Tags:
artificial-intelligence machine-learning deep-learning computer-vision yolo cnn opencv flask tensorflow pytorch pandas numpy poultry-disease-detection agriculture-tech smart-farming ai-in-agriculture livestock-monitoring edge-ai iot-integration end-to-end-project portfolio-project
An AI-powered poultry health monitoring system leveraging YOLO, CNN, and OpenCV for real-time detection of diseases based on posture and behavioral analysis.
BioFlock AI is designed to help poultry farmers monitor the health of their flocks using computer vision and deep learning. The system detects abnormal behavior patterns and predicts potential diseases early, enabling farmers to take preventive measures.
- Real-time Monitoring – Live video analysis using YOLO object detection.
- Disease Detection – Posture and movement analysis to identify health issues.
- Dashboard Integration – Centralized data visualization for decision-making.
- API Connectivity – Flask API for easy integration with IoT devices or web apps.
- High Accuracy – Trained on 500K+ annotated images.
- Languages: Python, jupyter
- Frameworks & Libraries: YOLO, CNN, OpenCV, Flask
- Tools: Git, GitHub
- Deployment: Flask APIs + Dashboard
- Capture – Live camera feed or uploaded video/images.
- Detect – YOLO detects poultry and identifies individual postures.
- Analyze – CNN model classifies posture as healthy or abnormal.
- Predict – Alerts generated for suspected diseases.
- Display – Dashboard shows statistics and historical trends.
# Clone repository
git clone https://github.com/your-username/BioFlock-AI.git
cd BioFlock-AI
# Install dependencies
pip install -r requirements.txt
# Run Flask API
python app.py