Case Study: Ragweed Identification

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

CHALLENGE

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.

SOLUTION

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.

DETAILS

Orthomosaic is automatically created
Multiple Machine Learning algorithms were evaluated by leveraging Proofminder’s MLOps quick iteration capabilities. SVM and Random Forest were deemed most applicable
Infection metrics are accurately calculated according to local law
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

Case Study: Ragweed Identification​

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

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Case Study: Ragweed Identification