Maneva VITA flagging a broken cracker on a food production conveyor, labeled Broken: Rejected

Use cases

How do food plants catch broken and misshapen products at line speed?

A camera above the line checks the shape of every unit as it passes, and Video-to-Action AI flags broken, chipped, misshapen and incomplete products in real time. Defective units are removed or rerouted before packaging, and every decision is saved with its image.

Food & Beverages

Quality Assurance

What it catches

On a food line, Video-to-Action AI checks the shape of each unit for:

Broken or chipped pieces
Misshapen or deformed products
Incomplete products, such as missing corners or edges
Units outside your size range
Height and flatness problems, with a 3D camera

Why manual checks miss it

At line speed, people cannot look at every piece, so inspection relies on sampling and judgment that drifts over a shift. Fixed-rule vision struggles too, because every good piece of food looks slightly different and a rule tight enough to catch a broken piece also rejects good ones.

How it acts

VITA, Maneva's quality and downtime agent, makes up to 30 accept or reject decisions per second. A misshapen unit is removed by the line's own pusher, air jet, diverter or robotic arm, and lightly misshapen units can go to rework instead of scrap, where your process allows it.

Every decision is logged with its image. Repeated shape defects point back to the step that causes them, such as forming, cutting or cooling.

Where it sits on the line

The camera sits above the conveyor after forming or baking, ahead of packaging, with a 3D camera where height or flatness matters. Some lines can start with cameras they already have; many need at least one off-the-shelf camera placed for the task. Maneva helps choose the camera and where it goes.

Result

Every unit is checked against the same standard, not a sample. On a candy line, Maneva connected the model to a robotic arm, and defective products now come off automatically at full line speed while throughput held. Read the customer story.

See it on a line like yours →

More for food and beverage plants: AI for food and beverage lines →

Frequently Asked Questions

Didn't find what you need? Our team will walk you through Video-to-Action AI and show how it fits your production environment.

Which shape defects can it detect?

Broken, chipped, misshapen and incomplete products, and units outside your size range. Height and flatness need a 3D camera, which finds raised edges and depth variation that a standard camera misses.

Will natural variation cause false rejects?

The model is trained on images of your own product, good and defective, so it learns how much variation your standard allows. It is tuned to keep false rejects low, because every good unit thrown out is lost product.

Do we need new cameras?

It depends on your site and the problem you want to solve. Some customers can start with the cameras they already have. Many need at least one new off-the-shelf camera placed and set up for the task, so the AI sees exactly what it needs to see.

Maneva helps you scope this from the start. We look at your line, the result you want, and what the camera needs to capture, then recommend the right camera and where to put it.

Maneva is hardware agnostic and connects to a wide range of camera types, including:

Standard RGB and security cameras for many quality, downtime, safety and efficiency use cases
3D and depth cameras for measuring volume, position and dimensions
Thermal and infrared cameras for heat signatures and low-light areas
Short-wavelength infrared (SWIR) cameras, which see through plastic to check seal integrity
High-resolution precision cameras for defects under a millimeter across large surfaces

If you can see it in the video, our AI can understand it and act on it. Start with one line, and we'll help you choose the setup that gets you there.