Multi-spectral cameras and deep learning identify 40+ weed species at 30 frames per second — in daylight, at night, and without an internet connection.
WeedBot Pro doesn't rely on a single camera. Its sensor array combines visible light with near-infrared wavelengths to see what the human eye cannot.
Three high-resolution RGB cameras capture detailed colour images from multiple angles, building a composite view of the ground surface at sub-millimetre resolution. Each camera covers a 120-degree field of view.
A dedicated NIR sensor detects reflected infrared light between 700-1000nm. Chlorophyll in living plants reflects NIR strongly, making it trivial to distinguish green weeds from soil, stubble, and dead organic matter — even when colour alone is ambiguous.
Integrated NIR LED arrays flood the target area with invisible light, enabling consistent imaging in complete darkness. Night operation actually improves accuracy by eliminating variable shadows, sun glare, and overexposed highlights.
The brain behind WeedBot is a convolutional neural network purpose-built for Australian conditions.
Our CNN was trained on a dataset of over 2 million annotated field images collected across every major agricultural region in Australia — from the Darling Downs to the Western Australian wheatbelt, Tasmania's vegetable country to the northern Queensland sugar cane belt. The dataset captures weeds at every growth stage, in every soil type, under every lighting condition.
WeedBot doesn't just distinguish "weed" from "crop." It identifies individual species — annual ryegrass, blackberry nightshade, wild radish, fleabane, common marshmallow, Paterson's curse, sowthistle, fat hen, and dozens more. This matters because different species warrant different treatment intensities. A small fleabane seedling needs a fraction of the herbicide required for an established marshmallow plant.
All inference happens on-board using an NVIDIA Jetson edge computing module. Images are captured, processed, classified, and treatment decisions executed in under 33 milliseconds — without sending a single byte to the cloud. This is critical for Australian farms where reliable cellular coverage is rare. The robot operates with full autonomy from the moment it enters the paddock.
Every deployment generates new labelled data. When WeedBot encounters a plant it's uncertain about, the image is flagged for agronomist review. Confirmed identifications feed back into the training pipeline, and updated models are pushed to the fleet during scheduled maintenance windows. The system gets smarter with every hectare it covers.
How does AI-powered real-time detection stack up against traditional approaches?
| Criteria | Manual Scouting | Drone Mapping | WeedBot Real-Time |
|---|---|---|---|
| Detection Speed | 2-5 ha/hour walking | 100+ ha/hour (imaging only) | 40-60 ha/day (detect + treat) |
| Species Identification | Depends on scout expertise | Limited by altitude (5-20 species) | 40+ species, sub-cm resolution |
| Night Operation | Not practical | Not available | Full capability via NIR |
| Immediate Treatment | No — requires follow-up pass | No — data must be processed | Yes — detect and treat in one pass |
| Internet Required | No | Yes (for image upload/processing) | No — all processing on-board |
| Consistency | Variable (fatigue, weather) | High (but post-processing lag) | Constant — no fatigue factor |
| Cost per Hectare | $35-60 (labour only) | $8-15 (imaging) + treatment | $5-12 all-inclusive |
"We trialled WeedBot on a 400-hectare fallow paddock. It picked up fleabane rosettes that our scout walked straight past — plants barely 2cm across, hidden under stubble. By the time a drone map is processed, those weeds have already set seed."
— Grain grower, Central West NSWWeedBot Pro's neural network currently identifies 40+ weed species common in Australian broadacre, horticulture, and viticulture. The model is trained on over 2 million annotated field images and is continuously updated — new species are added as field data accumulates from deployments across the country.
Yes. WeedBot uses near-infrared illumination that is invisible to the human eye but provides consistent, shadow-free lighting for the camera system. Night operation often delivers higher accuracy than daytime because variable sunlight, shadows, and glare are eliminated entirely.
WeedBot processes every image on-board using an NVIDIA Jetson edge computing module. No cloud connection is required for detection, classification, or treatment. Weed maps and treatment logs are stored locally and sync automatically to your farm dashboard when the robot is back in range of your home network or cellular signal.