IoT-Enabled Modular Bird Repellent System: A Pattern-Based Approach for Enhanced Detection and Control

Effective bird repellent systems are crucial for mitigating the negative impacts of birds on agriculture, aviation safety, and urban infrastructure. This paper presents an internet of things (IoT)-enabled modular bird repellent system that integrates real-time sensing, intelligent classification, and adaptive actuation to enhance efficiency and scalability. A pattern-based method is proposed for classifying birds and assessing the risk of bird populations. Experimental data showed that the detection accuracy of the perception module reached 95.2%, and the signal-to-noise ratio during daytime and nighttime was about 43.6 dB and 40.5 dB after stable image acquisition. The success rate of the driving module during the daytime within 500 m2 was about 97.6%. The error rate of human-machine interaction module status detection was less than 2.6%, and the remote-control response time tended to be 157 ms. In communication testing, Ethernet, serial port, and USB communication had lower packet loss and error rates, and biomimetic buss performance was better than conventional bus. After 48 hours of on-site testing, the bird repellent efficiency reached 94.6%, with only 2 malfunctions and a recovery time of less than 5 seconds. The integration of functional modules ensured efficient collaboration, while the inclusion of multiple communication methods and a biomimetic bus enhanced the system’s stability and adaptability to complex environments. This work demonstrates the potential of IoT-based intelligent bird repellent systems for precision wildlife management and environmental sustainability.