Autonomous Bat Echolocation Monitoring System with Machine Learning for Real-Time Pest and Biodiversity Management

The preservation of biodiversity and the mitigation of agricultural pests are crucial components of sustainable agriculture. This study presents the Autonomous Bat Echolocation (ABE) Monitoring System, a sustainable approach to biodiversity preservation and pest control in agriculture. The system combines auditory sensors, low-power devices, and deep learning algorithms to monitor bat activity by detecting and classifying species based on their echolocation calls. The integration of the BatDetect2 model allowed real-time processing on low-power devices within our monitoring stations, enabling efficient and continuous monitoring of bat activity. By establishing a network of these stations, the system provides real-time detection as an early warning for potential pest infestations in agricultural fields. Overall, the ABE Monitoring System is a scalable, eco-friendly tool that helps in biodiversity monitoring and supports sustainable agricultural practices, aiding farmers in pest prediction and management.