Fruit detection: Datasets & Models

Manually counting fruit for yield forecasting or inspecting produce for grading is slow, labor-intensive, and inconsistent between workers, and it does not scale to thousands of trees or high-throughput packing lines. Automating fruit detection with a vision model gives growers repeatable counts and objective quality signals without pulling people off other tasks. In an agriculture setting this means earlier and more accurate harvest planning, tighter control over grade and pack-out, and the ability to spot damaged or diseased fruit before it ships. The same model can run continuously across a season where human inspection would be prohibitively expensive.

Automated fruit detection delivers consistent counts and classifications that support yield forecasting, robotic harvesting, and quality sorting, while running on the cameras and edge hardware many operations already deploy. Because it is built on the RF-DETR architecture, the model offers strong accuracy on dense, overlapping fruit and adapts to new crops or conditions through fine-tuning. The workflow scales from a single packing line to fleet-wide field deployment without adding proportional labor.

1. Upload an image: Drop any photo into the widget above to try fruit detection for free, right on this page.
2. Preview instant results: The model detects apple, orange, banana, strawberry, grape cluster 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.