What is agricultural robotics data collection?
Agricultural robotics data collection is the capture of field, orchard, greenhouse, and packhouse data that trains robots to weed, thin, harvest, scout, and handle produce. It combines ground-level camera and LiDAR runs, teleoperated picking demonstrations, and aerial imagery, and it has to cover every growth stage, light condition, and crop variety the robot will face.
The pull toward farm robotics is labor. The U.S. Department of Labor certified 378,000 seasonal farm jobs for H-2A guest workers in fiscal year 2023, a sign of how much field work depends on hard-to-find seasonal hands. Harvesting, weeding, and thinning are exactly the tasks growers want robots to take on.
The catch: farms change faster than any other robot environment. A row of lettuce at week two and at week six are different perception problems, and the window to record each one is short.
Where agricultural robots need better data
| Application | What the robot must learn | Data we collect | Services |
|---|---|---|---|
| Weeding and thinning | Crop vs weed at small sizes, across soil types and growth stages | Low-altitude aerial and ground RGB and multispectral passes, repeated through the season | Aerial, edge case |
| Selective harvesting | Ripeness, occluded fruit, gentle grasp and detach | Teleoperated picking with wrist cameras and force sensing, plant-level 3D scans | Teleop, multimodal, point cloud |
| Orchard and vineyard autonomy | Row following under canopy with weak GNSS | LiDAR and camera runs through rows, seasons, and weather | LiDAR, edge case |
| Field scouting and mapping | Stand counts, stress, route planning | Georeferenced aerial surveys with calibrated multispectral | Aerial |
| Packhouse handling | Sorting and packing soft, variable produce | Teleop demonstrations on the line, with bruise-sensitive grasps | Teleop |
The Season Ledger: collect by growth stage, not by calendar
Gamasome framework
A missed collection window on a factory floor costs a week. On a farm it costs a year. The Season Ledger is how we plan agricultural programs so every stage the robot will meet is captured while it exists.
You can't reschedule a harvest to suit your data plan.
Growth-stage coverage
Collection sessions are keyed to crop stages (emergence, canopy close, flowering, harvest), not to calendar dates, with a standby crew for early or late seasons.
Light and weather
Dawn, midday, overcast, and wet foliage, because harvest crews start early and weeds don't wait for sun.
Occlusion by foliage
Deliberate capture of fruit hidden behind leaves and stems, including the views where the robot must move a leaf to see.
Cultivar and field variety
Multiple varieties, farms, and soil types, so a model trained on one grower's field doesn't fail on the neighbor's.
Terrain and GNSS
Slopes, mud, rutted rows, and canopy that blocks satellite signals, recorded as conditions, not avoided.
What a harvesting data program looks like
Composite scenario: greenhouse tomato harvesting (details generalized)
- Situation
- A harvesting robot team has a strong detector for ripe tomatoes but low pick success on the actual vines.
- Problem
- Their training data came from one greenhouse in a single month. The robot rarely saw trusses hidden behind leaves, and it squeezed fruit too hard on detach.
- Solution
- Teleoperated picking sessions in two greenhouses across the season, with wrist force sensing, plant-level 3D scans, and every episode tagged by occlusion level and ripeness.
- Outcome
- A dataset that teaches both where the fruit is and how hard to hold it, plus a held-out greenhouse to show the policy transfers.
For a grower evaluating the robot, that last step is the one that matters. A picker that only works in the vendor's demo greenhouse isn't a product yet.
Common mistakes in agricultural robot data
One farm, one variety
Models learn the field, not the crop. Plan at least two sites and varieties from the first season.
Midday-only capture
Harvest and weeding happen at dawn. Flat noon light hides the shadows and glare your robot will see.
Inconsistent ripeness labels
If two annotators disagree on ripe vs almost-ripe, the model will too. We lock a visual ripeness guide before labeling.
Flying too high for small weeds
Early-stage weeds are tiny. Set pixels on target first, as in our aerial data collection contract.
Labels for ripeness, occlusion, and plant parts come from our data annotation team working against the same season plan.
Agricultural robotics data FAQs
What is agricultural robotics data collection?
It's the capture of field, greenhouse, orchard, and packhouse data that trains robots to weed, harvest, scout, and handle produce, using ground cameras, LiDAR, teleoperated picking demonstrations, and aerial imagery across the growing season.
How many seasons of data does an agricultural robot need?
Most teams need data from every growth stage the robot will work in, across more than one site and variety. A first season can cover this if collection is scheduled by crop stage and includes at least two farms.
Can you collect harvesting demonstrations for robot learning?
Yes. We run teleoperated picking sessions on your robot or ours, with wrist cameras and force sensing, and tag every episode by ripeness, occlusion, and outcome.
Do you use drones for agricultural data?
Yes, for field mapping, stand counts, and weed detection, with multispectral calibration and flight altitudes set by the pixels your model needs on target.
How do you handle weak GPS under orchard canopy?
We record GNSS quality alongside LiDAR, camera, and IMU data so localization models can be trained and evaluated in the conditions where satellite positioning degrades.
Which crops have you worked with?
Capture methods apply across row crops, orchards, vineyards, and greenhouses. We scope crop-specific details, such as growth stages and ripeness guides, during the pilot.