Ziad Matar
CEO
Veridi Technologies BV
Veridi Technologies provides AI and automated microscopy solutions for soil health assessment. The platform optimizes diagnostic workflows for plant disease and soil biodiversity analysis, increasing throughput while reducing costs. They offer flexible, no upfront cost services focused initially on plant parasitic nematode detection.
- Agriculture Technology
- Artificial Intelligence
- Data & Analytics
- Software Only
Updated 23 months ago
Funding
Raised to date
$2.6MRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Current methods for diagnosing plant pathogens and analyzing soil biodiversity are often manual, labor-intensive, and lack the scalability needed for comprehensive agricultural monitoring. This can lead to delays in disease detection, hindering timely intervention and potentially resulting in significant crop losses and soil degradation.
Solution
Veridi Technologies offers an AI-powered platform that automates the analysis of soil microbiomes and the diagnosis of plant pathogens. By combining computer vision, automated microscopy, deep learning, and cloud computing, the platform optimizes laboratory diagnostic workflows, providing accurate, affordable, and scalable insights into soil health. This technology enables early disease detection, facilitates informed decision-making for farmers, and supports sustainable agriculture practices through enhanced soil biodiversity analysis. The platform initially focuses on plant parasitic nematodes but is expanding to include environmental nematodes and other microorganisms of interest.
Target Audience
The primary target audience includes farmers, agricultural researchers, and laboratory diagnostic services seeking to improve crop yields, reduce costs, and promote soil health through advanced diagnostic tools.
Features
- Automated microscopy and computer vision for identifying and quantifying soil microorganisms
- Deep learning algorithms for accurate and rapid analysis of plant pathogens
- Cloud-based platform for scalable data processing and storage
- Optimized laboratory workflows for cost savings and efficiency
- Analysis of soil biodiversity indicators, including environmental nematodes
- Expansion to other microorganisms of interest, such as fusarium