murugeshwari, B., Jayakumar, C., & Sarukesi, K. (2012). Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications, 55(7).

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Published

2026-03-28

How to Cite

Integrated Smart Agricultural Field Protection and Automatic Irrigation System. (2026). International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 1839-1844. https://doi.org/10.15662/IJEETR.2026.0802150