'IRIS' as colony separator


How does it work?
Our software uses advanced image analysis and deep learning techniques. During training, the AI model was fed with thousands of images of colonies in various shapes, sizes, colors, and textures. As a result, the system can recognize visual patterns that are barely distinguishable to the human eye.
The AI takes into account, among other things:
- Color variations: such as white, yellow, red, or transparent.
- Shape and edge definition: smooth versus jagged edges.
- Surface characteristics: glossy, matte, or textured.
What are the benefits?
- Improved differential analysis: e.g., between contaminants and target colonies.
- Time savings: less manual inspection required.
- Consistency: no variation as with human evaluation.
- Data integration: each colony type can be logged, counted, and evaluated separately.
Applications
This smart colony detection is especially valuable in:
- Clinical diagnostics (e.g., antibiograms)
- Food safety testing
- Pharmaceutical quality research
- Microbial diversity studies
Our solution shows that AI in the lab is not just a support tool, but a full-fledged colleague. Feel free to contact us for a demonstration or to discuss how this could work for your applications.

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AI That Sees Beyond: Our Colony Counter Detects More Than Just Numbers
In a world where microbial analysis is becoming faster and more complex, simply counting colonies is no longer enough. What really matters is knowing what you're counting. Our AI colony counter does exactly that — it distinguishes between bacterial colonies as if an experienced microbiologist were working inside the machine full-time. And to be honest, it often does so even more consistently.
Detects Differences Humans Often Miss
Our AI is trained not just to count colonies, but to actively classify them into categories. That means:
- By color – from milky white to deep red, even subtle shades are detected.
- By shape and structure – smooth, ragged, concentric, irregular.
- By surface and sheen – matte vs. glossy, wet vs. dry.
- By growth pattern – dispersed, grouped, or overlapping.
Even when multiple colony types grow on a single plate, the system recognizes each type individually.
Automatic Classification Into Categories
After training, our AI instantly classifies colonies into categories such as:
- Target organisms vs. contaminants
- Resistant strains vs. wild type
- Large vs. small colonies (e.g., in growth or stress studies)
- Known vs. unknown patterns (for further research)
This classification can be directly linked to analysis tools or LIMS systems. So you don't just get a total count, you gain immediate insight into what is growing.
Where Does This Make a Difference?
In food safety: quickly detecting unwanted flora among normal bacteria.
- In pharma: detection of abnormal colonies in production environments.
- In clinical research: efficient screening for mixed infections.
- In R&D: monitoring subpopulations in selection experiments.
More Than a Counter: A Digital Microbiologist
Our AI changes the way you look at petri dishes. The system doesn’t just count, it understands what it sees. That means faster decisions, higher reliability, and most importantly: less guesswork.
Curious to see how this works in your lab? We’d be happy to show you live during an on-site demo or via Teams!
Besides distinguishing colonies, our colony counter IRIS is also perfectly suited for counting colony numbers. Want to see how this process works? Then check out this page.
Wondering how this product works? We will gladly give you a demo to show!


















