ECO-Guard: An Integrated AI-Sensor System for Monitoring Wildlife and Sustainable Forest Management

Introduction

Welcome to "The ECO-Guard" initiative, where environmental responsibility and innovation unite to empower sustainable tourism and wildlife preservation. This project is the result of collaborative efforts by Ch. Nikhilesh Krishna, Sujay Bharath Raj, N. Kireeti Sai Bharadwaj, and Avishek Rauniyar, students of S2-B.Tech AIE A Batch. We are excited to share our experiences and outcomes from this journey of exploration and discovery.

Project Overview

🌿"The ECO-Guard" aligns with multiple Sustainable Development Goals (SDGs) like:

• Responsible Consumption and Production (SDG 12)
• Climate Action (SDG 13)
• Life on Land (SDG 15)
• Industry, Innovation, and Infrastructure (SDG 9)
• Sustainable Cities and Communities (SDG 11)
• Partnerships for the Goals (SDG 17)

Problem Definition

With rapid urbanization and deforestation, wildlife habitats and ecosystems are constantly threatened. Sustainable tourism often overlooks the environmental impact on local flora and fauna. A robust, real-time monitoring system is critical to managing wildlife conservation, forest management, and responsible tourism. "The ECO-Guard" addresses this gap by providing a scalable IoT solution that leverages AI to support these efforts.

Abstract

Our project centres around an IoT-based system designed to advance sustainable tourism and wildlife conservation. Through the use of technology and ecological awareness, we foster a symbiotic relationship between people and nature, reducing ecological footprints and protecting biodiversity.🌳🔍

Key Features

1. Smart Sensor Integration: Advanced sensors capture environmental metrics such as temperature, humidity, air quality, and wildlife activity in real-time. This enables data-driven decisions for effective forest management.🦋🌍

2. AI-Powered Insights: 📊 Machine learning models transform data into actionable insights, giving forest rangers and conservationists invaluable tools for ecological preservation.

3. Tourist Guidance: 🌐 A network of interconnected devices guides tourists through the ecosystem, ensuring minimal ecological disturbance and a sustainable, immersive experience.

4. Positive Impact: By integrating various SDGs, our project is a climate action initiative that promotes biodiversity, sustainable tourism, and community support.

Project Showcase

We presented "The ECO-Guard" at the "UNLEASHING THE IMPACT OF SDG IN REAL WORLD APPLICATION OF ENERGY AND AI" expo and poster presentation hosted by the School of Computing and School of Engineering at Amrita Vishwa Vidyapeetham, Kollam, India, on August 17th, 2023.

For more details, view our publication: ECO-Guard | Springer Nature

Repository Structure

├───22AIE114_Project_codes_A12
│   ├───Arduino
│   │   ├───Arduino and sensors
│   │   └───Wifi_ESP32cam
│   └───YOLO(Python)
├───22AIE114_Project_Group_A12
│   ├───22AIE114_Project_codes_A12
│   │   ├───Arduino
│   │   │   ├───Arduino and sensors
│   │   │   └───Wifi_ESP32cam
│   │   └───YOLO(Python)
│   ├───22AIE114_Project_Photos_A12
│   └───22AIE114_Project_Poster_A12
├───22AIE114_Project_Photos_A12
└───22AIE114_Project_Poster_A12

Files and Directories

• Arduino and sensors: Code for interfacing Arduino with environmental sensors.
• Wifi_ESP32cam: Code for ESP32 camera module integration, which supports real-time monitoring.
• YOLO (Python): YOLO model implementation in Python for object detection in captured footage.
• 22AIE114_Project_Photos_A12: Project images and system setup photos.
• 22AIE114_Project_Poster_A12: Project poster used for exhibitions and presentations.
• Working Model.mp4: Demo video showing the working model of ECO-Guard in action.

Demo Video

To see the ECO-Guard system in action, please watch our demo video:

This video provides a comprehensive overview of the project, including setup, data gathering, and real-time monitoring of environmental and wildlife metrics.

Instructions for Setup

1. Arduino and Sensors Setup:

   • Upload the Arduino code from the Arduino and sensors folder to your Arduino board.
   • Ensure that the sensors are properly connected as per the schematic provided in the folder.

2. ESP32 Camera Integration:

   • Flash the code from the Wifi_ESP32cam folder to the ESP32 cam module.
   • Configure the Wi-Fi settings within the code for real-time data transmission.

3. YOLO Object Detection:

   • Run the YOLO Python scripts to enable wildlife detection in video footage.
   • Follow the README in the YOLO directory for installation and usage instructions.

Running the Application

• Follow the above setup steps.
• Use the provided Streamlit web app for live monitoring at ecoguardamrita.streamlit.app.
• Use the telegram channel to view all the predictions made by ECO-Guard at ECO-Guard Telegram Group.

Acknowledgments

We extend our heartfelt gratitude to:

• Dr. V Ravikumar Pandi
• Bri Deepthi
• Ms. Soumya
• Ms. Kavya

These individuals from the School of Engineering at Amrita Vishwa Vidyapeetham provided crucial guidance and support for our project.

Join Us on Our Journey

Join us as we strive to harmonize human ambitions with the rhythms of nature, fostering responsible tourism and amplifying wildlife conservation through technology and empathy.

Project Hashtags

• #EcoSenseAiNator
• #SustainableTourism
• #WildlifeConservation
• #amritaschoolofcomputing
• #amritapuricampus
• #amritaviswavidhyapeetham
• #amritaschoolofengineering
• #InnovationForGood
• #SDGs
• #FutureForward
• #TechForGood
• #ClimateAction
• #UNESCO
• #UNDP

Thank you for your interest in "The ECO-Guard" project. Together, we can make a positive impact on our environment and the world. 🌱🌎