🔧 Core Components

1. High-Resolution Camera Arrays
   
   • Capture real-time images of the field, covering every inch as the robot moves.
   • Often include RGB and infrared cameras for better contrast between crops and weeds.
2. Computer Vision + AI/ML Models
   
   • Pre-trained deep learning models (e.g., convolutional neural networks) identify plant species at the leaf level.
   • The system distinguishes between crops and weeds with ~95%+ accuracy, even in dense planting environments.
3. Precision Laser System
   
   • Typically uses high-energy CO₂ or fiber lasers (around 150W–300W).
   • Mounted on robotic gantries or articulated arms to aim at individual weeds.
   • Delivers short bursts of intense heat (over 1000°C) to rupture plant cells and kill the meristem, the growing point of the weed.
4. Real-Time Targeting System
   
   • Based on GPS + inertial navigation for macro location and robotic micro-adjustments for laser targeting.
   • Laser firing is triggered only when the system is confident in weed classification.
5. Autonomous Navigation
   
   • The machine moves through rows using GPS, LiDAR, or RTK guidance, operating day or night.

⚙️ How It Works

1. Scan: Cameras capture continuous field imagery.
2. Classify: AI model distinguishes weeds from crops using shape, color, and texture.
3. Target: Onboard computing calculates weed position relative to the laser.
4. Fire: The laser pulses heat energy onto the weed, destroying its tissue.
5. Move: The robot advances and repeats the process, adjusting for weed density and crop spacing.

🔬 Key Technical Advantages

• No herbicides: Completely non-chemical.
• Selective: Targets only weeds, preserving soil health and crop integrity.
• Data capture: Logs every weed hit and creates real-time field maps for later analysis.‍
• Scalable: Works across row crops like lettuce, onions, and carrots, with some systems processing over 100,000 plants per hour.

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