Grape detection: Datasets & Models

Manually scouting rows and hand-counting clusters is slow, labor-intensive, and prone to sampling error, which makes accurate yield forecasts hard to produce before harvest. Automating grape detection lets an agriculture team survey far more vines in less time and generate consistent, repeatable counts across an entire block. This translates into better harvest logistics, more precise labor and equipment planning, and earlier identification of underperforming sections of a vineyard. Because the model runs on ordinary camera imagery, you can scale monitoring without adding specialized field crews.

You get repeatable cluster and berry counts that plug directly into yield estimation and crop-monitoring pipelines, reducing reliance on manual sampling. The workflow integrates with existing vineyard cameras, drones, and mobile devices, so you can deploy without new hardware investment. Trained on a grape dataset and powered by RF-DETR, the detection model scales from a single test block to entire estates while maintaining consistent accuracy.

1. Upload an image: Drop any photo into the widget above to try grape detection for free, right on this page.
2. Preview instant results: The model detects grape cluster, grape berry, grape leaf, vine, unripe grape 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.