Agritech Computer Vision

Field-grade image annotation for agriculture AI.

From drone and rover imagery to in-row cameras, your models are only as good as the labels behind them. Labelix runs dedicated, in-office annotation pods that label crops, weeds, fruit, disease and terrain — consistently, at the volume agritech demands.

Read the FAQ
  • Independent & neutral
  • In-office · NDA-bound
  • Live in ~3 weeks
  • Never crowdsourced
What we label

The annotation your models actually need.

Crop & plant classification
Weed vs. crop segmentation
Fruit & yield counting
Disease & stress detection
Row & terrain segmentation
Bounding boxes & polygons
Why it's hard

The bottleneck isn't the model. It's the labels behind it.

01

Every new crop, region or season resets accuracy — and needs a fresh labeled set, fast.

02

Agronomy labels demand consistency across millions of frames a generalist crowd can't hold.

03

Your field imagery is proprietary — it shouldn't be scattered across anonymous gig workers.

Why Labelix

A dedicated team — not a crowd you can't see.

Autonomous weeding & harvesting robots · crop-health & scouting platforms · yield estimation · precision-spray systems.

A dedicated pod, live in ~3 weeks

We recruit and train a dedicated, in-office team for your domain and ramp it under daily QA — not a rotating, anonymous crowd. A small paid pilot proves quality before you scale.

Independent & data-firewalled

No Big-Tech owner, no conflicted incumbent. Your data is handled by vetted staff under signed NDAs in a controlled, access-controlled environment — never farmed out.

Consistency that compounds

The same retained team learns your taxonomy and edge cases, so each new product line, region, template or language is a re-train — not a restart.

FAQ

Questions, answered straight.

Still have one? Tell us about your data and we'll scope a small paid pilot.

Drone, satellite, rover and in-row camera imagery — for crop and weed classification, segmentation, fruit and plant counting, disease and stress detection, and row/terrain segmentation. We work in 2D image and video and scope each project to your taxonomy.

Put a dedicated agritech computer vision pod on your data.

Start with a small paid pilot — see the quality before you scale. Independent, in-office, and live in about three weeks.