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 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 Precise Pollen Grain Detection in Bright Field Microscopy Using Deep Learning Techniques

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3583 ◽  
Author(s):  
Ramón Gallardo-Caballero ◽  
Carlos J. García-Orellana ◽  
Antonio García-Manso ◽  
Horacio M. González-Velasco ◽  
Rafael Tormo-Molina ◽  
...  
Keyword(s):  
Network Architecture ◽  
Detection System ◽  
Pollen Grains ◽  
Pollen Grain ◽  
Average Value ◽  
Pollen Types ◽  
Pollen Concentrations ◽  
Pollen Counting ◽  
Estimation System ◽  
3D Information

The determination of daily concentrations of atmospheric pollen is important in the medical and biological fields. Obtaining pollen concentrations is a complex and time-consuming task for specialized personnel. The automatic location of pollen grains is a handicap due to the high complexity of the images to be processed, with polymorphic and clumped pollen grains, dust, or debris. The purpose of this study is to analyze the feasibility of implementing a reliable pollen grain detection system based on a convolutional neural network architecture, which will be used later as a critical part of an automated pollen concentration estimation system. We used a training set of 251 videos to train our system. As the videos record the process of focusing the samples, this system makes use of the 3D information presented by several focal planes. Besides, a separate set of 135 videos (containing 1234 pollen grains of 11 pollen types) was used to evaluate detection performance. The results are promising in detection (98.54% of recall and 99.75% of precision) and location accuracy (0.89 IoU as the average value). These results suggest that this technique can provide a reliable basis for the development of an automated pollen counting system.

scholarly journals Development and application of a method to classify airborne pollen taxa concentration using light scattering data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenji Miki ◽  
Toshio Fujita ◽  
Norio Sahashi
Keyword(s):  
Light Scattering ◽  
Cryptomeria Japonica ◽  
Airborne Pollen ◽  
Chamaecyparis Obtusa ◽  
Scattering Data ◽  
Spatial Density ◽  
Monitoring Networks ◽  
Pollen Monitoring ◽  
Pollen Counts ◽  
Laser Optics

AbstractAlthough automated pollen monitoring networks using laser optics are well-established in Japan, it is thought that these methods cannot distinguish between pollen counts when evaluating various pollen taxa. However, a method for distinguishing the pollen counts of two pollen taxa was recently developed. In this study, we applied such a method to field evaluate the data of the two main allergens in Japan, Chamaecyparis obtusa and Cryptomeria japonica. We showed that the method can distinguish between the pollen counts of these two species even when they are simultaneously present in the atmosphere. This result indicates that a method for automated and simple two pollen taxa monitoring with high spatial density can be developed using the existing pollen network.

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 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 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.

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.

Universality of fractal aggregates as probed by light scattering

1989 ◽  
Vol 423 (1864) ◽  
pp. 71-87 ◽  
Keyword(s):  
Light Scattering ◽  
Dynamic Light Scattering ◽  
Master Curve ◽  
Rotational Diffusion ◽  
Fractal Dimensions ◽  
Scattering Data ◽  
Diffusion Limited ◽  
Single Master Curve ◽  
Using Data ◽  
Dynamic Light Scattering Data

Fractal colloid aggregates are studied with both static and dynamic light scattering. The dynamic light scattering data are scaled onto a single master curve, whose shape is sensitive to the structure of the aggregates and their mass distribution. By using the structure factor determined from computer-simulated aggregates, and including the effects of rotational diffusion, we predict the shape of the master curve for different cluster distributions. Excellent agreement is found between our predictions and the data for the two limiting régimes, diffusion-limited and reaction-limited colloid aggregation. Furthermore, using data from several completely different colloids, we find that the shapes of the master curves are identical for each régime. In addition, the cluster fractal dimensions and the aggregation kinetics are identical in each régime. This provides convincing experimental evidence of the universality of these two régimes of colloid aggregation.

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.

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