Automated plant root inoculation system combining computer vision, deep learning, and robotics for precise root tip targeting.
Quick Start • Documentation • Features • Development • Performance
Plant Inoculation Robot is a solution to automate the process of inoculating plant root tips with high precision. The system integrates computer vision algorithms, deep learning models, and robotic control systems to achieve sub-millimeter accuracy in root tip targeting.
graph TB
subgraph "Input Layer"
A[Plant Image] --> B[Petri Dish Detection]
B --> C[Image Preprocessing]
end
subgraph "Computer Vision Pipeline"
C --> D[Plant Segmentation]
D --> E[Root Analysis]
E --> F[Tip Detection]
F --> G[Coordinate Extraction]
end
subgraph "Control System"
G --> H[PID Controller]
G --> I[RL Agent]
H --> J[Hybrid Controller]
I --> J
J --> K[Robot Control]
end
subgraph "Output Layer"
K --> L[Precise Positioning]
L --> M[Inoculation]
end
style A fill:#e1f5fe
style M fill:#c8e6c9
style J fill:#fff3e0
# Clone the repository
git clone https://github.com/your-org/plant-inoculation-ai.git
cd plant-inoculation-ai
# Install with Poetry (recommended)
poetry install
# For GPU acceleration
poetry install --extras gpu
# For development environment
poetry install --extras allimport numpy as np
from plant_inoculation_ai import CVPipeline, PIDController
# Initialize computer vision pipeline
pipeline = CVPipeline(
patch_size=960,
model_weights_path="path/to/model.weights.h5"
)
# Process plant image and extract root tips
image_path = "plant_image.jpg"
results = pipeline.process_image(image_path)
root_tips = pipeline.get_root_tips_for_robotics(results)
# Initialize precision controller
controller = PIDController(
kp=[1.0, 1.0, 1.0], # Proportional gains [x, y, z]
ki=[0.1, 0.1, 0.1], # Integral gains
kd=[0.05, 0.05, 0.05] # Derivative gains
)
# Execute precise targeting
for target in root_tips:
current_pos = np.array([0.0, 0.0, 0.15])
target_pos = np.array([target['pixel_x'], target['pixel_y'], 0.2])
# Compute control action
action = controller.compute(current_pos, target_pos, dt=1/240)
# Apply to robot (implement your interface)
robot.move(action)flowchart LR
subgraph "Image Processing"
A[Raw Image] --> B[Petri Dish Detection]
B --> C[Plant Segmentation]
C --> D[Root Analysis]
end
subgraph "Deep Learning"
D --> E[U-Net Model]
E --> F[Root Mask]
F --> G[Tip Detection]
end
subgraph "Output"
G --> H[Target Coordinates]
H --> I[Robot Control]
end
style A fill:#e3f2fd
style I fill:#e8f5e8
| Component | Technology | Performance |
|---|---|---|
| Petri Dish Detection | OpenCV Contour Analysis | ~50ms per image |
| Plant Segmentation | Traditional CV + Morphology | ~100ms per image |
| Root Analysis | U-Net + MobileNet | ~200ms per patch |
| Tip Detection | Skeletonization + Pathfinding | ~100ms per image |
| End-to-End | Optimized Pipeline | ~500ms per plant |
- PID Controllers: Anti-windup protection with output saturation
- Reinforcement Learning: SAC algorithm for adaptive control
- Hybrid Control: Seamless PID + RL blending
- Precision Targeting: Sub-millimeter accuracy (±0.1mm)
- Real-time Response: <1ms controller latency
graph TD
subgraph "Model Architecture"
A[Input Image] --> B[Encoder: MobileNet]
B --> C[Decoder: U-Net]
C --> D[Output Mask]
end
subgraph "Training Process"
E[Training Data] --> F[Data Augmentation]
F --> G[Model Training]
G --> H[Validation]
H --> I[Model Save]
end
subgraph "Inference"
J[New Image] --> K[Preprocessing]
K --> L[Model Inference]
L --> M[Post-processing]
M --> N[Root Tips]
end
style A fill:#e1f5fe
style N fill:#c8e6c9
- Custom U-Net: MobileNet backbone with transfer learning
- Multi-loss Optimization: Dice + Focal + Tversky + Boundary losses
- GPU Acceleration: Mixed precision training support
- Performance Monitoring: Real-time metrics and visualization
src/plant_inoculation_ai/
├── computer_vision/ # CV pipeline and analysis
│ ├── petri_dish.py # Petri dish detection
│ ├── segmentation.py # Plant segmentation
│ └── root_analysis.py # Root architecture analysis
├── models/ # Deep learning models
│ └── unet.py # U-Net implementation
├── robotics/ # Robot control systems
│ └── controllers/ # PID and RL controllers
│ └── pid.py # PID controller implementation
├── pipeline/ # End-to-end workflows
│ └── cv_pipeline.py # Integrated CV pipeline
├── data/ # Data loading utilities
├── utils/ # Helper functions
└── core/ # Core configurations
| Operation | Time (ms) | Hardware | Notes |
|---|---|---|---|
| Petri Dish Detection | 50 | CPU | OpenCV optimized |
| U-Net Inference | 200 | GPU (RTX 3080) | 960×960 patch |
| Root Analysis | 100 | CPU | Skeletonization |
| End-to-End Pipeline | 500 | Mixed | Full plant processing |
graph LR
subgraph "Control Metrics"
A[Controller Latency] --> B[<1ms]
C[RL Inference] --> D[~5ms]
E[Positioning Accuracy] --> F[±0.1mm]
G[Response Time] --> H[<10ms]
end
subgraph "Hardware Requirements"
I[GPU Acceleration] --> J[Real-time]
K[Calibrated System] --> L[High Precision]
end
style B fill:#c8e6c9
style F fill:#c8e6c9
| Metric | Value | Conditions |
|---|---|---|
| Controller Latency | <1ms | Real-time loop |
| RL Inference | ~5ms | GPU acceleration |
| Positioning Accuracy | ±0.1mm | Calibrated system |
| Response Time | <10ms | Target acquisition |
# Build development environment
docker build -t plant-inoculation-ai:dev .
# Build production image
docker build --target production -t plant-inoculation-ai:prod .
# Run with GPU support
docker run --gpus all -it plant-inoculation-ai:dev
# Run Jupyter environment
docker run --gpus all -p 8888:8888 plant-inoculation-ai:dev jupyter labgraph TB
subgraph "Infrastructure"
A[Load Balancer] --> B[Application Servers]
B --> C[GPU Clusters]
C --> D[Database]
end
subgraph "Security"
E[Non-root Containers] --> F[Dependency Scanning]
F --> G[Health Checks]
end
subgraph "Monitoring"
H[Performance Metrics] --> I[Alerting]
I --> J[Logging]
end
style A fill:#e3f2fd
style G fill:#fff3e0
The package is designed for seamless cloud deployment with:
- Security: Non-root containers, dependency scanning
- Scalability: Multi-GPU support, load balancing ready
- Monitoring: Health checks, performance metrics
- CI/CD: Automated testing and deployment pipelines
# Install development dependencies
poetry install --with dev
# Setup pre-commit hooks
poetry run pre-commit install
# Run code quality checks
poetry run black src/ tests/
poetry run isort src/ tests/
poetry run flake8 src/ tests/
poetry run mypy src/
poetry run bandit -r src/# Run comprehensive test suite
poetry run pytest
# Generate coverage report
poetry run pytest --cov=src --cov-report=html
# Run specific test categories
poetry run pytest -m "unit" # Unit tests
poetry run pytest -m "integration" # Integration tests
poetry run pytest -m "not slow" # Skip slow tests# Build Sphinx documentation
cd docs && poetry run sphinx-build -b html . _build/html
# Serve documentation locally
python -m http.server 8000 --directory _build/htmlfrom plant_inoculation_ai.utils.gpu_utils import print_gpu_summary
# Comprehensive GPU information
print_gpu_summary()
# Get recommended configuration
config = get_recommended_gpu_config()
print(f"Recommended batch size: {config['batch_size_multiplier']}x")from plant_inoculation_ai.utils.gpu_utils import (
configure_tensorflow_gpu,
set_tensorflow_mixed_precision
)
# Configure TensorFlow GPU
configure_tensorflow_gpu(memory_growth=True)
# Enable mixed precision for faster training
set_tensorflow_mixed_precision(enabled=True)This project is licensed under the MIT License - see the LICENSE file for details.