Case Study: Ragweed Identification​

Ragweed (Ambrosia) identification based on phenology, using high-resolution RGB drone aerial photos


Ragweed pollen is a common allergen causing yearly suffering for millions of people. A single ragweed plant can produce up to a billion pollen grains in a season, which are carried long distances by the wind. It is a major health concern in many parts of the world – also, various countries have laws imposing fines if too much ragweed is found on a property.



Our partner turned to Proofminder to be able to recognize and identify ragweed on ragweed on thousands of hectares of land daily, based on plant phenology. 

It only took a few weeks for visual AI development and deployment on the Proofminder platform to ensure scalability.

Ragweed detection and the calculation of infection metrics became accessible via a simple, browser-based map display, to take decisive actions.


  1. Orthomosaic is automatically created
  2. Multiple Machine Learning algorithms were evaluated by leveraging Proofminder’s MLOps quick iteration capabilities. SVM and Random Forest were deemed most applicable
  3. Infection metrics are accurately calculated according to local law
  4. Country-level deployment envisioned using Proofminder:
  • 1st pass using satellite photos to identify potentially infected areas
  • 2nd pass uses drone photos for detailed analysis and ragweed identification
  • 3rd pass can focus on elimination – e.g. spraying

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