EnviroGuard AI - Real-time Hazard Detection & AI Assistant

🌍 Overview

EnviroGuard AI is an advanced real-time environmental monitoring system that leverages computer vision, AI, and IoT sensors to detect hazards like fire, smoke, pollution, and wildlife intrusions. It also features interactive AI-driven insights via natural language processing, making it a powerful tool for smart cities, industrial safety, and environmental conservation.

🚀 Features

• 🔥 Real-time hazard detection (fire, smoke, air quality, wildlife recognition)
• 🤖 AI-powered Q&A (Ask about environmental conditions, pollution levels, etc.)
• 🎙 Voice alerts & interaction (STT & TTS for accessibility)
• 🌡 IoT sensor integration (Air quality, temperature, gas sensors)
• 📊 Historical data analysis (Track environmental changes over time)
• 🌐 Web & mobile-friendly interface (For easy access & monitoring)

🛠 Technology Stack

Component	Technology Used
Computer Vision	OpenCV, YOLO, ViT, TensorFlow/PyTorch
AI & NLP	LangChain, Gemini AI
Voice Processing	gTTS, Whisper
IoT Integration	MQTT, Raspberry Pi, NodeMCU
Web/Mobile Interface	Gradio, Streamlit, FastAPI
Cloud & Storage	AWS, Firebase, GCP

📌 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/EnviroGuard-AI.git
   cd EnviroGuard-AI

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   python main.py

🏗 System Architecture

1. Input Sources:

   • Video feeds (CCTV, drones, mobile cameras)
   • IoT sensors (air quality, temperature, gas detection)
   • User queries (text/voice interactions)

2. Processing Modules:

   • Computer Vision Models for hazard detection
   • AI-powered Q&A using LangChain & Gemini AI
   • Sensor Fusion to combine vision & IoT data

3. Output & Alerts:

   • Web Dashboard & Mobile App for real-time monitoring
   • Voice & text-based alerts for hazards
   • Geospatial hazard mapping for visualization

🔥 Use Cases

• Smart Cities: Automated environmental monitoring & emergency alerts
• Industrial Safety: Fire & pollution detection in factories & plants
• Wildlife Conservation: Detecting endangered species & intrusions
• Public Health: Real-time air quality tracking & pollution insights

⚡ Challenges & Solutions

• Latency in detection: Optimized AI models for faster processing
• Multi-modal data fusion: Seamless integration of video, sensors, and NLP
• Deployment on Edge Devices: Lightweight models for Raspberry Pi/Jetson Nano

🚧 Roadmap

• ✅ Wildlife detection enhancement using YOLOv8
• 🔜 Mobile app deployment for remote monitoring
• 🔜 AI-driven preventive hazard alerts

📜 License

This project is licensed under the MIT License.

Deplpyment

https://www.veed.io/view/f291da0b-059b-4313-8b44-674f527c3509?panel=share

📬 Contact

For questions, suggestions, or collaborations, reach out:

• 📧 Email: yessasvini.s@gmail.com
• 🐙 GitHub: yessasvini23

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🚀 EnviroGuard AI - Smart Vision for a Safer Planet