I spent last week at a poultry processing plant in regional Victoria. Not the glamorous side of AI, I’ll admit. But watching cameras inspect thousands of chicken pieces per hour—catching defects human eyes would miss—that’s where the real action is.

Quality control in food processing has traditionally been one of those “throw people at it” problems. Put workers on the line, train them to spot issues, hope they stay focused during an eight-hour shift. It works, mostly. But it’s expensive, inconsistent, and increasingly hard to staff.

Computer vision is changing that equation.

What computer vision actually does on a production line

Let’s strip away the marketing speak. A typical computer vision quality system has a few components:

Cameras: High-resolution cameras positioned at key points on the production line. These need to handle the speed of the line, the lighting conditions, and the specific product being inspected.

Processing hardware: Usually an industrial PC or edge device that runs the image analysis in real-time. Latency matters—you can’t have a three-second delay when products are flying past.

AI models: Software trained to recognise defects, contaminants, or quality issues specific to your product. This is where machine learning comes in.

Integration: Connecting the system to your line so it can reject bad products automatically, trigger alerts, or log data for traceability.

Three applications that are genuinely ready

Not everything being sold works as advertised. But these three applications have reached the point where they’re reliable enough for production use.

Defect detection

The most mature application. Systems can reliably spot:

• Physical damage (bruising, tears, dents)
• Colour variations indicating quality issues
• Size and shape inconsistencies
• Missing components in packaged goods

A bakery in Queensland I spoke with has cameras checking every loaf of bread. They catch burnt edges, collapsed tops, and undercooked portions that slipped past human inspectors about 15% of the time.

Foreign object detection

Finding things that shouldn’t be there. Metal detectors have been around forever, but vision systems can catch plastic, glass, cardboard fragments, and organic contaminants that other methods miss.

One seafood processor told me their vision system caught a piece of plastic packaging that had gotten into the line. It was the same colour as the fish—no human would have spotted it at production speed.

Label verification

Sounds boring, but mislabelling is a massive compliance risk, especially with allergen regulations. Vision systems can verify:

• Correct product-label matching
• Legible print and barcodes
• Proper date coding
• Regulatory compliance marks

A recall due to allergen mislabelling can cost millions and destroy consumer trust. Automated verification catches errors before products ship.

What’s still not ready for prime time

I’m going to be honest about the limitations, because too many vendors oversell.

Complex quality judgments: Some quality calls require context that current AI struggles with. “Is this meat marbling good enough for premium grade?” involves subjective assessment that trained graders do better.

Highly variable products: AI works best when products are reasonably consistent. Artisanal cheese with natural variations? Much harder than uniform processed products.

Dusty, wet, extreme environments: The cameras and hardware exist, but maintenance becomes a significant factor. One client spent more on keeping cameras clean than on the AI itself.

Real costs for real factories

Let me give you some actual numbers from recent Australian implementations.

A basic single-camera inspection station for defect detection typically runs $40,000-80,000 installed and configured. That includes cameras, processing hardware, software licensing, and integration work.

A multi-point system covering several inspection stages might be $150,000-300,000 for a mid-size facility.

Ongoing costs include software licensing (often annual), maintenance, and occasional model retraining as products or conditions change. Budget 10-15% of initial costs annually.

The ROI calculation usually comes down to:

• Labour savings (but rarely complete elimination of QC staff)
• Reduced waste from catching issues earlier
• Fewer customer complaints and returns
• Better compliance documentation

Most facilities I’ve seen hit payback in 12-24 months, but it varies enormously based on throughput and current defect rates.

How to evaluate if this makes sense for your operation

Start by documenting your current quality challenges. What defects slip through? What’s the cost when they do? How much are you spending on manual inspection?

Then consider your production environment. Is it stable enough for camera systems? Do you have the technical capability to maintain this kind of equipment?

Talk to other food manufacturers who’ve implemented vision systems—they’ll give you the straight truth that vendors won’t. Industry associations like AFGC can sometimes make introductions.

If you decide to move forward, pilot on one line before rolling out broadly. The technology works, but every factory is different. You’ll learn things in the first three months that change your approach.

The workforce angle

I asked the plant manager in Victoria how his team reacted to the new system. His answer surprised me.

“They were relieved, honestly. Standing there staring at product for hours is mind-numbing work. Now they’re doing final checks on flagged items and handling the exceptions. It’s a better job.”

That matches what I’ve seen elsewhere. The jobs change rather than disappear. But it does require different skills, which means training investment.

Looking ahead

Computer vision in food processing is past the experimental phase. It’s production-ready technology that’s proving itself in Australian facilities right now.

The next frontier is combining vision with other sensors—temperature, humidity, spectral analysis—to build more complete quality pictures. Some facilities are already doing this, but it’s early days.

For now, if you’re in food processing and still relying purely on human inspection, it’s worth a serious look at what’s possible. The technology has caught up with the promise.