Plant Inoculation AI System 🌱🤖

Root System Analysis

Opentrons OT-2 Robot

Simulation Environment

Automated system combining root image analysis and robotic precision for targeted plant inoculation.

---

🎯 Overview

An advanced automated system that combines state-of-the-art computer vision and robotic control for precise plant root analysis and targeted inoculation.

🌟 Key Features

• 🔬 Deep learning-based root segmentation
• 🎯 Instance segmentation for individual plant identification
• 🌿 Root System Architecture (RSA) extraction
• 🤖 Automated robotic control for precise inoculation
• 🔗 Integration with Opentrons OT-2 liquid handling robot

💫 Project Description

The system seamlessly processes images from NPEC's Hades system, capable of handling:

• 📊 Up to 10,000 seedlings
• 🔬 Over 2,000 Petri dishes
• 🔄 Automated processing pipeline
• 🎯 Precise inoculation targeting

🛠️ System Components

1. Computer Vision Pipeline 👁️

graph LR
    A[Image Input] --> B[Preprocessing]
    B --> C[Segmentation]
    C --> D[Root Analysis]
    D --> E[Measurements]

Loading

• 📸 Image preprocessing and Petri dish detection
• 🎯 Plant instance segmentation
• 🧠 Deep learning model for root segmentation
• 🌿 Root System Architecture extraction
• 📏 Primary root length measurement

2. Robotic Control System 🤖

graph LR
    A[Vision Output] --> B[Path Planning]
    B --> C[Control System]
    C --> D[Robot Execution]

Loading

• 💻 Opentrons OT-2 simulation environment
• 🎮 Gymnasium-compatible environment wrapper
• 🧠 Reinforcement Learning (RL) controller
• ⚙️ PID controller implementation
• 🔗 Vision-robotics integration

🚀 Getting Started

Prerequisites

Requirement	Version
Python	3.8+
PyTorch	Latest
OpenCV	Latest
PyBullet	Latest
Gymnasium	Latest
Stable-Baselines3	Latest

⚡ Quick Start

1. Clone the repository:

   git clone https://github.com/yourusername/plant-inoculation-ai.git
   cd plant-inoculation-ai

2. Install dependencies:

   pip install -r requirements.txt

3. Run the setup script:

   python setup.py install

📚 Usage Examples

🖼️ Computer Vision Pipeline

# Process a batch of images
from plant_inoculation import vision

# Initialize processor
processor = vision.ImageProcessor()

# Process images
results = processor.process_batch(
    input_dir="path/to/images",
    output_dir="path/to/output"
)

🤖 Robotics Control

# Run the robotic controller
from plant_inoculation import robotics

# Initialize controller
controller = robotics.Controller(type="RL")

# Execute movement
controller.move_to_target([x, y, z])

📁 Project Structure

📦 plant-inoculation-ai
 ┣ 📂 computer_vision/
 ┃ ┣ 📂 preprocessing/
 ┃ ┣ 📂 segmentation/
 ┃ ┣ 📂 root_analysis/
 ┃ ┗ 📂 models/
 ┣ 📂 robotics/
 ┃ ┣ 📂 simulation/
 ┃ ┣ 📂 controllers/
 ┃ ┗ 📂 integration/
 ┣ 📂 data/
 ┃ ┣ 📂 raw/
 ┃ ┣ 📂 processed/
 ┃ ┗ 📂 models/
 ┗ 📂 utils/

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

📄 License

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

✨ Acknowledgments

• 🏢 Netherlands Plant Eco-phenotyping Centre (NPEC)
• 👥 Project supervisors and mentors
• 🌟 Contributors and researchers

📞 Contact

For questions and support:

• 📧 Email: [email protected]
• 💬 Discord: Join our community
• 🌐 Website: project-website.com

---

_{Built with ❤️ by the Plant Inoculation AI Team}