Weed detection: Datasets & Models
A computer vision workflow that locates and identifies weeds among crops in field imagery, enabling targeted intervention instead of blanket spraying.
Weed detection
This weed detection workflow applies object detection to field imagery, drawing bounding boxes around individual weeds and separating them from surrounding crops and soil. It accepts input from drones, tractor-mounted cameras, or handheld devices, and a model trained on a weed dataset outputs the location and class of each detected weed for targeted spraying or removal.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate weed detection
Manual weed scouting is slow, labor-intensive, and inconsistent across large acreage, and calendar-based herbicide application wastes chemicals while missing localized infestations. Automating detection with computer vision lets a sprayer or robotic platform treat only the plants that need it, cutting herbicide use and reducing crop damage. For agriculture teams, this means faster field coverage, earlier identification of problem areas, and data on weed pressure that informs where and when to act.
Key benefits
Precise, plant-level detection supports spot spraying and mechanical weeding, reducing input costs and limiting chemical drift onto healthy crops. The workflow integrates with existing drone and tractor camera setups, and models like RF-DETR deliver the accuracy needed to tell weeds apart from crops in cluttered field scenes. As you collect more field imagery, the underlying dataset and model can be expanded to new species, regions, and seasons.
How to use it
- Upload an image: Drop any photo into the widget above to try weed detection for free, right on this page.
- Preview instant results: The model detects weed, crop seedling, broadleaf weed, grass weed, soil 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 weed 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 weed detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Weed Detection project to Roboflow Agent.