Flower detection: Datasets & Models
Flower detection uses computer vision to locate and count individual flowers or blooms in field, greenhouse, or orchard imagery. It draws bounding boxes around each flower so you can quantify bloom stages, density, and distribution.
Flower detection
This detection workflow locates individual flowers in agricultural imagery and draws a bounding box around each bloom, allowing you to count and classify them by stage such as open flowers, buds, and unopened blooms. It accepts still images or video from drones, fixed field cameras, greenhouse setups, or handheld devices, and runs on models trained from an annotated flower dataset.
Try it out
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Why automate flower detection
Manually scouting fields to count blossoms is slow, subjective, and impossible to scale across large acreage, so growers often sample small plots and extrapolate with significant error. Automating flower detection turns the same drone or camera imagery you already collect into consistent, plant-level bloom counts without sending crews to walk rows. This gives agronomists an objective signal for predicting fruit set, timing pollination or thinning, and comparing performance across blocks and seasons.
Key benefits
Automated flower detection delivers repeatable bloom counts and density maps that manual scouting can't match, and it runs on the cameras and drones already deployed in your operation. Starting from an existing flower dataset shortens the path to a working model, and RF-DETR provides strong detection accuracy on small, densely clustered blooms. The same pipeline scales from a single greenhouse bench to thousands of acres of orchard.
How to use it
- Upload an image: Drop any photo into the widget above to try flower detection for free, right on this page.
- Preview instant results: The model detects flower, bud, blossom, petal, flower 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.
- 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 flower 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 flower detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Flower Detection project to Roboflow Agent.