Field-level agricultural data

Field-level crop intelligence from space

OpenFields is building the first open field-level dataset connecting earth observation signals to real agricultural fields.

Training data for acreage, yield, and crop stress models.

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Used by researchers, builders, and early market participants.

Problem

Earth observation data doesn’t line up with real fields

Pixels are not fields

Public data is often late

Models train on noisy geometry

OpenFields closes the gap between earth observation signals and field reality.

Dataset

The missing data layer for agriculture

We align earth observation data with real field boundaries and crop rotations to create cleaner training data for agricultural models.

Output

Acreage

Output

Yield

Output

Stress

A decade of field-level crop history

API / GitHub coming soon

Historical crop rotations across nearly a decade, built for forecasting, research, and market intelligence.

Coverage: Midwest pilot
Years: 2016–2024
Crops: Corn, Soy
Resolution: Field level
Format: Open dataset

field_id crop_year primary_crop stress_signal

IA-LY-1842 2024 Corn 0.18

IA-LY-1843 2023 Soy 0.11

Preview schema View sample records

Product preview

Explore the map

Watch rotation intelligence appear in context, then click through to the live application.

Rotation popup Years + crops Field-level view

Explore Crop Rotation Data

Founder

Built by a farmer turned data scientist

I grew up on a farm in Iowa and now work in advanced analytics. OpenFields started with a simple question: why is so much crop data still disconnected from the real world of fields?

Serious data infrastructure for agriculture.

Founder, OpenFields Iowa farm roots · advanced analytics

Early access

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