An Acoustic Bird Repellent System Leveraging Edge Computing and Machine Learning Technologies

In contemporary agriculture, protecting ripening fruit crops is crucial for enhancing agricultural productivity and preventing economic losses. This study presents an intelligent, Edge Computing-based acoustic bird repellent system developed to deter birds from damaging ripening fruits in orchards. The system is equipped with microphone sensors to detect bird vocalizations and audible alarms utilized to scare off the birds. Machine learning algorithms analyze the collected audio data to identify bird sounds in real-time and generate effective bird repellent noises accordingly. This Edge Computing solution minimizes data transmission latency and network load, providing rapid and efficient protection. The system can be tailored to different bird species and optimized according to specific conditions within the orchard. Experimental tests conducted in this study demonstrate that the proposed acoustic bird repellent system is successful in safeguarding fruit crops and significantly mitigating the detrimental impact of birds. In conclusion, the Edge Computingbased acoustic bird repellent system offers a sustainable and environmentally friendly solution in the agricultural domain, assisting in the prevention of yield loss in orchards and enhancing economic benefits.