An integrated system for automated measurement of airborne pollen based on electrostatic enrichment and image analysis with machine vision

Yang, Jia-Jing; Klinkenberg, Christian; Pan, Jian-Zhang; Wyss, Hans M.; Toonder, Jaap M.J. den; Fang, Qun

doi:10.1016/j.talanta.2021.122908

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…SEM images can be used to improve the performance of pollen identification in practice [ 20 , 21 , 22 , 23 , 24 , 25 ], since the high resolution of SEM unlocks the potential to provide the obvious differentiation of the pollen grains for the experts (as shown in Table 1 ). In our study, we obtained 1324 SEM pollen images in total (805 Cupressaceae, 248 Fraxinus and 271 Ginkgo) from Beijing Meteorological Center, which served as the data for our automatic pollen identification task.…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Therefore, SEM images have exhibited significant potential to enhance the ability to accurately identify airborne pollens in an automatic manner for the palynology community [ 19 ]. Currently, some preliminary research works on SEM-based automatic pollen identification have been reported [ 20 , 21 , 22 , 23 , 24 , 25 ]. An early example of an SEM-image-based method was [ 20 ], in which the authors extracted the texture features to train a Fisher linear discriminant classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Treloar et al [ 21 ] calculated the area, perimeter, and roundness of pollen grains, followed by a linear discriminant function to classify 12 types of pollens from SEM images. Yang et al [ 22 ] first employed the R-CNN to detect pollen grains in SEM images, then AlexNet, GoogLeNet, and ResNet50 were trained for banana shrub and Papaver rhoeas pollen identification. To subdivide Artemisia pollen into three types, hierarchical clustering analysis on the pollen’s morphological features was carried out by Lu et al [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images

Xu²,

Cheng³

et al. 2023

Life

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Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels).

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Section: Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images

Xu²,

Cheng³

et al. 2023

Life

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show abstract

“…The standardized workflow for pollen concentration monitoring has been established in many countries, including twin pillars of pollen sampling and pollen identification [ 7 , 8 ]. In China, the Durham pollen sampler [ 9 ] has achieved significant success for pollen monitoring and the Hirst volume sampler [ 10 ] is regarded as a biomonitoring gold-standard device in Europe. The Burkhard [ 11 ] and Lanzoni samplers [ 12 ], based on the Hirst sampler, are also widely used in other countries.…”

Section: Introductionmentioning

confidence: 99%

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images

Zhao

Cheng

et al. 2022

Biology

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Existing API approaches usually independently leverage detection or classification models to distinguish allergic pollens from Whole Slide Images (WSIs). However, palynologists tend to identify pollen grains in a progressive learning manner instead of the above one-stage straightforward way. They generally focus on two pivotal problems during pollen identification. (1)Localization: where are the pollen grains located? (2) Classification: which categories do these pollen grains belong to? To perfectly mimic the manual observation process of the palynologists, we propose a progressive method integrating pollen localization and classification to achieve allergic pollen identification from WSIs. Specifically, data preprocessing is first used to cut WSIs into specific patches and filter out blank background patches. Subsequently, we present the multi-scale detection model to locate coarse-grained pollen regions (targeting at “pollen localization problem”) and the multi-classifiers combination to determine the fine-grained category of allergic pollens (targeting at “pollen classification problem”). Extensive experimental results have demonstrated the feasibility and effectiveness of our proposed method.

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DeepPollenCount: a swin-transformer-YOLOv5-based deep learning method for pollen counting in various plant species

Zhang,

Liu,

Wang

et al. 2024

Aerobiologia

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An integrated system for automated measurement of airborne pollen based on electrostatic enrichment and image analysis with machine vision

Cited by 4 publications

References 38 publications

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images

Weakly Supervised Collaborative Learning for Airborne Pollen Segmentation and Classification from SEM Images

Simulation Palynologists for Pollinosis Prevention: A Progressive Learning of Pollen Localization and Classification for Whole Slide Images

DeepPollenCount: a swin-transformer-YOLOv5-based deep learning method for pollen counting in various plant species

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