Biodiversity Monitoring System
AI-powered biodiversity monitoring platform using camera traps and video analysis to automatically detect and classify wildlife including animals, birds, fish, and plants.
Background
Biodiversity monitoring is essential for ecosystem conservation, environmental impact assessment, and wildlife management. Traditional manual observation methods are labor-intensive, time-consuming, and limited in coverage. This project leverages advanced computer vision and deep learning technologies to build an automated biodiversity monitoring system that can identify and track wildlife species 24/7 across remote locations with minimal human intervention.
The system addresses key challenges in ecological research: species identification accuracy, real-time monitoring capability, large-scale data processing, and integration of multi-source sensor data from distributed camera networks.
Technical Architecture
Hardware System
- Camera Traps: Motion-triggered wildlife cameras with infrared night vision
- Underwater Cameras: Waterproof cameras for fish and aquatic species monitoring
- High-Resolution IP Cameras: For continuous video streaming in key habitats
- Edge Computing Devices: NVIDIA Jetson for on-site AI inference
- Solar Power Systems: Off-grid power supply for remote deployments
- LoRa/4G Communication: Wireless data transmission from field devices
Software Stack
- Image/Video Capture: OpenCV + GStreamer for multi-source input
- Object Detection: YOLOv8/YOLOv9 for real-time species detection
- Species Classification: ResNet/EfficientNet fine-tuned models
- Tracking Algorithm: DeepSORT for multi-object tracking
- Data Pipeline: Apache Kafka for stream processing
- Backend API: FastAPI + PostgreSQL + MinIO
- Visualization Dashboard: React + ECharts for data analytics
Core Features
Multi-Species Detection
- Wildlife Detection: Automatic detection of mammals, reptiles, and amphibians
- Bird Recognition: Species identification with behavior classification (flying, perching, feeding)
- Fish Monitoring: Underwater species detection and population counting
- Plant Identification: Flora species recognition and health assessment
Intelligent Analysis
- Population Estimation: Statistical analysis of species abundance
- Activity Pattern Analysis: Temporal distribution of wildlife activities
- Behavior Recognition: Feeding, mating, migration behavior detection
- Anomaly Detection: Alerts for invasive species or abnormal patterns
Data Management
- Real-time Streaming: Live video analysis with instant notifications
- Historical Archive: Searchable database with species tags and timestamps
- Export & Reporting: Automated biodiversity reports and data exports
- API Integration: RESTful API for third-party system integration
Results
- Detection accuracy exceeds 92% across 200+ species
- Processing capability of 100+ concurrent video streams
- 80% reduction in manual species identification workload
- Successfully deployed in 15+ conservation sites
- Over 500,000 wildlife observations recorded and classified
