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 at many parts of the world – also various countries have laws triggering fines if too much ragweed is found on a property.



Our partner turned to Proofminder to be able to recognize and identify ragweed on many thousand hectares a day based on plant phenology. 

It took just a few weeks for visual AI development and deployment on the Proofminder platform ensured scalability right away. 

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


  1. Orthomosaic is automatically created
  2. Multiple Machine Learning algorithms evaluated leveraging Proofminder’s MLOps quick iteration capabilities. SVM and Random Forest deemed most applicable
  3. Infection metrics 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