DODA
UTokyo-FieldPhenomics-Lab
Plant measurement research often needs code you can adapt rather than a finished tool. This is a code release related to plant measurement work, published on a public repository for others to read and build on.
QUICK INFO
SOLUTION TYPE
Models, data & tools
TECHNOLOGY CATEGORY
Benchmarks & evaluations
AREA OF WORK
Agronomy, soil & crop management
ACCESS
Free · Open source
DEPLOYMENT
Self-hosted / on-premise · Downloadable
SOURCE REVIEWED
August 17, 2026
What it helps with
- Code for measuring plants
- Adapt plant measurement code for my project
- Starting point for plant measurement work
- Measure plants from data in code
- Plant measurement research code
Scope & audience
WHO IT'S FOR
Developer / AI builder
AREA OF WORK
Agronomy, soil & crop management
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
Explore more like this
lm-evaluation-harness task kmmlu_direct_agricultural_sciences
EleutherAI
Models, data & tools
Before trusting an AI system on a farming or food task you need a way to check it. Korean Agricultural-Sciences multiple-choice subject defined as a direct-answer task in the lm-evaluation-harness.
Free
Self-hosted / on-premise · Downloadable
Benchmarks & evaluations
lm-evaluation-harness task kmmlu_direct_agricultural_sciences
research-develop
research-and-scientific-knowledge
buildable-or-downloadable-component
benchmarks-and-evals
Free
Self-hosted / on-premise · Downloadable
August 17, 2026
- Test a model on Korean agriculture questions
- How well does a model know agriculture in Korean
- Run a Korean farm knowledge test
- Compare models on Korean agricultural sciences
- Direct answer agriculture question set
Annotated datasets for the validation of AI algorithms on crops, weeds, diseases, and pests
Wageningen University & Research and Munster Technological University, via agrifoodTEF
Services & solution providers
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.
Paid or approval
Cloud-hosted
Benchmarks & evaluations
Annotated datasets for the validation of AI algorithms on crops, weeds, diseases, and pests
research-develop
research-and-scientific-knowledge
obtainable-service-or-consultancy
benchmarks-and-evals
Paid or approval
Cloud-hosted
August 17, 2026
- 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
Is this your Resource?
Contact us to claim this listing and add details, screenshots or a demo.