scholarly journals Real-time pollen monitoring using digital holography

2019 ◽  
Author(s):  
Eric Sauvageat ◽  
Yanick Zeder ◽  
Kevin Auderset ◽  
Bertrand Calpini ◽  
Bernard Clot ◽  
...  
Keyword(s):  
Particle Size ◽  
Monitoring System ◽  
Digital Holography ◽  
Pollen Grains ◽  
Polystyrene Latex ◽  
Pollen Monitoring ◽  
Pollen Types ◽  
Coarse Particulate Matter ◽  
Sampling Volume ◽  
Using Data

Abstract. We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognised using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations to ensure particle size and sampling volume were correctly characterised.

scholarly journals Real-time pollen monitoring using digital holography

2020 ◽  
Vol 13 (3) ◽  
pp. 1539-1550 ◽  
Author(s):  
Eric Sauvageat ◽  
Yanick Zeder ◽  
Kevin Auderset ◽  
Bertrand Calpini ◽  
Bernard Clot ◽  
...  
Keyword(s):  
Particle Size ◽  
Monitoring System ◽  
Digital Holography ◽  
Pollen Grains ◽  
Polystyrene Latex ◽  
Pollen Monitoring ◽  
Pollen Types ◽  
Coarse Particulate Matter ◽  
Sampling Volume ◽  
Using Data

Abstract. We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols, and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognized using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device, and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations in order to ensure particle size and sampling volume were correctly characterized.

scholarly journals Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – Establishment of Automated Multi-taxa Pollen Counting Estimation System (AME System)

2020 ◽  
Author(s):  
Kenji Miki ◽  
Shigeto Kawashima
Keyword(s):  
Light Scattering ◽  
Pollen Grains ◽  
Temporal Changes ◽  
Scattering Data ◽  
Pollen Monitoring ◽  
Pollen Types ◽  
Laser Optics ◽  
Pollen Counting ◽  
Estimation System ◽  
Multiple Pollen

Abstract. Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination. Our experimental results indicate that different pollen types often have different light scattering characteristics, as previous research has suggested. Our results also show that light scattering distributions did not undergo significant temporal changes. Further, we show that the concentration of two different types of pollen could be estimated separately from the total number of pollen grains by fitting the light scattering data to a probability density curve. These findings should help realize a fast and simple automatic pollen monitoring system.

scholarly journals COLOUR IMAGE IN 2D AND 3D MICROSCOPY FOR THE AUTOMATION OF POLLEN RATE MEASUREMENT

2011 ◽  
Vol 21 (1) ◽  
pp. 25 ◽  
Author(s):  
Pierre Bonton ◽  
Alain Boucher ◽  
Monique Thonnat ◽  
Regis Tomczak ◽  
Pablo J Hidalgo ◽  
...  
Keyword(s):  
Pollen Grains ◽  
Pollen Grain ◽  
Pollen Monitoring ◽  
3D Images ◽  
Pollen Dispersion ◽  
Colour Image ◽  
Pollen Types ◽  
2D And 3D ◽  
Prevention Of Allergy ◽  
Original Device

Pollen monitoring is of great importance for the prevention of allergy. As this activity is still largely carried out by humans, there is an increasing interest in the automation of pollen monitoring. The goal is to reduce monitoring time in order to plan more efficient treatments. In this context, an original device based on computer vision is developed. The goal of such a system is to provide accurate measurement of pollen concentration. This information can be used as well by palynologists, clinicians or by a forecast system to predict pollen dispersion. The system is composed of two modules: pollen grain extraction and pollen grain recognition. In the first module, the pollen grains are observed in light microscopy and are extracted automatically from a microscopic slide dyed with fuchsin and digitised in 3D. The colour segmentation techniques implemented on a hardware architecture are presented. In the second module, the pollen grains are analysed for recognition. To accomplish recognition, it is necessary to work on 3D images and to use deep palynological knowledge. This knowledge describes the pollen types according to their main visible characteristerics and to those which are important for recognition. Some pollen structures are identified, like the pore with annulus in Poaceae, the reticulum in Olea and similar pollen types or the cytoplasm in Cupressaceae. Preliminary results show correct recognition of some pollen types, like Urticaceae or Poaceae, and some groups of pollen types, like reticulate group.

scholarly journals Estimation of pollen counts from light scattering intensity when sampling multiple pollen taxa – establishment of an automated multi-taxa pollen counting estimation system (AME system)

2021 ◽  
Vol 14 (1) ◽  
pp. 685-693
Author(s):  
Kenji Miki ◽  
Shigeto Kawashima
Keyword(s):  
Light Scattering ◽  
Pollen Grains ◽  
Temporal Changes ◽  
Scattering Data ◽  
Pollen Monitoring ◽  
Pollen Types ◽  
Laser Optics ◽  
Pollen Counting ◽  
Estimation System ◽  
Multiple Pollen

Abstract. Laser optics have long been used in pollen counting systems. To clarify the limitations and potential new applications of laser optics for automatic pollen counting and discrimination, we determined the light scattering patterns of various pollen types, tracked temporal changes in these distributions, and introduced a new theory for automatic pollen discrimination. Our experimental results indicate that different pollen types often have different light scattering characteristics, as previous research has suggested. Our results also show that light scattering distributions did not undergo significant temporal changes. Further, we show that the concentration of two different types of pollen could be estimated separately from the total number of pollen grains by fitting the light scattering data to a probability density curve. These findings should help realize a fast and simple automatic pollen monitoring system.

scholarly journals Feed particle size evaluation: conventional approach versus digital holography based image analysis

2009 ◽  
Vol 8 (sup2) ◽  
pp. 283-285
Author(s):  
Anna Campagnoli ◽  
Marco Alberto Carlo Potenza ◽  
Matteo Alaimo ◽  
Alessandro Agazzi ◽  
Vincenzo Chiofalo ◽  
...  
Keyword(s):  
Particle Size ◽  
Image Analysis ◽  
Digital Holography ◽  
Conventional Approach ◽  
Size Evaluation

Pollen Taphonomy in a Canyon Stream

1987 ◽  
Vol 28 (3) ◽  
pp. 393-406 ◽  
Author(s):  
Patricia L. Fall
Keyword(s):  
Surface Soil ◽  
Pollen Grains ◽  
Close Correlation ◽  
Soil Samples ◽  
Alluvial Deposits ◽  
Modern Pollen ◽  
Pollen Types ◽  
Pollen Rain ◽  
Arid Steppe ◽  
Turbulent Water

AbstractSurface soil samples from the forested Chuska Mountains to the arid steppe of the Chinle Valley, Northeastern Arizona, show close correlation between modern pollen rain and vegetation. In contrast, modern alluvium is dominated by Pinus pollen throughout the canyon; it reflects neither the surrounding floodplain nor plateau vegetation. Pollen in surface soils is deposited by wind; pollen grains in alluvium are deposited by a stream as sedimentary particles. Clay-size particles correlate significantly with Pinus, Quercus, and Populus pollen. These pollen types settle, as clay does, in slack water. Chenopodiaceae-Amaranthus, Artemisia, other Tubuliflorae, and indeterminate pollen types correlate with sand-size particles, and are deposited by more turbulent water. Fluctuating pollen frequencies in alluvial deposits are related to sedimentology and do not reflect the local or regional vegetation where the sediments were deposited. Alluvial pollen is unreliable for reconstruction of paleoenvironments.

scholarly journals Neural networks for increased accuracy of allergenic pollen monitoring

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marcel Polling ◽  
Chen Li ◽  
Lu Cao ◽  
Fons Verbeek ◽  
Letty A. de Weger ◽  
...  
Keyword(s):  
Neural Networks ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Allergenic Pollen ◽  
Herbarium Specimens ◽  
Pollen Monitoring ◽  
Pollen Concentrations ◽  
Source Of Information ◽  
Urticaceae Pollen

AbstractMonitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen is traditionally counted under the microscope, but with the latest developments in image recognition methods, automating this process has become feasible. A challenge that persists, however, is that many pollen grains cannot be distinguished beyond the genus or family level using a microscope. Here, we assess the use of Convolutional Neural Networks (CNNs) to increase taxonomic accuracy for airborne pollen. As a case study we use the nettle family (Urticaceae), which contains two main genera (Urtica and Parietaria) common in European landscapes which pollen cannot be separated by trained specialists. While pollen from Urtica species has very low allergenic relevance, pollen from several species of Parietaria is severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these without the often used acetolysis step to train the CNN model. The models show that unacetolyzed Urticaceae pollen grains can be distinguished with > 98% accuracy. We then apply our model on before unseen Urticaceae pollen collected from aerobiological samples and show that the genera can be confidently distinguished, despite the more challenging input images that are often overlain by debris. Our method can also be applied to other pollen families in the future and will thus help to make allergenic pollen monitoring more specific.

scholarly journals Airborne pollen observations using a multi-wavelength Raman polarization lidar in Finland: characterization of pure pollen types

2020 ◽  
Author(s):  
Xiaoxia Shang ◽  
Elina Giannakaki ◽  
Stephanie Bohlmann ◽  
Maria Filioglou ◽  
Annika Saarto ◽  
...  
Keyword(s):  
Optical Properties ◽  
Airborne Pollen ◽  
Pollen Grains ◽  
Wavelength Dependence ◽  
Depolarization Ratio ◽  
Mean Values ◽  
Pollen Types ◽  
Pine Pollen ◽  
Significant Wavelength ◽  
532 Nm

Abstract. We present a novel algorithm for characterizing the optical properties of pure pollen particles, based on the depolarization values obtained in lidar measurements. The algorithm was first tested and validated through a simulator, and then applied to the lidar observations during a four-month pollen campaign from May to August 2016 at the European Aerosol Research Lidar Network (EARLINET) station in Kuopio (62°44′ N, 27°33′ E), in Eastern Finland. Twenty types of pollen were observed and identified from concurrent measurements with Burkard sampler; Birch (Betula), pine (Pinus), spruce (Picea) and nettle (Urtica) pollen were most abundant, contributing more than 90 % of total pollen load, regarding number concentrations. Mean values of lidar-derived optical properties in the pollen layer were retrieved for four intense pollination periods (IPPs). Lidar ratios at both 355 and 532 nm ranged from 55 to 70 sr for all pollen types, without significant wavelength-dependence. Enhanced depolarization ratio was found when there were pollen grains in the atmosphere, and even higher depolarization ratio (with mean values of 25 % or 14 %) was observed with presence of the more non-spherical spruce or pine pollen. The depolarization ratio at 532 nm of pure pollen particles was assessed, resulting to 24 ± 3 % and 36 ± 5 % for birch and pine pollen, respectively. Pollen optical properties at 1064 nm and 355 nm were also estimated. The backscatter-related Ångström exponent between 532 and 1064 nm was assessed as ~ 0.8 (~ 0.5) for pure birch (pine) pollen, thus the longer wavelength would be better choice to trace pollen in the air. The pollen depolarization ratio at 355 nm of 17 % and 30 % were found for birch and pine pollen, respectively. The depolarization values show a wavelength dependence for pollen. This can be the key parameter for pollen detection and characterization.

scholarly journals DESIGN AND IMPLEMENTATION OF INTELLIGENT SEAWATER AUTOMATIC ON-LINE MONITORING SYSTEM BASED ON BIG DATA

2018 ◽  
Vol 48 (3) ◽  
pp. 157-162
Author(s):  
L. Y. LI ◽  
J. YANG ◽  
Y. LEI ◽  
K. H. XIONG ◽  
W. H. CHEN ◽  
...  
Keyword(s):  
Monitoring System ◽  
Online Monitoring ◽  
Large Data ◽  
Test System ◽  
Data Conversion ◽  
Detection Technology ◽  
Large Data Analysis ◽  
Using Data ◽  
Integration Data ◽  
Sea Area

Based on large data analysis method and automatic detection technology, this paper designs a test system, which can realize intelligent online monitoring of seawater. Based on the theory of large data, the data preprocessing method of large data is applied by relying on the information transmitted by integrated sensors. Using data cleaning, data integration, data conversion and data reduction technology, a large number of data collected by marine monitoring devices are processed accurately. An automatic seawater monitoring system is designed on a software platform. Finally, combined with the experimental data of a certain sea area, the test results are analyzed, which proves the feasibility and effectiveness of the designed seawater online monitoring system. It has achieved the effect of seawater environmental analysis and early warning.

scholarly journals Calibration of the passive cavity aerosol spectrometer probe for airborne determination of the size distribution

2013 ◽  
Vol 6 (3) ◽  
pp. 4123-4152 ◽  
Author(s):  
Y. Cai ◽  
J. R. Snider ◽  
P. Wechsler
Keyword(s):  
Particle Size ◽  
Size Distribution ◽  
Scattered Light ◽  
Particle Diameter ◽  
Polystyrene Latex ◽  
Calibration Methods ◽  
Research Aircraft ◽  
Amplifier Gain ◽  
Gain Stage

Abstract. This work describes calibration methods for the particle sizing and particle concentration systems of the passive cavity aerosol spectrometer probe (PCASP). Laboratory calibrations conducted over six years, in support of the deployment of a PCASP on a cloud physics research aircraft, are analyzed. Instead of using the many calibration sizes recommended by the PCASP manufacturer, a relationship between particle diameter and scattered light intensity is established using three sizes of mobility-selected polystyrene latex particles, one for each amplifier gain stage. In addition, studies of two factors influencing the PCASP's determination of the particle size distribution – amplifier baseline and particle shape – are conducted. It is shown that the PCASP-derived size distribution is sensitive to adjustments of the sizing system's baseline voltage, and that for aggregate spheres, a PCASP-derived particle size and a sphere-equivalent particle size agree within uncertainty dictated by the PCASP's sizing resolution. Robust determination of aerosol concentration, and size distribution, also require calibration of the PCASP's aerosol flowrate sensor. Sensor calibrations, calibration drift, and the sensor's non-linear response are documented.

Sign in / Sign up

Export Citation Format

Share Document