WEED INTELLIGENCE

See what GreenBeam sees.

GreenBeam uses computer vision and weed-recognition data to distinguish target weeds from grass, flowers, groundcover, and other non-target plants. Weed Intelligence shows the visual data, human annotation, regional context, and recognition workflow behind those decisions.

Plant-Level Recognition

First decide what the plant is.

GreenBeam evaluates candidate plants before treatment. Computer vision helps distinguish target weeds from grass, flowers, groundcover, and other non-target vegetation so treatment can remain plant-specific rather than lawn-wide.

01 · Non-target

Grass

Turf and other non-target lawn vegetation remain untreated.

Decision Leave
02 · Non-target

Flower

Flowers and ornamental plants are non-target vegetation.

Decision Leave
03 · Non-target

Groundcover

Low-growing non-target plants remain outside the treatment decision.

Decision Leave
04 · Target plant

Target weed

When the recognition system classifies the plant as a target weed, GreenBeam can proceed to the treatment decision.

Decision Target confirmed

Knowing what not to target matters just as much as finding a weed. Plant discrimination is a foundational requirement for a system designed to treat individual plants rather than broadly treat the surrounding lawn.

Explore the Technology →
GREENBEAM WEED INTELLIGENCE · UNIVERSITY OF SYDNEY

From field image to machine understanding.

GreenBeam develops weed-recognition intelligence using real plant imagery and structured annotation data. Research datasets such as Weed-AI from the University of Sydney provide valuable examples of the visual variation computer vision must understand. This explorer follows the Broadleaf Weeds in Common Couch dataset from field imagery and human annotation into a GreenBeam recognition view.

OFFICIAL DATASET View University of Sydney source ↗
DATASET Broadleaf Weeds in Common Couch
PUBLISHED 2022
CONTRIBUTOR Guy Coleman · University of Sydney
DATASET SIZE 78 images
ANNOTATIONS 567 bounding boxes
SELECTED SPECIES

Dandelion

Taraxacum officinale
75 IMAGES WITH SPECIES
533 BOXES
WEED-AI
UNIVERSITY OF SYDNEY SOURCE

Start with the real plant.

Real field imagery shows the variation a vision system must understand across plant shape, scale, turf, background, and outdoor conditions. This University of Sydney dataset contains top-down photographs of broadleaf weeds growing over common couch grass in Perth, Western Australia.

EXPLORE THE ORIGINAL IMAGES ↗
01 FIELD IMAGE Real weed imagery
→
02 HUMAN ANNOTATION Species + plant location
→
03 TRAINING SIGNAL Structured examples
→
04 GREENBEAM VISION Plant classification during inference
→
05 DECISION TARGET or LEAVE
DATASET CITATION Guy Coleman (2022). “Broadleaf Weeds in Common Couch.” In Weed-AI.
LICENSE CC BY 4.0
VIEW OFFICIAL DATASET ↗
Target lock

TARGET CONFIRMED

SOURCE IMAGE → HUMAN ANNOTATION → TRAINING DATA → GREENBEAM AI → GREENBEAM CAMERA → WEED DETECTED → TARGET CONFIRMED → PRECISION LIGHT

GREENBEAM RECOGNITION LIBRARY

Find a weed.
Explore the evidence.

Search a weed by common or scientific name to explore the imagery, geographic context, and recognition research GreenBeam brings together around that species.

GREENBEAM SPECIES VIEW

Dandelion

Taraxacum officinale
Asteraceae · Taraxacum
iNaturalist · field imagery GBIF · distribution records Weed-AI · recognition research
GreenBeam brings these evidence layers together into one species-intelligence view: what the weed looks like, where it has been recorded, and what recognition research exists for it.
— iNaturalist observations
— GBIF occurrence records
— Matched Weed-AI datasets

See the weed in the real world.

See where the species is recorded.

Distribution records add geographic context that helps GreenBeam understand where a species is relevant.

Leading record locations

A global view of where public biodiversity records for this species are concentrated.

Species identity

Dandelion · Taraxacum officinale

See the research behind recognition.

GreenBeam connects the selected species to matching Weed-AI research datasets, including dataset context, annotation type, and licensing.

TRACEABLE DATA SOURCES

iNaturalist · GBIF · Weed-AI / University of Sydney. GreenBeam keeps source attribution connected to the records organized into Weed Intelligence.

GREENBEAM LOCAL INTELLIGENCE

Your ZIP. Your weeds.
GreenBeam already knows them.

Weed species vary by geography. Enter your ZIP code and GreenBeam builds a regional weed profile with five species especially relevant to your area.

WEED INTELLIGENCE · GREENBEAM

The data behind the decision.

GreenBeam's weed-recognition intelligence brings together real plant imagery, human-created annotations, geographic plant records, and structured computer-vision datasets. Together, these evidence layers help GreenBeam understand what a plant is before deciding whether to target it or leave it alone.


Back GreenBeam on Indiegogo to support the next stage of precision weed recognition and autonomous weed control.

Dataset attribution: Weed-AI, hosted by the University of Sydney. Human annotations are not GreenBeam AI predictions.