Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

MLA

Schaefer, Jakob, et al. “Towards Automatic Airborne Pollen Monitoring: From Commercial Devices to Operational by Mitigating Class-Imbalance in a Deep Learning Approach.” The Science of the Total Environment, vol. 796, Nov. 2021, p. 148932. EBSCOhost, https://doi.org/10.1016/j.scitotenv.2021.148932.

APA

Schaefer, J., Milling, M., Schuller, B. W., Bauer, B., Brunner, J. O., Traidl-Hoffmann, C., & Damialis, A. (2021). Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach. The Science of the Total Environment, 796, 148932. https://doi.org/10.1016/j.scitotenv.2021.148932

Chicago

Schaefer, Jakob, Manuel Milling, Björn W Schuller, Bernhard Bauer, Jens O Brunner, Claudia Traidl-Hoffmann, and Athanasios Damialis. 2021. “Towards Automatic Airborne Pollen Monitoring: From Commercial Devices to Operational by Mitigating Class-Imbalance in a Deep Learning Approach.” The Science of the Total Environment 796 (November): 148932. doi:10.1016/j.scitotenv.2021.148932.