(Maths-ia) Leveraging Citizen Science and machine learning for Plant Identification: Challenges from Pl@ntNet

Pl@ntNet is a global citizen science platform that harnesses artificial intelligence to enable large-scale plant species identification and biodiversity monitoring.

With millions of contributors across over 200 countries, it represents one of the world’s largest biodiversity observatories.

While such platforms offer a cost-effective and scalable approach to data collection, the quality of user-generated annotations can vary significantly.

This variability can affect the reliability of automated plant identification, which is a key issue for both research and real-world biodiversity monitoring applications.

To address these challenges, we propose robust aggregation techniques to consolidate training data and reduce biases stemming from heterogeneous contributor inputs.

We further leverage statistically grounded prediction methods, including conformal prediction, to provide valid confidence sets for species identification, specifically optimized for long-tail classification scenarios.