🌱 PlantCare AI - Disease Detection System

AI-powered plant disease detection with 92% accuracy using Deep Learning

An intelligent web application that uses Convolutional Neural Networks (CNN) to detect plant diseases from images and provides AI-powered treatment recommendations.

✨ Features

• 🔍 Real-time Disease Detection - Identify 38 different plant diseases with 92% accuracy
• 🤖 AI Chat Assistant - Get personalized plant care advice powered by Claude AI
• 📊 Comprehensive Analysis - Detailed symptoms, causes, and treatment plans
• 💊 Treatment Solutions - Step-by-step remediation guides
• 🌿 Prevention Tips - Proactive care recommendations
• 📱 Responsive Design - Beautiful UI that works on all devices
• ⚠️ Confidence Warnings - Low confidence alerts when model needs more data

🎯 Supported Plants

• 🍅 Tomato (10 diseases)
• 🥔 Potato (3 diseases)
• 🍎 Apple (4 diseases)
• 🌽 Corn (4 diseases)
• 🍇 Grape (4 diseases)
• 🍑 Peach (2 diseases)
• 🌶️ Pepper (2 diseases)
• 🍓 Strawberry (2 diseases)
• And more! (38 total plant conditions)

📊 Model Performance

Metric	Score
Validation Accuracy	92%
Top-3 Accuracy	98%
Training Images	43,456
Validation Images	10,864
Classes	38

🚀 Technology Stack

Backend:

• Python 3.12
• Flask 3.0
• TensorFlow 2.15
• Keras

Machine Learning:

• Transfer Learning (MobileNetV2)
• Image Preprocessing
• Data Augmentation
• CNN Architecture

Frontend:

• HTML5
• JavaScript (ES6+)
• Tailwind CSS
• Lucide Icons

AI Integration:

• Anthropic Claude API
• Natural Language Processing
• Context-aware responses

🛠️ Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package manager)
• 4GB RAM minimum
• Internet connection (for AI chat)

Setup Instructions

1. Clone the repository

git clone https://github.com/learner0018/Plantcare-ai.git
cd plantcare-ai

2. Create virtual environment

# Windows
python -m venv venv
venv\Scripts\activate

# Mac/Linux
python3 -m venv venv
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Download the trained model

The trained model file is too large for GitHub.

5. Set up environment variables

Create a .env file in the project root:

ANTHROPIC_API_KEY=your_api_key_here

Get your API key from: https://console.anthropic.com/

6. Run the application

python app.py

7. Open in browser

http://localhost:5000

📸 Usage

1. Upload Image - Take or select a photo of the affected plant
2. Analyze - Click "Analyze Plant" to process the image
3. Review Results - See disease identification and confidence score
4. Read Treatment - Follow the step-by-step treatment guide
5. Ask Questions - Use the AI chat for personalized advice

📁 Project Structure

plantcare-ai/
│
├── app.py                      # Main Flask application
├── model_handler.py            # AI model inference
├── disease_database.py         # Disease information database
├── ai_chat_handler.py          # Claude AI integration
├── train_model.py              # Model training script
├── requirements.txt            # Python dependencies
├── .env                        # Environment variables (not in repo)
├── .gitignore                  # Git ignore rules
├── LICENSE                     # MIT License
├── README.md                   # This file
│
├── model/
│   ├── plant_model.h5         # Trained model (download separately)
│   ├── classes.txt            # Disease class names
│   └── training_history.png   # Training performance graph
│
├── templates/
│   └── index.html             # Frontend UI
│
├── static/
│   └── js/
│       └── app.js             # Frontend JavaScript
│
└── uploads/                    # Temporary image storage (not in repo)

🧠 How It Works

Model Architecture

Input Image (224x224x3)
    ↓
MobileNetV2 (Pre-trained on ImageNet)
    ↓
Global Average Pooling
    ↓
Dense Layer (128 neurons, ReLU)
    ↓
Dropout (0.5)
    ↓
Output Layer (38 neurons, Softmax)
    ↓
Disease Prediction

Training Process

1. Data Collection - PlantVillage dataset (54,000+ images)
2. Preprocessing - Resize, normalize, augment
3. Transfer Learning - Fine-tune MobileNetV2
4. Training - 10 epochs with validation
5. Evaluation - 92% accuracy achieved
6. Deployment - Export to .h5 format

🎓 Training Your Own Model

If you want to retrain the model:

# Download dataset from Kaggle
# Place in dataset/color/ folder

# Run training script
python train_model.py

# Model will be saved to model/plant_model.h5

🔒 Security

• ✅ API keys stored in .env (excluded from Git)
• ✅ No sensitive data in code
• ✅ Secure file upload validation
• ✅ CORS protection enabled
• ✅ Input sanitization

🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

🐛 Known Issues

• Large model file requires separate download
• First API request may be slow (model loading)
• Limited to 38 plant disease classes currently

🔮 Future Enhancements

• Mobile app (React Native)
• Offline mode support
• More plant species
• Disease progression tracking
• Community forum
• Multiple language support

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Dataset: PlantVillage Dataset from Kaggle
• Base Model: MobileNetV2 from TensorFlow
• AI Chat: Anthropic Claude API
• Icons: Lucide Icons
• Styling: Tailwind CSS

📧 Contact

Aditya Singh

• GitHub: https://github.com/johnn021as
• LinkedIn: https://www.linkedin.com/in/aditya-singh-083b02351/
• Email: adityasinghdrdo70@gmail.com

Project Link: https://github.com/johnn021as/Plantcare-ai

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Made with ❤️ and Python