Carbon Robotics partners with iMerit to power instant in-field AI customization

partners with iMerit to power instant in-field AI customization">

Carbon AI is trained on 150 million labeled plants, enabling farmers to start laser weeding any field or crop in minutes. | Credit: Carbon Robotics

Carbon Robotics is bringing the era of foundation models to the farm. The company has replaced its collection of crop‑specific AI vision models with a single “large plant model” that understands plant structure across regions and species, allowing farmers to instantly tune Carbon’s laser weeder to their own definition of crops and weeds.

Unlike traditional vision systems that require time‑consuming retraining whenever a new crop, weed, or geography is introduced, Carbon said its large plant model is pre‑trained on millions of images of baby plants collected worldwide and then personalized in the field. Using an iPad app, farmers can simply review thumbnails from their own fields and tag a small number as “crop” or “weed.”

With the tool and the new foundation model, Carbon Robotics said farmers can customize their use of the its autonomous weeder to “zap” the specific plants that they want removed from their field. Even if those plants are desirable plants in another region or part of the farm, the grower can customize the intended target of the laser for each deployment.

Those examples are fed into the foundation model , which immediately adjusts its behavior—without rolling new models or downloading new software. The same global model can selectively zap weeds in a carrot field in Arizona, then move to lettuce or herbs on another farm with only minutes of configuration.

When the LaserWeeder kills weeds, the nutrients from the weed go back into the soil to fertilize the plant. | Source: Carbon Robotics

Alex Sergeev, chief technology officer at Carbon Robotics, recently spoke with The Robot Report about the company ‘s capabilities.

“What we had to do was to build a process that lets farmers give model examples, and that model doesn’t need time to retrain, just instantaneously work,” he said. “The model can understand differences. So it uses examples, and it’s able to compare those examples to plants that we see in the field at very fast speed. It’s fundamentally different principles.”

Sergeev continued, “The main thing that model needs to output instead of saying, ‘Here’s my confidence that this is a weed’ and ‘Here’s my confidence that this is a crop,’ it gives you a way to do comparison between this plant and all the plants that the farmer told you ‘These are crops’ and ‘These are weeds.’ That’s the difference, and that needs to happen really fast.”