Carbon Robotics unveils Large Plant Model trained on 150M+ plants to transform farming

Carbon Robotics built an AI model that detects and identifies plants | TechCrunch

What is and isn't a weed that needs to be eliminated in the field is determined by the eyes of the farmer -- and now, increasingly, by a new AI model from Carbon Robotics. Seattle-based Carbon Robotics, which builds the LaserWeeder -- a robot fleet that uses lasers to kill weeds -- announced a new AI model, the Large Plant Model (LPM), on Monday. This model recognizes plant species instantly and allows farmers to target new weeds without needing to retrain the robots. The LPM is trained on more than 150 million photos and data points collected by the company's machines across the more than 100 farms in 15 countries where the robots currently operate. The model now powers Carbon AI, the AI system that serves as the brains inside the company's autonomous weed-killing robots. Paul Mikesell, the founder and CEO of Carbon Robotics, told TechCrunch that prior to LPM, every time a new type of weed would show up on a farm -- or even the same type of weed in different soil or with a slightly different appearance -- the company would have to create new data labels to retrain its machines to recognize the plant. This process took about 24 hours each time, Mikesell said. Now, LPM can learn a new weed instantly, even if it's never seen it before. "The farmer can live in real time and say, 'Hey, this is a new weed. I want you to kill this,' and that was something that had never been done before," Mikesell said. "There's no new labeling or retraining because the Large Plant Model understands, at a much deeper level, what it's looking at and the type of plant." Mikesell said that the company, which was founded in 2018, started developing this model shortly after it began shipping its first machines in 2022. Mikesell has years of experience building these types of neural networks from previous roles at Uber and working on Meta's Oculus virtual reality headsets. This new model will reach the company's existing systems through a software update. From there, farmers can tell the machine what to kill and what to protect by selecting photos that the machine has collected in the robot's user interface. Carbon Robotics has raised more than $185 million in venture capital from backers including Nvidia NVentures, Bond, and Anthos Capital, among others. Now, the company will look to continue to fine-tune the model as the machines continue to feed the LPM new data. "We have over 150 million labeled plants now in our training set," Mikesell said. "We have enough data now that we should be able to look at any picture and decide what kind of plant that is, what species it is, what it's related to, what its structure is like, without having ever even seen that particular plant before, because we have so much data going into the neural net."

World's first Large Plant Model trained on 150 million plants unveiled

The new model powers its LaserWeeder robots, allowing them to identify and laser-weed virtually any crop or field within minutes. Continuously learning from data gathered by a global fleet of machines, the system adapts in real time. The Seattle-based company says the breakthrough could help farmers cut labor costs, reduce herbicide use, and improve yields. The LPM is designed to continuously improve through real-world data collected by Carbon Robotics' global fleet of LaserWeeder machines. Each deployment feeds new plant imagery back into the system, strengthening its ability to detect and classify plants across diverse environments. This ongoing feedback loop, described by the company as a compounding data effect, enables performance improvements to be shared across the entire fleet rather than remaining isolated to individual machines, according to Quantum News. Rather than functioning as a static model, the LPM serves as the core of Carbon AI, a broader decision-making framework that supports both the LaserWeeder platform and the company's Autonomous Tractor Kit (ATK). Together, these systems are intended to help farmers reduce dependence on manual labor and chemical herbicides while maintaining or improving crop yields.