Year: 2025
Type: article
Abstract: This study aims to explore the application of Artificial Intelligence (AI) technology in wetland bird monitoring to improve monitoring efficiency and accuracy. By integrating modern communication networks and AI technologies, a sky-ground integrated monitoring network covering key ecosystems has been established. In the Chen Lake Wetland in Hubei, high-definition cameras, sound sensors, and drones were deployed to achieve real-time video surveillance and sound data collection of bird activities. Deep learning algorithms were utilized to analyze video streams and sound signals, automatically identifying bird species and behavior patterns. The study selected 11 bird species as monitoring objects and collected a large amount of image data using drones. YOLOv8 and PP-HGNET algorithms were employed for bird recognition and classification. The results showed that the AI monitoring system achieved a mean Average Precision (mAP) of 87.7% in bird recognition accuracy, providing reliable data support for bird dynamic monitoring, epidemic source monitoring, and conservation research. Additionally, AI technology can monitor wetland environmental indicators, offering a scientific basis for ecological protection. The research demonstrates that AI technology has broad application prospects in wetland bird monitoring, capable of driving the innovation of monitoring technologies and enhancing the efficiency and effectiveness of ecological conservation.
Authors: Zhao Liang, Zou Yan-Long, Wang Li-Quan, Li Jia, Cao Teng-Fang, Tian Shan
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: bronze
OpenAlex ID: W4409536200