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Advancing automated identification of airborne fungal spores: guidelines for cultivation and reference dataset creationAbstract
volumetric monitoring, there’s a growing interest in automated real-time methods. However, these methods rely heavily on machine learning, facing challenges due to diverse particle characteristics and limited ...
Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
automated real-time methods. However, these methods rely heavily on machine learning, facing challenges due to diverse particle characteristics and limited training data availability, especially for fungal ...
Advancing automated identification of airborne fungal spores: Guidelines for cultivation and reference dataset creation
allowing automated real-time monitoring. Most of them rely on machine learning for the identification of bioaerosols. However, the diverse nature of airborne particles in terms of size, properties and ...