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ZoomAgri

ZoomAgri utilizes computer vision and machine learning to enhance the testing, inspection, and certification processes for agricultural commodities and food products. By providing standardized and objective quality assessments, the company addresses inefficiencies in the agriculture supply chain, ensuring accurate results at a lower cost per analysis.

Coslada, SpainFounded 20178010K+ followers
Updated 20 months ago

Funding

$9.5M raised to dateRaised 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.

GV
Funding rounds are not available yet.

Founders

Product

Problem

Traditional methods of testing, inspection, and certification (TIC) for agricultural commodities often involve manual processes, leading to subjective assessments, classification errors, delays, and high costs. This can result in inefficiencies across the agriculture supply chain and hinder accurate quality control.

Solution

ZoomAgri provides a hardware-software system that digitizes the testing, inspection, and certification (TIC) process for agricultural commodities. The system combines a hardware scanner with artificial intelligence and computer vision to deliver standardized and objective quality assessments. ZoomAgri's technology enables real-time analysis of grain and seed varieties, as well as their physical quality, through a single scan. The company's image database, containing over 250 million unique images, powers its algorithms to identify quality, varietal, and origin for commodities, bringing transparency to the process. This allows stakeholders in the supply chain to improve efficiency, reduce waste, and ensure accurate distribution.

Target Audience

ZoomAgri's primary customers include agribusinesses, grain and oilseed producers, malting companies, and other stakeholders in the agricultural supply chain who require accurate and efficient quality assessments of commodities.

Features

  • Variety determination via computer vision and machine learning
  • Visual quality assessment of grains and oilseeds
  • Real-time results
  • Standardized and objective results
  • Low cost per analysis
  • Image database of over 250 million unique images
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