Apple detection: Datasets & Models
A computer vision workflow that locates and counts apples in images of orchard trees, harvested bins, or packing lines. It identifies individual fruit even under partial occlusion by leaves and branches.
Apple detection
This apple detection workflow uses object detection to locate individual apples in images from orchard cameras, drones, harvesting rigs, or packing line feeds, drawing a bounding box around each piece of fruit. It handles partial occlusion from leaves and branches, producing per apple detections that support downstream counting, sizing, and yield estimation.
Try it out
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Why automate apple detection
Manually counting apples for yield estimation or grading a harvest by hand is slow, subjective, and does not scale across large orchards. Automating apple detection lets growers estimate crop load early, plan labor and logistics, and monitor fruit development without walking every row. For packing operations, it standardizes counting and sorting so throughput no longer depends on inspector fatigue or availability.
Key benefits
Teams get consistent, repeatable apple counts that feed directly into yield forecasting, harvest scheduling, and quality tracking. The model runs on standard orchard cameras, mobile devices, or existing grading-line hardware, so there is no need to rethink infrastructure. Built on RF-DETR, it detects densely clustered and partially hidden fruit accurately and scales from a single tree to full orchard blocks.
How to use it
- Upload an image: Drop any photo into the widget above to try apple detection for free, right on this page.
- Preview instant results: The model detects apple, leaf, branch, tree trunk 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 apple detection
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 apple detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Apple Detection project to Roboflow Agent.