Seedling detection: Datasets & Models
Detect and localize individual seedlings in field or greenhouse images to support automated crop counting, stand assessment, and plant health monitoring.
Seedling detection
This detection workflow locates individual seedlings in agricultural imagery and draws a bounding box around each young plant, separating emerging crops from soil, weeds, and residue. It accepts inputs such as drone overhead captures, tractor-mounted camera feeds, greenhouse tray photos, and handheld field shots, and it can be trained on a seedling dataset to support crop counting, stand assessment, and plant health monitoring.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate seedling detection
Manually scouting fields to count emerged seedlings and assess stand uniformity is slow, subjective, and impossible to scale across hundreds of acres. Automating seedling detection lets agronomists and growers quantify emergence rates, identify replant zones, and flag gaps within hours of imagery capture instead of walking transects by hand. A detection model turns raw field imagery into structured counts and coordinates you can act on, reducing labor and catching problems while there's still time to intervene.
Key benefits
Automated seedling detection delivers consistent, repeatable plant counts and spacing metrics that manual scouting can't match at scale. It integrates with existing drone and camera infrastructure, processing imagery from equipment you already deploy, and scales from a single greenhouse tray to full-field surveys. Using RF-DETR for detection gives you strong accuracy on small, densely packed plants without extensive hand-tuning.
How to use it
- Upload an image: Drop any photo into the widget above to try seedling detection for free, right on this page.
- Preview instant results: The model detects seedling, sprout, seed tray, weed, soil patch 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 seedling 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 seedling detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Seedling Detection project to Roboflow Agent.