🌱 Agri-Vision: Cotton Crop Analysis System

Agri-Vision is an AI-powered system that analyzes cotton crop images to determine growth stages and health conditions.
It helps farmers and researchers make informed decisions about crop management and harvest timing.

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📌 Overview

Agri-Vision uses deep learning and computer vision techniques to:

• Detect cotton growth phases
• Identify crop health issues and diseases
• Provide confidence scores and actionable recommendations
• Offer both a web interface and a REST API

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✨ Features

• 🌿 Growth Phase Detection (4 stages)
• 💚 Health Assessment (disease & damage detection)
• 🤖 AI-Powered Analysis using deep learning
• 🌐 Web Interface (Flask-based)
• 📊 REST API Support for programmatic access
• 🎯 Smart Recommendations for farmers
• ⚡ Fast Processing (< 2 seconds per image)

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🛠️ Tech Stack

• Python
• Flask
• TensorFlow
• OpenCV
• HTML/CSS
• JavaScript

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📁 Project Structure

Agri-Vision/
│
├── results/                        # Stores output results and visualizations
│   └── training_history.png
│
├── static/                         # Static assets
│   ├── css/
│   ├── uploads/
│   └── favicon.png
│
├── templates/                      # Flask HTML templates
│   ├── index.html
│   ├── results.html
│   └── upload.html
│
├── .env                            # Environment variables (create manually)
├── .gitignore
├── app.py                          # Main Flask application
├── LICENSE
├── README.md
├── requirements.txt
└── train.py                        # Model training script

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🚀 Project Setup & Execution

Follow the steps below to run the project locally.

1️⃣ Clone the Repository

git clone <repository-url>
cd <project-folder>

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2️⃣ Create a .env File

Create a .env file in the root directory of the project and add your secret key.

Example

SECRET_KEY=your_secret_key_here

Replace your_secret_key_here with your actual secret key.

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3️⃣ Install Python Dependencies

Install all the required Python packages using:

pip install -r requirements.txt

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4️⃣ Run the Project

Start the application using:

python app.py

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✅ Setup Complete

The project should now be running successfully on your local machine.

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📊 Growth Phases Detected

• 🌱 Vegetative / Budding – Early growth stage
• 🌸 Flowering – Flower development stage
• 🍂 Bursting (Ripped) – Cotton bolls opening
• ✅ Harvest Ready – Optimal harvest time

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🩺 Health Issues Identified

• ✅ Healthy – No issues detected
• 🐛 Pink Bollworm Damage – Pest infestation detected
• 🎨 Discoloration – Nutrient or water deficiency
• ⚠️ Other Damage – Miscellaneous crop issues

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🛠️ API Reference

Analyze Image (POST Request)

curl -X POST -F "file=@cotton_image.jpg" http://localhost:5000/api/analyze

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📦 Response Format (JSON)

{
  "status": "success",
  "analysis": {
    "stage": "Bursting (Ripped)",
    "stage_confidence": 0.87,
    "health_status": "Pink Bollworm Damage",
    "health_confidence": 0.76,
    "health_score": 68.5,
    "is_ripped": true,
    "has_damage": true
  },
  "recommendations": ["..."]
}

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🎯 Usage

🌐 Web Interface

1. Go to /analyze
2. Upload a cotton crop image
3. View detailed analysis results
4. Download the JSON report if needed

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📈 Model Performance

• ✅ Accuracy: ~85–90% on test data
• ⚡ Processing Time: < 2 seconds per image
• 🖼️ Supported Formats: JPG, PNG, JPEG
• 📦 Maximum File Size: 10MB

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🚀 Future Enhancements

• 📱 Mobile application support
• 🎥 Real-time video analysis
• 🌾 Multi-crop support
• ☁️ Weather data integration
• 📊 Yield prediction system
• 🧠 Improved AI models

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🤝 Contributing

Contributions are welcome to improve Agri-Vision and make it more useful for farmers, researchers, and developers.

Feel free to:

• Fork the repository
• Create a feature branch
• Submit a pull request

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📌 Contribution Guidelines Reminder

Before Contributing

• Do not start working on an issue until it is assigned to you.
• Comment on the issue you want to work on.
• Wait for maintainers to assign the issue before creating a PR.

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🔀 Pull Request Guidelines

• Mention the related issue in your PR description using:
  • Closes #issue_number
  • Fixes #issue_number
• Keep PRs focused on a single issue/topic.
• Add screenshots or demo videos for UI-related changes.
• Sync your branch with the latest repository changes before submitting a PR.

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📝 Additional Notes

• Follow the project structure and coding style.
• Avoid spam or duplicate PRs/issues.
• Be respectful during code reviews and discussions.
• Beginners are welcome — feel free to ask questions if stuck ✨

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📜 License

This project is licensed under the MIT License.
See the LICENSE file for more details.

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🙌 Acknowledgements

Special thanks to:

• TensorFlow
• Flask
• OpenCV
• Open-source contributors
• Agricultural research datasets

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❤️ Made with Passion by neeru24

⭐ If you found this project helpful, consider giving it a star. ⭐