Strawberry detection: Datasets & Models

Manually scouting strawberry fields to estimate yield or check ripeness is slow, labor-intensive, and inconsistent between workers, and skilled labor for harvesting is increasingly hard to find. A detection model turns any camera feed into a repeatable source of fruit counts and ripeness data, letting you forecast harvest windows, prioritize picking, and reduce waste from overripe fruit. For robotic harvesters, accurate per-berry localization is the foundation for guiding a picking arm to the right target. Automating this removes the guesswork from crop monitoring and scales far beyond what a scouting crew can cover.

You get consistent, per-fruit counts and ripeness classification across an entire operation without adding headcount, and the models run on standard RGB cameras you may already have mounted on rovers, gantries, or greenhouse structures. RF-DETR delivers strong accuracy on cluttered, partially occluded fruit while remaining practical to deploy at the edge. Because the workflow builds on an open, extensible dataset, you can retrain for new cultivars, growing conditions, or camera angles as your deployment scales.

1. Upload an image: Drop any photo into the widget above to try strawberry detection for free, right on this page.
2. Preview instant results: The model detects ripe strawberry, unripe strawberry, strawberry flower, leaf, strawberry plant within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
3. Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
4. Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.