Fruit counting: Datasets & Models
Fruit counting detection uses computer vision to identify and tally individual fruits on trees, vines, or conveyor lines from images or video. It turns raw camera feeds into per-frame and aggregate counts without manual tallying.
Fruit counting
This fruit counting detection workflow locates individual fruits on trees, vines, or packing-line conveyors and returns bounding boxes with per-type counts for each frame. It accepts standard inputs such as drone footage, handheld phone video, fixed field cameras, and still images captured during harvest, then aggregates the detections into total counts.
Try it out
Powered by general detection model
Run on custom image
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Why automate fruit counting
Manual fruit counting is slow, subjective, and impossible to scale across large orchards or high-throughput packing operations, so growers typically rely on sampling a few trees and extrapolating, which introduces error into yield forecasts. Automating fruit counting with computer vision lets an agriculture team count every visible fruit across whole blocks, catch variability between rows, and time labor and logistics around accurate numbers. It also removes the fatigue and inconsistency that come with human counting during long harvest days. The result is repeatable measurement you can compare season over season.
Key benefits
You get consistent, auditable counts that integrate with existing drone, greenhouse, and packing-line cameras rather than requiring new hardware. Detection models like RF-DETR handle dense clusters and partial occlusion common in fruit imagery, and the same pipeline scales from a single tree to thousands of acres. Counts export cleanly into yield estimation and harvest-planning tools your team already uses.
How to use it
- Upload an image: Drop any photo into the widget above to try fruit counting for free, right on this page.
- Preview instant results: The model detects apple, orange, lemon, tomato, strawberry 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.
- 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.
- 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.
Recommended model for fruit counting
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for fruit counting, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Fruit Counting project to Roboflow Agent.