Agriculture-Vision
SHI Labs (University of Oregon), University of Illinois Urbana-Champaign and Intelinair
Before trusting an AI system on a farming or food task you need a way to check it. Semantic segmentation of six aerial farmland anomalies including weed cluster, standing water and planter skip, scored by mean intersection over union.
QUICK INFO
SOLUTION TYPE
Models, data & tools
TECHNOLOGY CATEGORY
Benchmarks & evaluations
AREA OF WORK
Research & scientific knowledge
ACCESS
Free
DEPLOYMENT
Self-hosted / on-premise · Downloadable
SOURCE REVIEWED
August 23, 2026
What it helps with
- Find weed clusters in aerial farm images
- Spot standing water in a field from the air
- Detect planter skip from imagery
- Test a model on farmland problems from above
- Aerial field problem image dataset
- Compare segmentation models on farmland
Scope & audience
WHO IT'S FOR
Scientist / researcher · Developer / AI builder
AREA OF WORK
Research & scientific knowledge
COMMODITY / SCOPE
Cross-commodity
Access & technical details
ACCESS
Anyone can get it
INTERFACE
Code repository
DEPLOYMENT
You run it yourself
LANGUAGES
Not stated by provider
Featured media
Source information
This listing was compiled from publicly available source material. Verify current details with the provider.
PROVIDER DOCUMENTATION
Reviewed August 2026
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