Green-on-Green Detection

Green-on-Green (GoG) detection enables the OWL to identify weeds growing within crops, where traditional colour-based detection cannot distinguish weed from crop.

Overview

GoG uses YOLO object detection models to identify weeds directly, regardless of background. Two inference backends are supported:

Backend	Format	Best for
NCNN (recommended)	Directory with .param + .bin files	Production on Raspberry Pi — fast, no GPU needed
PyTorch	Single .pt file	Development and training

Models are loaded automatically from the models/ directory. The OWL auto-detects the model format.

Hardware Requirements

The OWL runs YOLO inference natively on the Raspberry Pi via NCNN. The Google Coral USB Accelerator that earlier documentation referenced is no longer recommended — it has been superseded by the Raspberry Pi AI Hat+, which is the current recommended accelerator for in-field Green-on-Green deployments.

Recommended: Raspberry Pi AI Hat+. The Waveshare variant is widely available — Waveshare AI Hat+ — and other Hailo-based AI Hat+ variants from different vendors work the same way. Higher-compute variants cost more but let you run larger inference image sizes, which is worth it if you need higher accuracy on smaller targets.

Without an accelerator: the Pi can run smaller NCNN models on CPU only. That works for testing and low-resolution inference, but the AI Hat+ is what unlocks larger image sizes and the frame rates needed for boom-speed operation.

Detection Modes

OWL supports two GoG detection strategies, selected by the algorithm config key:

gog — Direct detection

The YOLO model detects weeds directly. Each detection bounding box is mapped to a relay lane for actuation. Use this when your model is trained specifically on weeds.

gog-hybrid — Crop masking + colour detection

The YOLO model identifies and masks crop regions. The remaining (non-crop) areas are then processed with the ExHSV colour algorithm to find weeds. Use this when you have a crop model but not a weed-specific model.

Actuation Modes

How YOLO detections are mapped to relay lanes:

centre (default)

The horizontal centre of each bounding box determines which relay lane fires. Works with both detection and segmentation models.

zone

Uses the segmentation mask to count pixels in each relay lane. Only fires if the pixel count exceeds min_detection_pixels. Requires a segmentation model (trained with -seg suffix).

Set the mode with the actuation_mode key in [GreenOnGreen].

Model Management

Placing models manually

Copy model files to ~/owl/models/ on the Raspberry Pi:

• NCNN: A directory containing a .param and .bin file pair

• PyTorch: A single .pt file

# Example: copy an NCNN model
scp -r my_model_ncnn/ owl@owl.local:~/owl/models/

Upload and deploy via dashboard

The networked controller includes a model management page for uploading and deploying models wirelessly:

1. Open the controller’s /models page from a laptop browser

2. Upload a .pt file or .zip containing NCNN .param + .bin files

3. Select target OWL devices

4. Click Deploy — the model is transferred to each OWL over the network

Hot-swap via AI tab

The dashboard AI tab lets you switch between models without restarting:

1. Open the AI tab in the dashboard

2. Select a model from the dropdown — shows model type (detection/segmentation), format, and size

3. Filter which classes to detect using the class button grid

4. Click Apply to hot-swap the model

Weed Tracking

When tracking_enabled = True in the [Tracking] config section, OWL uses ByteTrack to assign persistent IDs to detected weeds across frames. This provides:

• Class smoothing: Predictions are averaged over a sliding window (track_class_window frames) to reduce class flickering

• Crop mask stabilisation: In gog-hybrid mode, crop mask regions persist for track_crop_persist frames after a crop detection leaves the frame, preventing false positives at crop edges

Tracking adds minimal overhead and is recommended for GoG deployments.

Training Your Own Model

1. Collect images of weeds in your specific field conditions — lighting, soil type, growth stage, and crop all matter

2. Annotate images using Roboflow or Label Studio to draw bounding boxes (or polygons for segmentation) around weeds

3. Train with Ultralytics YOLO:

   pip install ultralytics
   yolo detect train data=your_dataset.yaml model=yolov8n.pt epochs=100 imgsz=320
   

4. Export to NCNN for deployment on Raspberry Pi:

   yolo export model=best.pt format=ncnn imgsz=320
   

   This creates a directory with .param and .bin files ready to copy to ~/owl/models/.

Check Weed-AI for existing annotated weed image datasets that may be relevant for your conditions.

Configuration

GreenOnGreen parameters

Parameter	Default	Description
model_path	models	Directory containing YOLO model files.
confidence	0.5	Minimum confidence threshold (0.0-1.0).
detect_classes	(empty)	Comma-separated class IDs to detect. Empty = all classes.
actuation_mode	centre	Relay mapping: centre (box centre) or zone (mask pixel count).
min_detection_pixels	50	Minimum mask pixels for zone mode actuation.
inference_resolution	320	YOLO input image size. Smaller = faster, larger = more accurate.
crop_buffer_px	20	Pixel buffer around crop mask regions (gog-hybrid).

Tracking parameters

Parameter	Default	Description
tracking_enabled	False	Enable ByteTrack multi-object tracking.
track_class_window	5	Frames to smooth class predictions over.
track_crop_persist	3	Frames a crop mask persists after detection leaves.

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Next Steps

• Configuration Guide - Full parameter reference

• AI Assistant - Control detection settings through natural language

• Weed-AI - Access training datasets