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.
- Independent & neutral
- In-office · NDA-bound
- Live in ~3 weeks
- Never crowdsourced
The annotation your models actually need.
The bottleneck isn't the model. It's the labels behind it.
Every new crop, region or season resets accuracy — and needs a fresh labeled set, fast.
Agronomy labels demand consistency across millions of frames a generalist crowd can't hold.
Your field imagery is proprietary — it shouldn't be scattered across anonymous gig workers.
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.
Questions, answered straight.
Still have one? Tell us about your data and we'll scope a small paid pilot.
Agriculture data annotation is the labeling of farm imagery — from drones, satellites, rovers and in-row cameras — so agritech AI models can recognize crops, weeds, fruit, disease and terrain. Labelix does this with a dedicated, in-office team trained to your crop taxonomy, not an anonymous crowd.
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.
Typically about three weeks. We recruit and train a dedicated, in-office pod for your project, then ramp it under daily QA. A small paid pilot proves quality before you commit to volume.
Annotation happens with a dedicated, vetted team in an access-controlled facility under signed NDAs, within a controlled environment — never farmed out to anonymous crowd workers. Your data and labels remain yours.
Yes. Because your pod is dedicated and retained, it carries forward the taxonomy and edge cases — so a new crop, geography or season is a re-train, not a restart.
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.