AgriPotential: Viticulture Edition
AgriPotential organisers (Codabench), CLEF 2026 phase
Before trusting an AI system on a farming or food task you need a way to check it. Predicts the five-class ordinal viticulture potential of each land pixel from multi-temporal Sentinel-2 imagery over southern France.
QUICK INFO
SOLUTION TYPE
Models, data & tools
TECHNOLOGY CATEGORY
Benchmarks & evaluations
AREA OF WORK
Research & scientific knowledge
ACCESS
Free
DEPLOYMENT
Cloud-hosted
SOURCE REVIEWED
August 17, 2026
What it helps with
- Is this land good for growing vines
- Rate vineyard potential from satellite images
- Map grape growing suitability in southern France
- Test a model on land potential scoring
- Satellite based vineyard potential benchmark
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
Hosted leaderboard
DEPLOYMENT
Hosted by the publisher
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
Explore more like this
SNAPMe
USDA Agricultural Research Service, via Ag Data Commons
Models, data & tools
Before trusting an AI system on a farming or food task you need a way to check it. Computer vision models predicting foods, ingredients and nutrients from photographs of meals, snacks and beverages paired with dietary records.
Free
Self-hosted / on-premise · Downloadable
Benchmarks & evaluations
SNAPMe
research-develop
research-and-scientific-knowledge
buildable-or-downloadable-component
benchmarks-and-evals
Free
Self-hosted / on-premise · Downloadable
August 17, 2026
- How many calories are in this dish
- Identify foods in a meal photo
- Work out ingredients from a picture
- Test a model on food photographs
- Compare nutrient estimates with dietary records
- Food photo benchmark for nutrition models
MIRAGE MMST and MMMT leaderboards
University of Illinois Urbana-Champaign (AIFARMS and Center for Digital Agriculture) with Amazon
Models, data & tools
Before trusting an AI system on a farming or food task you need a way to check it. Ranks models on single-turn and multi-turn agricultural consultation tasks using identification accuracy, reasoning quality and management recommendation metrics.
Free
Cloud-hosted
Benchmarks & evaluations
MIRAGE MMST and MMMT leaderboards
research-develop
research-and-scientific-knowledge
buildable-or-downloadable-component
benchmarks-and-evals
Free
Cloud-hosted
August 17, 2026
- Compare models on farm consultation tasks
- Which model identifies crop problems better
- Rankings for multi turn farm advice
- Check management recommendation scores
- Leaderboard for farm vision language models
Is this your Resource?
Contact us to claim this listing and add details, screenshots or a demo.