<i>Urtica membranacea</i>and the importance of its separation from the rest of the Urticaceae in aeropalynological studies carried out in the Mediterranean region

Rodríguez, Adolfo Francisco Muñoz; Palacios, Inmaculada Silva; Molina, Rafael Tormo; Corchero, Alfonsa Moreno

doi:10.1080/11263500600947814

Cited by 4 publications

(2 citation statements)

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“…a thickening of the otherwise very thin exine and a germination area called the oncus (lens-shaped body located in the apertural region) 7 . The only species of Urticaceae that can be distinguished in aerobiological samples is Urtica membranacea due to its small size (~ 10–12 μm) and a high number of pores (usually more than six 21 . The main difference between the pollen of Urtica and Parietaria are the slightly smaller size and coarser surface ornamentation of Parietaria , and a more angular outline and more pronounced annulus of Urtica 22 .…”

Section: Introductionmentioning

confidence: 99%

Neural networks for increased accuracy of allergenic pollen monitoring

Polling

Li²,

Cao³

et al. 2021

Sci Rep

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Monitoring 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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Section: Introductionmentioning

confidence: 99%

Neural networks for increased accuracy of allergenic pollen monitoring

Polling

Li²,

Cao³

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…a thickening of the otherwise very thin exine and a germination area called the oncus (lens-shaped body located in the apertural region) 7 . The only species of Urticaceae that can be distinguished in aerobiological samples is Urtica membranacea due to its small size (~10-12μm) and high number of pores (usually more than six 21 . The main difference between the pollen of Urtica and Parietaria are the slightly smaller size and coarser surface ornamentation of Parietaria, and a more angular outline and more pronounced annulus of Urtica 22 .…”

Section: Introductionmentioning

confidence: 99%

Automatic Image Classification Using Neural Networks Increases Accuracy for Allergenic Pollen Monitoring

Polling¹,

Li²,

Cao³

et al. 2020

Preprint

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Monitoring of airborne pollen concentrations provides an important source of information for the globally increasing number of hay fever patients. Airborne pollen are 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 a Convolutional Neural Network (CNN) 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 have very low allergenic relevance, those from several species of Parietaria are severely allergenic. We collect pollen from both fresh as well as from herbarium specimens and use these to train the CNN model VGG16. The model shows that Urticaceae pollen can be distinguished with 98.3% accuracy. We then apply our model on 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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