Carbon Robotics is revolutionizing agriculture with the LaserWeeder, which uses artificial intelligence, deep learning, robotics, and lasers to identify and eliminate weeds with sub-millimeter accuracy.
LaserWeeder by Carbon Robotics
Types of crops
Vegetables
Commercialization & Business model
TRL : 10: Market introduction - Full commercial operation
Number of units in service : 151 - 200
Price : over-500K
Business model : direct-sales
Technical specifications
Robot’s primary need : The LaserWeeder addresses the persistent challenge of weed management in farming without disturbing crops or soil, relying on chemicals, or depending on manual labor.
Power source : LaserWeeder generates power from a front power take off that's mounted to a tractor.
Horse power : Tractor horsepower requirements range based on LaserWeeder size, ranging from 110 - 145 hp
Size : Smallest Machine: 2.79 m (W) x 2.15 m (D) x 3.22m (H) Largest Machine: 6.38 m (W) × 2.16 m (D) × 3.43 m (H) Four size options available
Net weight : Smallest Machine: 1,996 kg Largest Machine: 3,266 kg
Functionality : LaserWeeder G2 uses AI, computer vision, and lasers to identify and eliminate weeds with sub-millimeter precision, without disturbing the soil or using chemicals.
Embedded technologies : High-resolution cameras and a 100% liquid-cooled system ensure precise visualization and dependable operation in all conditions. Advanced computer vision and deep learning models identify crops and weeds in real time, while high-powered lasers eliminate weeds with sub-millimeter accuracy. Integrated GPS and navigation systems enable precise field operation, and Starlink high-speed connectivity supports fast software updates and seamless image uploads.
Tools : LaseWeeder is a comprehensive weeding solution that is operable by any brand of tractor.
Connections : The LaserWeeder is an implement that attaches to the back of a tractor via 3-point hitch. Each unit is equipped with its own GPS and Starlink systems for navigation and connectivity.
Development phase
Next development phase : We are continually advancing software, deep learning models, and hardware systems to improve efficiency, precision, and reliability in the field.