Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: how does it work and how can we improve it?

TL;DR

This paper presents a cooperative label aggregation strategy for plant species identification that estimates user expertise to improve data quality and AI training, leveraging crowdsourced data and botanical knowledge.

Contribution

It introduces a trust score-based method to evaluate user expertise, enhancing label quality and AI performance in large-scale crowdsourced plant identification datasets.

Findings

Estimating user expertise improves labeling accuracy.

Filtering unreliable observations enhances AI training.

Combining AI votes with human input further refines data quality.

Abstract

Deep learning models for plant species identification rely on large annotated datasets. The PlantNet 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. Existing methods either retain all observations, resulting in noisy training data or selectively keep those with sufficient votes, discarding valuable information. 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 AI training step than the average user. Our proposed label aggregation strategy aims to cooperatively train plant identification AI models. This strategy estimates user expertise as a trust score per user 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. Subsequently, our strategy removes unreliable observations but retains those with limited trusted annotations, unlike other approaches. We evaluate PlantNet's strategy on a released large subset of the PlantNet database focused on European flora, comprising over 6M observations and 800K users. We demonstrate that estimating users' skills based on the diversity of their expertise enhances labeling performance. Our findings emphasize the synergy of human annotation and data filtering in improving AI performance for a refined dataset. We explore incorporating AI-based votes alongside human input. This can further enhance human-AI interactions to detect unreliable observations.