Dan Crummett

Large Plant Model Allows LaserWeeder Operators to Update ‘Targets’ with a Thumb Tap

Carbon Robotics machines update global systems each time they go to the field, boosting data in the machine-learning library which currently holds images of 150 million plants

TAKEAWAYS

  • Artificial intelligence is boosting the capability of laser weeding technology.
  • Farmers using Carbon Robotics’ large plant model (LPM) technology can identify and enter plant into the LaserWeeder system for future identification.
  • Robots that can immediately understand a plant in the field and adapt in real time provide maximum value.

Carbon Robotics recently introduced its AI-driven Large Plant Model (LPM), which includes 150 million labeled plants to enable growers to start using the company’s popular LaserWeeder in any field or crop in minutes. 

The LPM, and its AI architecture, is also destined to add precision and convenience to CR’s new Autonomous Tractor Kit (ATK), a retrofit kit designed to enable autonomous operation on existing tractors. Company officials also say the LPM will be used to guide robotic equipment in CR’s forth-coming project introducing AI-trained robotics across a broad spectrum of farm-related operations. 

Paul Mikesell, founder and CEO of Seattle-based Carbon Robotics, says LaserWeeders operating in 15 countries since 2022 have been cataloging various plant/weed species with each field pass, amassing the “world’s largest and most diverse” agricultural dataset.

“As the global LaserWeeder fleet operates daily, the system continuously ingests real-world data in a compounding fashion, and with our customers’ knowledge uploads that data to strengthen the model exponentially and improve LaserWeeders worldwide,” Mikesell explains. “When our robots can understand any plant in any field immediately and adapt behavior in real time, farmers immediately get maximum value from the machines.”

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HIGH ENERGY WEEDING — The LaserWeeder by Carbon Robotics is now

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Dan Crummett

Dan Crummett has more than 40 years in regional and national agricultural journalism including editing state farm magazines, web-based machinery reporting and has a long-term interest in no-till and conservation tillage. He holds B.S. and M.S. degrees from Oklahoma State University.

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