Fruit grading: Datasets & Models
Automated fruit grading using image classification to sort produce by quality, ripeness, and defects. Takes camera images of individual fruits and assigns a grade class.
Fruit grading
This computer vision workflow performs fruit grading detection by classifying images of individual pieces of produce into quality grades based on visual traits such as color, ripeness, size, bruising, and surface defects. It takes single-fruit images captured by inline cameras or grading station rigs as input and returns a grade label like premium, standard, or reject.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate fruit grading
Manual fruit grading is slow, subjective, and inconsistent between graders and across shifts, and it becomes a bottleneck during peak harvest volumes. Human inspectors fatigue, disagree on borderline cases, and cannot easily produce the audit trail that food and beverage buyers and retailers increasingly demand. Automating grading with a classification model delivers repeatable, objective grade assignments at line speed and captures a record of every decision. This lets packing operations redeploy skilled workers to handling and quality oversight rather than repetitive visual sorting.
Key benefits
A ViT-based grading classifier delivers consistent, high-throughput sorting that runs on the cameras and conveyor infrastructure many packing houses already operate, avoiding costly hardware replacement. It scales across multiple lines and fruit varieties by retraining on new labeled images, and it produces per-fruit grade data you can feed into yield tracking and quality reporting. The result is tighter grade uniformity, reduced giveaway, and traceable quality decisions.
How to use it
- Upload an image: Drop any photo into the widget above to try fruit grading for free, right on this page.
- Preview instant results: The model detects apple, orange, banana, tomato, strawberry 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 fruit grading
When you're ready to build a production model for fruit grading, 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 Fruit Grading project to Roboflow Agent.