Precise automatic classification of 46 different pollen types with convolutional neural networks

Sevillano, Víctor; Holt, Katherine; Aznarte, José Luis

doi:10.1371/journal.pone.0229751

Cited by 67 publications

(69 citation statements)

References 29 publications

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“…Deep learning models have shown similar accuracy rates to ours on larger and more varied pollen datasets as well, but these either focussed on the family level 28 or on insect-collected pollen for honey analysis 29,30 . Increasing the taxonomic resolution of pollen grains has been achieved by incorporating an extensively trained deep learning model with super-resolution microscopy on a case study of fossil pollen 31 .…”

Section: Discussionsupporting

confidence: 52%

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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Section: Discussionsupporting

confidence: 52%

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

Polling¹,

Li²,

Cao³

et al. 2020

Preprint

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“…In a reasonable time frame of 9 min on average, fast measurements make it easy to rapidly establish a comprehensive image New Phytologist database for training of deep learning models. Comparable studies used 1060 (Daood et al, 2016), 13 617 images (Pedersen et al, 2017) or 19 500 images (Sevillano et al, 2020), while in our study the CNN classifier was trained with 426 876 images.…”

Section: Discussionmentioning

confidence: 99%

“…A recent review of 24 studies using automated pollen analysis showed that only seven of these studies could discriminate between more than 10 species and no study could identify more than 26 (Holt & Bennett, 2014). Since this review, several additional studies have emerged using a combination of microscopy and machine learning (Daood et al, 2016(Daood et al, , 2018Marcos et al, 2015;Oteros et al, 2015;Pedersen et al 2017;Sevillano & Aznarte, 2018;Sevillano et al, 2020), the best of which can identify 46 species with 98% accuracy (Sevillano et al, 2020). Most of these studies did not include congener species, which can be difficult to distinguish from each other.…”

Section: Introductionmentioning

confidence: 99%

“…Since this review, several additional studies have emerged using a combination of microscopy and machine learning (Daood et al ., 2016, 2018; Marcos et al ., 2015; Oteros et al ., 2015; Pedersen et al . 2017; Sevillano & Aznarte, 2018; Sevillano et al ., 2020), the best of which can identify 46 species with 98% accuracy (Sevillano et al ., 2020). Most of these studies did not include congener species, which can be difficult to distinguish from each other.…”

Section: Introductionmentioning

confidence: 99%

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Pollen analysis using multispectral imaging flow cytometry and deep learning

et al. 2020

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Pollen identification and quantification are crucial but challenging tasks in addressing a variety of evolutionary and ecological questions (pollination, paleobotany), but also for other fields of research (e.g. allergology, honey analysis or forensics). Researchers are exploring alternative methods to automate these tasks but, for several reasons, manual microscopy is still the gold standard. In this study, we present a new method for pollen analysis using multispectral imaging flow cytometry in combination with deep learning. We demonstrate that our method allows fast measurement while delivering high accuracy pollen identification. A dataset of 426 876 images depicting pollen from 35 plant species was used to train a convolutional neural network classifier. We found the best-performing classifier to yield a species-averaged accuracy of 96%. Even species that are difficult to differentiate using microscopy could be clearly separated. Our approach also allows a detailed determination of morphological pollen traits, such as size, symmetry or structure. Our phylogenetic analyses suggest phylogenetic conservatism in some of these traits. Given a comprehensive pollen reference database, we provide a powerful tool to be used in any pollen study with a need for rapid and accurate species identification, pollen grain quantification and trait extraction of recent pollen.

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“…Over the past few years, several studies using machinelearning algorithms have been conducted to monitoring of the airborne pollen or to develop automatic classification system of pollen grains [19], [20], [21]. There are also many studies that have utilized CNN models to classify various pollen species [22], [23], [24], [25], [26], [27]. However, our research is focused on establishing a reliable method for assessing pollen viability using germination images rather than identifying the pollen species covered in the palynology fields.…”

Section: Introductionmentioning

confidence: 99%

Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

Yang

et al. 2021

IEEE Access

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Verifying pollen germination using microscopic images is a difficult task. It is usually timeconsuming and may entail reduced accuracy and reproducibility. Therefore, in this study, we used random forest (RF) and convolutional neural network (CNN) models to perform image classification on raw data corresponding to pollens with different germination rates; the data were obtained via flow cytometry. A heat map, which was based on the RF analysis results, showed that the variables that significantly influenced the classification decision between NG and 60G categories were mainly located in the center and top-right regions of the 30 × 30 pixel image. Additionally, a variable importance plot showed that among the 900 input variables, pixel_316 was the variable that contributed the most toward prediction. Gradient-weighted class activation mapping was used to visualize the class activation maps of the CNN model. The bottom-left region of the activation map was activated in the NG image. However, the 60G image showed that not only the bottom-left region but also the top-right region was activated. Both the models classified the input images into NG and 60G categories with high accuracy. However, considering that the RF model does not reflect the characteristics of adjacent variables, the CNN model is more appropriate for classifying pollen germination images corresponding to pollen with various germination rates into distinct classes. Taken together, these results suggest that the CNN model can provide a reliable method for verifying the pollen performance.

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Precise automatic classification of 46 different pollen types with convolutional neural networks

Cited by 67 publications

References 29 publications

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

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

Pollen analysis using multispectral imaging flow cytometry and deep learning

Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models

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