Annotated datasets for the validation of AI algorithms on crops, weeds, diseases, and pests
Wageningen University & Research and Munster Technological University, via agrifoodTEF
Before trusting an AI system on a farming or food task you need a way to check it. Provides FAIR annotated image datasets and returns performance metrics for customer algorithms submitted in ONNX format.
QUICK INFO
SOLUTION TYPE
Services & solution providers
TECHNOLOGY CATEGORY
Benchmarks & evaluations
AREA OF WORK
Research & scientific knowledge
ACCESS
Paid or approval
DEPLOYMENT
Cloud-hosted
SOURCE REVIEWED
August 17, 2026
What it helps with
- Test my crop and weed algorithm on labelled images
- Get performance numbers for my disease model
- Labelled pest images for validation
- Submit a model and get metrics back
- Independent check of a field vision model
Scope & audience
WHO IT'S FOR
Developer / AI builder · Ag-tech product team
AREA OF WORK
Research & scientific knowledge
COMMODITY / SCOPE
Cross-commodity
Access & technical details
ACCESS
People you hire
INTERFACE
Engagement / service
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
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