Precision Agriculture & Outdoor Field Robots
Crop monitoring drones, calibrated NDVI/NDRE indices, prescription maps and RTK row-following field robots.
$39
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsPrecision Agriculture & Outdoor Field Robots
Example session with this skill installed
I need a plan for a row-following vineyard robot that can handle GNSS dropouts under heavy canopy. How should I set up the sensor fusion and navigation logic?
- Read your context and instructions
- Compiled the precision agriculture
I have outlined the navigation stack for a vineyard robot, focusing on sensor fusion to maintain row alignment when RTK-GNSS signal quality degrades.
Vineyard Navigation Stack Design
| Component | Technical Specification | Fallback Logic |
|---|---|---|
| Primary Positioning | RTK-GNSS (Dual Band) + IMU | Switch to Odometry + Vision if < 6 satellites or high HDOP |
| Row Following | Stereo Vision / 2D LiDAR | Use Wheel Odometry + IMU dead reckoning for 5m intervals |
| Heading Reference | Dual-Antenna GNSS + Bosch/ST IMU | Kalman Filter fusion of IMU and Vision-based vanishing point |
| Obstacle Sensing | Ultrasonic + RGB-D Camera | Emergency stop on all motor controllers if < 1.0m proximity |
| Field Boundary | Virtual Geofence (Shapefile) | Hard-stop if centroid exits polygon coordinates |
Navigation Logic Implementation
Perception: Extract vine trunk lines or canopy edges using a Hough Transform or specialized segmentation network.
2.
Fusion: Use an Extended Kalman Filter (EKF) to fuse IMU (high frequency) with Vision/LiDAR (low frequency) for lateral offset and heading error.
3.
Control: Implement a PID or Pure Pursuit controller that prioritizes the visual centerline when GNSS accuracy exceeds 0.05m deviation.
4.
End-of-Row: Trigger headland turn sequence based on LiDAR detection of open space and confirmed GNSS coordinates.
Next steps
- Select a stereo camera (e.g., OAK-D or Intel RealSense) capable of operating in high-vibration outdoor environments.
- Define the headland turn radius based on the robot's wheelbase and implement a trajectory smoothing algorithm.
- Set up a simulation environment in Gazebo using a mock vineyard mesh to tune the EKF parameters.
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About this skill
Calibrated indices, prescriptions and RTK row-following robots.
Build agricultural and outdoor autonomy - crop monitoring drones (multispectral, NDVI/NDRE, disease and stress maps), field robots navigating rows with RTK-GNSS and vision, variable-rate prescription maps, weed detection, and robust operation in dust, mud, sun and rain. Civil use only. Use when the user works on precision agriculture, field robots, vineyard/orchard automation, or outdoor robots that must navigate unstructured terrain.
Includes an EXPERT.md reference (exact parameters, thresholds, failure diagnosis).
Acceptable use: civil and commercial applications only. Any military, paramilitary or weapon-related use is prohibited, as stated in the skill itself.
How to use
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