Crop row segmentation: Datasets & Models
Crop row segmentation uses computer vision to identify and outline planted rows in field imagery, separating crop lines from soil, weeds, and gaps. It provides pixel-level masks that machines and analytics tools can use to navigate and assess field structure.
Crop row segmentation
This workflow runs segmentation on aerial, drone, or ground-level field imagery to perform crop row detection, generating pixel-level masks for each planted row. It separates crop rows from bare soil, inter-row gaps, and weed patches, outputting per-row masks that navigation systems and analytics tools can consume.
Try it out
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Why automate crop row segmentation
Manually mapping crop rows or hand-labeling field boundaries across thousands of acres is slow, inconsistent, and impossible to keep current as crops grow and gaps emerge. Automating crop row detection with segmentation lets autonomous tractors and sprayers steer accurately down rows, enables precise inter-row weeding, and quantifies emergence and stand uniformity without a person walking the field. Because the model outputs pixel-accurate masks, it captures curved rows, missing sections, and irregular spacing that simpler detection approaches miss.
Key benefits
Teams get consistent, repeatable row maps that plug directly into existing drone pipelines, field management platforms, and machine guidance systems using cameras they already deploy. Segmentation scales from a single test plot to full-farm orthomosaics, and the same model can support navigation, weed pressure assessment, and yield-zone analysis. RF-DETR instance segmentation delivers strong accuracy on dense, overlapping row structures while remaining practical to fine-tune on your own field imagery.
How to use it
- Upload an image: Drop any photo into the widget above to try crop row segmentation for free, right on this page.
- Preview instant results: The model detects crop row, soil, weed, planting gap, tractor track 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 (instance segmentation) 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 crop row segmentation
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 crop row segmentation, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Crop Row Segmentation project to Roboflow Agent.