Protecting Endangered Birds with Edge-AI: Real-Time Detection of Invasive Cats in Natural Parks

Monitoring invasive species is essential for protecting biodiversity in sensitive ecosystems. In the natural park of Torrevieja (Alicante, Spain), domestic cats threaten local bird populations, including endangered species. To address this issue, we developed a system leveraging deep-learning models, including YOLO (You Only Look Once), to detect cats in real-time automatically. Our solution involves deploying edge-AI cameras equipped with LoRa communication technology to efficiently transmit detection data to the cloud. This infrastructure enables continuous monitoring, accurate detection, and prompt invasive species reporting while optimizing power consumption and network bandwidth. In this paper, we present the development and evaluation of multiple deep-learning models, assess their prediction accuracy, and discuss the integration of LoRa technology to enhance data transmission in remote areas. Our findings demonstrate the feasibility and effectiveness of using edge-AI and IoT technologies for biodiversity conservation in protected natural environments.