---
title: "How to grade shrimp with computer vision"
description: "A camera over the grading belt takes a photo of each shrimp as it passes. A model trained on the plant own images, labeled against its own size and grade specs, estimates size so counts per pack can be checked, and marks likely issues such as broken pieces, missing tails, shell left on after peeling, dark spots and color problems. Flagged pieces can go to a recheck lane, and each flag keeps its photo so the quality lead can see why it was pulled. The quality team checks sample packs during each shift, adjusts the settings when good product is lost, and decides what is packed, regraded or rejected. The model does not replace food safety tests, temperature checks or official inspection, which stay in place, and people make every grade and release call."
url: https://lidvizion.ai/answers/how-to-grade-shrimp-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Direct answers](https://lidvizion.ai/answers/)

# How can a seafood processor use computer vision to size and grade shrimp on the line?

A camera over the grading belt takes a photo of each shrimp as it passes. A model trained on the plant own images, labeled against its own size and grade specs, estimates size so counts per pack can be checked, and marks likely issues such as broken pieces, missing tails, shell left on after peeling, dark spots and color problems. Flagged pieces can go to a recheck lane, and each flag keeps its photo so the quality lead can see why it was pulled. The quality team checks sample packs during each shift, adjusts the settings when good product is lost, and decides what is packed, regraded or rejected. The model does not replace food safety tests, temperature checks or official inspection, which stay in place, and people make every grade and release call.

## What this means in practice

- Start with one line and one product, such as peeled shrimp.
- Label a few thousand photos from your own product against your own specs.
- Send flags to a recheck lane before using automatic rejects.
- Check sample packs for size count and defects each shift.
- Keep food safety tests, temperature checks and inspection in place.

## How Lid Vizion approaches it

We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.

## More on Automation & custom AI

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