SwisensPoleno Mars is the latest generation of real-time pollen monitors on the market. Thanks to its technology and network compatibility, SwisensPoleno Mars enables fully autonomous and stable long-term measurement of local pollen concentrations. SwisensPoleno Mars combines the measurement methods of digital holography with artificial intelligence and transparent data analysis to a reliable measurement system for automatic measurement and identification of pollen.
SwisensPoleno Mars and its measurement method:
Holographic images
Pollen identification with machine learning
The most important specifications at a glance:
Particle classes within 2 – 300µm
Air sampling volume 40l/min
Sigma-2 geometry sample inlet
Integrated particle concentrator
Further benefits of SwisensPoleno Mars
High temporal resolution of local pollen concentrations.
Non-invasive measurement method.
Immediate verification of identification results.
Fully remote operation, configuration and updates.
Fast and easy installation.
Developed as future standard in real-time pollen monitoring
Ambient Conditions: Outdoor proof at -20°C to +50°C, and 0 % to 100 % R.H.; for non-corrosive environment (contact us for close proximity to sea water)
External Interfaces: Power, Ethernet (if not using integrated mobile router)
Optional Accessories: Integrated mobile router, Stand for floor mounting,uninterruptible power supply (UPS), Solar cell power supply
Dimensions: 60 x 47 x 125 cm3 (L x W x H) (incl. roof, inlet and post mounting adapter)
Weight: 34 kg
Power Supply: 100-240 VAC, 50/60 Hz, 750 W peak incl. IPC & AC
Possible applications
Our technology is based on an airflow cytometer that uses digital holography and image recognition to identify pollen.
The integrated aerosol concentrator allows us to analyze an impressive volume flow of 40 liters per minute, which enables us to precisely detect local pollen concentrations in the minute range.
We can accurately detect local pollen concentrations in the minute range. The holographic images are captured in a few microseconds as the particle passes by. Our artificial intelligence processes the collected information from the images and identifies each particle. The AI recognizes and assigns the specific particle to the appropriate pollen class based on the known particle properties. By applying supervised machine learning and our SwisensEcosystem, new particle classes can be continuously generated and identified.
