Vegetable sorting: Datasets & Models
A computer vision workflow that classifies vegetables by type, quality, and ripeness on sorting lines. It takes camera images of produce and assigns each item a category label for grading or routing.
Vegetable sorting
This workflow uses image classification to sort vegetables on processing lines, assigning each item a category label based on type, quality grade, and ripeness. Trained on a vegetable sorting dataset, it takes camera images of individual produce items and predicts a class that downstream systems can use to route, grade, or reject each vegetable.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate vegetable sorting
Manual vegetable sorting is repetitive, labor-intensive, and inconsistent between workers and shifts, and it becomes a bottleneck as throughput rises. A computer vision classifier applies the same grading criteria to every item at line speed, catching bruises, discoloration, and mis-sized produce that tired human graders miss. This reduces mislabeled batches, improves consistency for buyers, and frees staff for higher-value tasks. It also creates an auditable record of quality decisions, which matters for traceability in food processing.
Key benefits
Automating vegetable sorting with a ViT classifier delivers consistent grading, higher throughput, and lower labor dependency while integrating with the cameras and conveyor systems most facilities already have. The workflow scales across multiple lines and produce types without redesigning hardware, and models can be retrained on new datasets as crops, seasons, and quality standards change.
How to use it
- Upload an image: Drop any photo into the widget above to try vegetable sorting for free, right on this page.
- Preview instant results: The model detects tomato, potato, carrot, bell pepper, onion 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 Classification (ViT) 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 vegetable sorting
When you're ready to build a production model for vegetable sorting, we recommend training Classification (ViT). Unlike the ready-to-use model in the demo above, you fine-tune it on your own labeled images for higher accuracy, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Vegetable Sorting project to Roboflow Agent.