The Pl@ntNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills. Achieving consensus is crucial for training, but the vast scale of collected data makes traditional label aggregation strategies challenging. Additionally, as many species are rarely observed, user expertise can not be evaluated as an inter-user agreement: otherwise, botanical experts would have a lower weight in the training step than the average user as they have fewer but precise participation. Our proposed label aggregation strategy aims to cooperatively train plant identification models. This strategy estimates user expertise as a trust score per worker based on their ability to identify plant species from crowdsourced data. The trust score is recursively estimated from correctly identified species given the current estimated labels. This interpretable score exploits botanical experts’ knowledge and the heterogeneity of users. We evaluate our strategy on a large subset of the Pl@ntNet database focused on European flora, comprising over 6 000 000 observations and 800 000 users. We demonstrate that estimating users’ skills based on the diversity of their expertise enhances labeling performance.