Abstract. Advances in automatic bioaerosol monitoring require updated approaches to evaluate particle classification algorithms. We present a training and evaluation framework based on three metrics: (1) Kendall's Tau correlation between predicted and manual concentrations, (2) scaling factor, to assess identification efficiency, and (3) off-season noise ratio, quantifying off-season false predictions. Metrics are computed per class across confidence thresholds and five stations, and visualised in graphs revealing overfitting, station-specific biases, and sensitivity–specificity trade-offs. We provide optimal ranges for each metric respectively calculated from correlations on co-located manual measurements, worst-case scenario off-season noise ratio, and physical sampling limits constraining acceptable scaling factor. The evaluation framework was applied to seven deep-learning classifiers trained on holography and fluorescence data from SwisensPoleno devices, and compared with the 2022 holography-only classifier. Classifier performances are compared through visualisation methods, helping identifying over-training, misclassification between morphologically similar taxa or between pollen and non-pollen particles. This methodology allows a transparent and reproducible comparison of classification algorithms, independent of classifier architecture and device. Its adoption could help standardise performance reporting across the research community, even more so when evaluation datasets are standardised across different regions.
Meurville et al. (Tue,) studied this question.