
Better crop decisions start below the surface.
Plant-parasitic nematodes can significantly affect crop yield and quality. Understanding which species are present, and in what numbers, is essential for effective crop selection, rotation planning and soil management. Together with Veridi Technologies, Brightlabs introduces a new generation of nematode diagnostics. By combining automated microscopy with AI-powered image recognition, we deliver detailed insights into nematode populations in soil samples. The result is a faster, scalable approach to soil diagnostics that transforms complex analytical data into clear, actionable information.

From soil sample to scientific insight. Advanced microscopy and artificial intelligence reveal what traditional analysis can take considerable time to uncover.
Advanced technology. Clearer insights. Better decisions.
Traditional nematode diagnostics rely heavily on specialised manual identification. Together with Veridi Technologies, Brightlabs makes this process more efficient through automated imaging and machine learning. The NEMASCOPE® platform captures microscopic images of nematodes extracted from soil samples. AI models analyse these images to identify, classify and count the organisms present. This technology supports faster, more scalable diagnostics without losing sight of what matters most: reliable information that helps growers, agronomists and agricultural businesses make informed decisions.
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Faster Results
Automated microscopy reduces reliance on time-consuming manual identification, enabling more efficient processing of soil samples and faster access to valuable diagnostic information.
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AI-Powered Identification
Advanced image recognition and machine learning identify, classify and count nematodes. The technology is continuously being developed to expand species coverage and improve diagnostic capabilities.
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Insights You Can Act On
Receive clear information about the nematode species detected and their quantities. These insights provide a scientific foundation for crop selection, rotation planning and informed soil management decisions.
Expertise
Reliability
Flexibility
Partnership
From soil sample to actionable insight
Understanding your soil shouldn't be unnecessarily complicated. Our analytical process combines laboratory expertise, automated microscopy and artificial intelligence to turn soil samples into meaningful information. Through five connected stages, Brightlabs and Veridi Technologies provide a structured approach to identifying plant-parasitic nematodes and understanding their presence in your soil.
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Sample Preparation
Soil samples undergo preparation to extract and isolate nematodes for microscopic examination. This creates the foundation for consistent and reliable analysis.
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Automated Microscopy
The extracted nematodes are captured using automated microscopy. High-resolution images provide the visual data required for further analysis.
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AI-Based Identification
Machine learning models analyse the microscopic images, recognising and classifying nematodes based on their visual characteristics and supported species profiles.
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Quantification & Analysis
Identified nematodes are counted and quantified, providing insight into species distribution and population levels within the analysed sample.
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Reporting & Insights
Brightlabs translates the analytical results into clear reporting. Customers gain insight into the species detected and their quantities, supporting better-informed crop and soil management decisions.
Sectors where method development is essential.
Insights from our laboratory

Brightlabs at CPHI Milan
Meet Brightlabs at CPHI Milan. Brightlabs will be attending CPHI Milan from 6 to 8 October 2026. We would be delighted to discuss your analytical challenges and explore how our expertise in chemical and microbiological analyses can support your product development, quality control and market launch.
The complete overview of what Brightlabs does.
Download our brochure for a complete overview of our analytical capabilities, services, and scientific approach.









