CGIAR Crop Damage Classification Challenge
Zindi with IFPRI, ACRE Africa, KALRO and CGIAR Digital Innovation
Before trusting an AI system on a farming or food task you need a way to check it. Assigns probabilities across five smallholder crop damage categories from field photographs, scored by log loss.
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 17, 2026
What it helps with
- What damaged my crop
- Classify crop damage from a field photo
- Damage categories for smallholder crops
- Test a model on crop damage photos
- Compare crop damage classifiers
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
Dataset download
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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