FarmAI - Custard Apple Pest Management System

FarmAI is an advanced, AI-powered platform designed to assist custard apple farmers in managing pest outbreaks, detecting diseases, and optimizing spray schedules. The system integrates real-time weather data, satellite imagery, and biological models to provide accurate, location-based recommendations.

🌟 Key Features

• Disease Detection: utilizing PyTorch-based CNNs to detect diseases from leaf images.
• Pest Prediction: sophisticated forecasting models (XGBoost) combined with biological risk assessments.
• Automated Spray Scheduling: Reinforcement Learning (RL) based scheduler for optimal chemical usage.
• Digital Twin: Integration of manual observations with satellite indices (NDVI) for precise canopy estimation.
• Research Integration: Automatic retrieval of relevant research papers for disease management.

🏗️ Architecture

The project is divided into two main components:

• Backend: A FastAPI-based server handling AI inference, data processing, and API endpoints.
• Frontend: A modern React application (Vite + TailwindCSS) providing the user interface.

🚀 Getting Started

Follow these instructions to set up the project locally.

Prerequisites

• Python: Version 3.10 or higher
• Node.js: Version 18 or higher
• Git: For version control

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1. Backend Setup

The backend powers the AI and API services.

1. Navigate to the project root:

   cd FarmAI

2. Create a virtual environment (Recommended):

   python -m venv venv

3. Activate the virtual environment:

   • Windows:

     .\venv\Scripts\activate

   • macOS/Linux:

     source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

   Note: If you have a GPU, ensure you install the CUDA version of PyTorch manually if needed.

5. Run the Server:

   uvicorn src.api.app_v2:app --reload

   The API will be available at http://localhost:8000. You can view the API documentation at http://localhost:8000/docs.

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2. Frontend Setup

The frontend provides the interactive dashboard.

1. Navigate to the frontend directory:

   cd frontend

2. Install Node dependencies:

   npm install

3. Run the Development Server:

   npm run dev

   The application will typically run at http://localhost:5173 (check the console output).

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📂 Project Structure

FarmAI/
├── builds/                 # APK/AAB Build Artifacts
├── frontend/               # React Frontend Code
├── scripts/                # Utility Scripts
│   └── data_extraction/    # Data Processing Scripts
├── src/                    # Backend Source Code
│   ├── api/                # FastAPI Endpoints and App
│   ├── automation/         # Spray Scheduling Logic
│   ├── models/             # PyTorch and XGBoost Models
│   ├── services/           # Business Logic Services
│   └── data_sources/       # Weather & Satellite Integration
├── knowledge_base/         # Research Papers / Docs
├── requirements.txt        # Python Dependencies
├── build-apk.bat           # Android Build Script (Windows)
└── setup-android.bat       # Android Environment Setup (Windows)

🤝 Contributing

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.

📄 License

Distributed under the MIT License. See LICENSE for more information.