Home / Current Issue / Paper 1722535
Development of an AI-Integrated IoT System for Bird Detection and Repelling in Farmlands
Subject area: Science,Engineering and Technology · Area of research: IOT and AI
Abstract
Bird-related crop damage presents a persistent and significant challenge for farmers, particularly those cultivating small plots in developing regions. This study introduces and evaluates an AI-enhanced Internet of Things (IoT) system designed to detect and repel birds from farmland. The system integrates a Passive Infrared (PIR) motion sensor, an Arduino microcontroller, a GSM module, an LED indicator, and an LCD to enable real-time field monitoring and alert farmers via SMS and voice calls when it detects bird activity. Field trials conducted at ten sites in Gombe State, Nigeria, demonstrated 90% motion-detection accuracy, effectively distinguishing genuine bird intrusions from non-threatening environmental movements. The system correctly identified motion in eight of ten tests, with the alarm triggering as intended in seven cases, improving on the 87% detection rate reported by Pendhari et al. (2020). The system's consistent performance, prompt alerting, and operational stability indicate its suitability for deployment in diverse agricultural contexts. Future work will focus on adding power backup, refining detection algorithms to minimize false alarms, extending field trial durations, and integrating advanced artificial intelligence and machine learning techniques to further improve accuracy and adaptability.
Keywords
Passive Infrared Sensor, Light Emitting Diode, Liquid Crystal Display, Internet of Things (IoT), Bird Repelling System, Smart Agriculture
References
[1] Chuyen, T. D., Nguyen, D. D., Cuong, N. C., & Thong, V. V. (2023). Design and manufacture of an IoT-based water-quality control system for aquaculture in Vietnam. Bulletin of Electrical Engineering and Informatics, 12(4), 1893–1900.
[2] Harsha, V. P. A., Koteswaramma, N., & Murali, C. B. K. (2019). IoT-based Raspberry Pi crop vandalism prevention system. International Journal of Innovative Technology and Exploring Engineering, 9(1), 3188–3192.
[3] Hippolyte, A., Emeline, P. S. A., Paulin, J. D., & Samadori, H. B. (2022). Effect of bird-scaring methods on crop productivity and the conservation of avian diversity in agroecosystems of Benin. International Journal of Biological and Chemical Sciences, 16(2), 527–542.
[4] Jaya, A., Amutha, M., Jeya, S., Karpagavalli, D. L., & Kavibharathi, M. (2020). Surveillance of farms from birds using Raspberry Pi. Journal of Advanced Research in Dynamical and Control Systems, 12(7), 8.
[5] Mohamed, W. M. W., Naim, M. N. M., & Abdullah, A. (2020). The efficacy of visual and auditory bird scaring techniques using a drone in paddy fields. IOP Conference Series: Materials Science and Engineering, 834(1), 012072. IOP Publishing
[6] Pankaj, R. D. (2025). Smart farming for farm security: Mitigating wild-bird intrusion in agricultural farms. The OCEM Journal of Management Technology and Social Sciences, 4(1), 209–216.
[7] Pedro, B. C., Lucía, B. M., Eduardo, H. F., Rubén, M. C., Ramón, G. C., & Fernando, M. C. (2023). AIoT in agriculture: Safeguarding crops from pest and disease threats. Sensors, 23(24), 9733.
[8] Pendhari, P. S., Shinde, A. P., & Tanpure, K. A. (2020). Scaring of birds in agriculture using IoT. International Research Journal of Modernisation in Engineering Technology and Science. IRJMETS
[9] Phetyawa, S., Kamyod, C., Yooyatiwong, T., & Kim, C.-G. (2022). Application and challenges of an IoT bird repeller system: As a result of bird behaviour. International Symposium on Wireless Personal Multimedia Communications, 323–327. IEEE
[10] Praveen, S., & Kavitha, G. (2020). IoT-based smart bird repeller for agricultural fields. International Journal of Scientific and Technology Research, 9(4), 3501–3505.
[11] Riya, R., KR, V., Sonamsi, S., & Jain, D. (2020, March). Automated bird detection and repeller system using IoT devices: An insight from the Indian agriculture perspective. Proceedings of the International Conference on Innovative Computing and Communications (ICICC), India.
[12] Kumar, R., Kumar, R., Gupta, S., Gupta, S., Hwang-Cheng, W., Hwang-Cheng, W., Hwang-Cheng, W., Kumari, C., Kumari, C., & Kumari, C. (2023). From Efficiency to Sustainability: Exploring the Potential of 6G for a Greener Future. Sustainability, 15(23), 16387.
How to cite this paper
@article{1722535,
author = {Yakubu Abubakar Maidu, Dr. Musa Abdullahi Yola},
title = {Development of an AI-Integrated IoT System for Bird Detection and Repelling in Farmlands},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {2},
pages = {3792-3798},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722535.pdf},
abstract = {Bird-related crop damage presents a persistent and significant challenge for farmers, particularly those cultivating small plots in developing regions. This study introduces and evaluates an AI-enhanced Internet of Things (IoT) system designed to detect and repel birds from farmland. The system integrates a Passive Infrared (PIR) motion sensor, an Arduino microcontroller, a GSM module, an LED indicator, and an LCD to enable real-time field monitoring and alert farmers via SMS and voice calls when it detects bird activity. Field trials conducted at ten sites in Gombe State, Nigeria, demonstrated 90% motion-detection accuracy, effectively distinguishing genuine bird intrusions from non-threatening environmental movements. The system correctly identified motion in eight of ten tests, with the alarm triggering as intended in seven cases, improving on the 87% detection rate reported by Pendhari et al. (2020). The system's consistent performance, prompt alerting, and operational stability indicate its suitability for deployment in diverse agricultural contexts. Future work will focus on adding power backup, refining detection algorithms to minimize false alarms, extending field trial durations, and integrating advanced artificial intelligence and machine learning techniques to further improve accuracy and adaptability.},
keywords = {Passive Infrared Sensor, Light Emitting Diode, Liquid Crystal Display, Internet of Things (IoT), Bird Repelling System, Smart Agriculture},
month = {August},
}