Orbem uses AI to reinvent MRI for food, agriculture and biology
Munich-based DeepTech company Orbem is redefining how Magnetic Resonance Imaging (MRI) can be used outside traditional hospitals. By combining advanced AI algorithms with high‑speed MRI scanners, the startup is turning a medical workhorse into a powerful, non‑invasive analysis tool for food, agriculture and broader biological applications.
Instead of relying on destructive sampling or chemical tests, Orbem enables producers and researchers to “look inside” products and living organisms in real time, extracting rich, quantitative data about structure, quality and health — all without cutting, injecting or harming the subject.
From hospital imaging to industrial and biological intelligence
For decades, MRI technology has been associated almost exclusively with clinical diagnostics. It offers detailed images of soft tissue but is expensive, slow and complex to operate. Orbem is challenging these limitations by redesigning the entire pipeline around AI-powered image acquisition and analysis.
The company’s approach focuses on three core shifts:
- Automation: Replacing manual configuration and interpretation with end‑to‑end machine learning workflows.
- Speed: Using AI-based reconstruction to dramatically reduce scan time while preserving or even enhancing image quality.
- Scalability: Adapting MRI systems for high‑throughput industrial environments, not just hospital imaging suites.
By doing so, Orbem aims to make MRI a practical, cost‑effective instrument for sectors that have historically relied on slower, less precise, or more invasive techniques.
Non-invasive insights for food and agriculture
One of the most promising applications of Orbem lies in the food and agriculture value chain. Producers typically depend on sampling, dissection or chemical tests to assess quality, freshness, or internal defects. These methods are often destructive, labor‑intensive and statistically limited.
Orbem’s AI‑driven MRI enables large volumes of products to be scanned rapidly, delivering detailed internal profiles without damage. Potential applications include:
- Quality grading of fruits, nuts, seeds and other commodities based on internal structure, moisture and defects.
- Early detection of spoilage, disease or contamination before it becomes visible externally.
- Yield optimization in breeding programs by analyzing internal traits of seeds, embryos or plant tissues.
By providing objective, high‑resolution data at scale, Orbem supports more precise pricing, better inventory decisions and reduced food waste. The company positions its technology as a way to make the food system more efficient, transparent and sustainable.
Revealing hidden biology without a scalpel
Beyond food and agriculture, Orbem is targeting a broad range of biological and life‑science use cases. Traditional research methods often require biopsies, staining or physical sectioning of samples. While informative, these approaches are invasive, time‑consuming and sometimes incompatible with longitudinal studies.
With AI-enhanced MRI, researchers can observe internal structures and dynamic processes in living systems repeatedly, without destroying the sample. This opens possibilities such as:
- Monitoring organ development and tissue changes over time in animal models.
- Studying water transport, storage and structural changes in plants under stress.
- Characterizing biomaterials and engineered tissues in three dimensions.
The combination of non‑invasive imaging and advanced data analytics allows scientists to generate richer datasets, accelerate discovery and reduce reliance on animal sacrifice or destructive testing.
AI at the core of Orbem’s DeepTech stack
What differentiates Orbem from conventional imaging vendors is the depth of its AI integration. Instead of treating machine learning as an add‑on for image interpretation, the company embeds AI models throughout the imaging chain.
Accelerated image acquisition
Using deep learning, Orbem can reconstruct high‑quality images from under‑sampled data, effectively shortening scan times. This is crucial for industrial settings where thousands of items may need to be inspected per hour.
Domain-specific analytics
On top of raw imaging, the company layers computer vision and predictive analytics tailored to each sector. For example, models can classify products by internal quality grade, flag anomalies or estimate key biological parameters. The output is not just an image, but actionable metrics and decisions.
Edge and cloud deployment
To support real‑world operations, Orbem designs its systems so that AI inference can run on‑device, in the cloud, or in hybrid configurations. This flexibility is essential for integration into factories, labs and processing plants with varying infrastructure constraints.
Implications for sustainability and industry standards
The ability to access hidden internal information non‑destructively has far‑reaching implications. For food producers, AI-powered MRI can help reduce waste by diverting sub‑optimal products to alternative uses, validating quality claims and minimizing unnecessary sampling. For breeders and researchers, it can shorten development cycles and improve the precision of selection.
On a broader scale, technologies like those developed by Orbem contribute to more data‑driven, resource‑efficient systems. By revealing what was previously invisible, they support better decision‑making across the supply chain, from farm and lab to factory and retailer.
As AI and imaging technologies continue to converge, Orbem exemplifies how DeepTech startups can repurpose established medical tools for new domains, turning complex physics into practical intelligence for food, agriculture and biology.