Recent Advances in AI-Based Wildlife Monitoring: Multimodal Detection, Edge Computing, and Conservation Applications.

Key Points

• This review aims to explore how AI and computer vision are utilized for wildlife monitoring and species identification.
• Analysis of seven key studies from 2020 to 2026 on AI applications in wildlife detection.
• Evaluation of techniques including CNNs, YOLO architectures, and bioacoustic monitoring.
• Focus on edge computing capabilities for real-time data processing in wildlife conservation.
• YOLO-based models achieve over 90% mean Average Precision (mAP) for wildlife detection.
• Lightweight AI variants support effective edge deployment for rapid responses.
• Future research directions emphasize multi-modal data integration and scalable individual species recognition.

Abstract