Industry: Agritech

Data for smarter, greener farming.

Crop, field and sensor data annotated for precision agriculture — from disease detection to yield estimation and autonomous machinery.

carpedestriansignal
350+
Global clients
25+
Languages
4-stage
QA workflow
98%
Post-QA accuracy
How we help

Data for precision agriculture

Crop, field and sensor data labeled for the models modern farming runs on.

Crop & Plant

  • Disease & pest labels
  • Growth-stage annotation
  • Weed detection
  • Species classification

Field Segmentation

  • Field & plot boundaries
  • Land-cover masks
  • Canopy & soil
  • Aerial & drone imagery

3D & Sensor

  • LiDAR & depth
  • Machinery perception
  • Sensor fusion
  • Row & obstacle labels
See LiDAR

Yield & Analytics

  • Counting & density
  • Yield estimation labels
  • Time-series data
  • Anomaly tagging

Collection

  • Drone & ground capture
  • Seasonal coverage
  • Region diversity
  • Consent & access
See collection

Validation

  • Agronomy SME review
  • Gold-set QA
  • Coverage checks
  • Audit reports
See validation
Why Graveiens

Why teams choose Graveiens

Compliance-first delivery and a pay-on-approval model that de-risks every engagement.

Domain awareness

Labeling tuned for crops, fields and machinery.

Region diversity

Contributors across geographies and seasons.

Consistent QA

Four-stage workflow with gold sets.

Pay on approval

Invoiced only for approved deliverables.

FAQ

Questions, answered

Which agritech tasks do you cover?
Crop and disease labeling, field segmentation, machinery perception, and yield analytics from drone, ground and satellite data.
Can you capture seasonal data?
Yes — collection across seasons and regions to reflect real growing conditions.
Do you support autonomous machinery?
Yes — 3D, obstacle and row labeling for perception in autonomous farm equipment.
How do we start?
A small paid pilot on a representative field or crop set.

Related services

Grow your agritech AI

Send us a sample task. You only pay for deliverables you approve.

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