“We use CVAT extensively to create the datasets used to train our autonomous weeding robots. It’s powerful and time-saving, yet still simple to use.”
CVAT for Agriculture & Agritech AI
CVAT turns field-robot, drone, and greenhouse imagery into validated training data for weed control, crop monitoring, and harvest automation, trusted by teams like Paltech.
Use cases
Turn Field Imagery Into Plant-Level Ground Truth
Weed detection for field robots
Tell crop from weed plant by plant for precision spraying and weeding.
Crop health and disease
Spot disease, pests, and nutrient stress on leaves before it spreads.
Fruit detection and harvest robotics
Count fruit and judge ripeness for yield estimates and robotic picking.
Livestock monitoring
Track animals, posture, and feeding behavior in barns and pastures.
Drone field scouting
Map stand counts, gaps, and waterlogging across whole fields.
How it Works
Integrate Seamlessly
Start labeling without changing how you work
Connect your robot and drone captures, existing labels, and review stages as they are. Nothing to re-plumb.
Book a demoProjects/weed-detect-2026
Search sourcesAdd source
SourceStatus
1s3://robot-captures/field-12Syncing…
2gs://drone-scouting/2026Syncing…
3azure://greenhouse-cams/bay-3Syncing…
4labels_v2 · COCO 1.0Syncing…
5Annotation → Validation → AcceptanceSyncing…
6webhook update:job → trainingSyncing…
7crop_weed_guide_v2.pdfSyncing…
Accelerate Validation
Clear labeling backlogs faster
AI pre-labels every plant. Your agronomists or our experts fix what it misses.
Book a demoJobs/Row 14 · Frame 882
weed #1
crop #2
weed #3
crop #4
weed #5
Ship with Confidence
Catch label errors before they reach training
Every job is scored against ground truth, so only clean data reaches training.
Book a demoTasks/Quality control
Mean annotation quality
98.4%
Target 95%
Honeypots
CVAT is how ML teams ship agriculture AI.
