WildEcoWatch is an intelligent wildlife monitoring and conservation system that combines Artificial Intelligence (AI), Internet of Things (IoT), and Computer Vision to detect wildlife activity, prevent poaching, and support conservation efforts in remote forest environments.
The system utilizes real-time image processing, motion sensing, and automated alert mechanisms to identify animal or human intrusions and notify authorities instantly. Designed for deployment in resource-constrained environments, WildEcoWatch provides a scalable and cost-effective solution for wildlife protection.
Wildlife ecosystems face increasing threats from:
- Poaching and illegal hunting
- Unauthorized human intrusion
- Habitat destruction
- Delayed response from monitoring teams
- Lack of real-time surveillance in remote areas
Traditional monitoring approaches rely heavily on manual patrols and camera traps, which often fail to provide immediate actionable intelligence.
WildEcoWatch addresses these challenges through automated detection, monitoring, and alert generation.
- YOLO-based object detection model
- Real-time identification of animals from images and video streams
- Confidence score prediction for detected objects
- PIR Motion Sensor integration
- Camera-based surveillance
- Edge processing using Raspberry Pi
- Email notifications
- SMS alert support
- Voice/Text-to-Speech announcements
- Wildlife activity monitoring
- Poaching prevention assistance
- Event logging for analysis and reporting
- Suitable for forests, wildlife sanctuaries, farms, and protected zones
- Low-cost deployment using embedded hardware
PIR Sensor
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Motion Detection
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Camera Activation
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AI Object Detection (YOLO)
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Animal Classification
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Alerts Event Storage
(Email/SMS) (Logs & Images)
- Python
- OpenCV
- YOLO Object Detection
- Machine Learning
- Flask/Web Interface
- Email Notification Services
- Raspberry Pi
- PIR Motion Sensor
- Camera Module
- IR Sensor
- Buzzer/Alert System
- Computer Vision
- Embedded Systems
- IoT
- Edge Computing
- Wildlife Conservation
| Metric | Value |
|---|---|
| mAP | 85.6% |
| Precision | 83.4% |
| Recall | 78.0% |
The developed model demonstrated reliable animal detection performance, making it suitable for real-world wildlife monitoring applications.
- Wildlife Conservation
- Anti-Poaching Systems
- Forest Surveillance
- National Parks
- Biodiversity Monitoring
- Smart Environmental Protection
- Human-Wildlife Conflict Management
- Live video stream analytics
- Multi-species recognition
- GSM/Satellite-based communication
- Solar-powered autonomous deployment
- Cloud dashboard for centralized monitoring
- Mobile application for forest authorities
- Drone-based wildlife surveillance
This project demonstrates the integration of Artificial Intelligence and IoT technologies to create a sustainable wildlife monitoring solution capable of supporting conservation agencies through automated detection, real-time alerts, and intelligent surveillance.
Anuj Gavhane, Abhishek Balgude, Sushant Awate B.Tech Electronics & Telecommunication Engineering
Final Year Major Project
This project is developed for academic and research purposes. Feel free to use and extend it with proper attribution.